Upload 32 files

.env

@@ -0,0 +1,35 @@
+# API 提供商 (openai SDK)
+API_PROVIDER=openai # Gemini 未适配，暂时只支持openai SDK，new-api解决方案
+
+# OpenAI API 配置
+#OPENAI_API_URL=https://***/v1
+#OPENAI_API_KEY=your_openai_api_key
+OPENAI_API_MODEL=comic-c
+NEWS_MODEL=gpt-4o
+#SERP_API_KEY=your_serp_api_key
+
+# Gemini API 配置
+# GEMINI_API_URL=https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent
+# GEMINI_API_KEY=your_gemini_api_key
+# GEMINI_API_MODEL=gemini-pro
+
+# 安全配置
+# API_KEY=your_api_key_for_protected_endpoints
+# HMAC_SECRET=your_hmac_secret_key_for_webhook_verification
+# ALLOWED_ORIGINS=http://localhost:8888,https://your-domain.com
+
+# Redis缓存设置(可选)
+# REDIS_URL=redis://redis:6379  #docker配置
+REDIS_URL=redis://localhost:6379
+USE_REDIS_CACHE=False
+
+# 数据库设置(可选)
+# DATABASE_URL=sqlite:///app/data/stock_analyzer.db  #docker配置
+DATABASE_URL=sqlite:///data/stock_analyzer.db
+USE_DATABASE=False
+
+# 日志配置
+LOG_LEVEL=INFO
+LOG_FILE=logs/stock_analyzer.log
+
+# API_KEY=UZXJfw3YNX80DLfN

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+static/favicon.ico filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -1,8 +1,32 @@
-FROM lanzhihong/stock-scanner:latest
-
-ENV TZ=Asia/Shanghai
-ENV API_TIMEOUT=60
-
-ENV OPENAI_API_MODEL=
-
-RUN mkdir /app/logs && chmod 777 /app/logs
+# 使用Python 3.11基础镜像（因为你的依赖包兼容性更好）
+FROM python:3.11-slim
+
+# 设置工作目录
+WORKDIR /app
+
+# 创建数据和日志目录
+RUN mkdir -p /app/data /app/logs
+
+# 设置环境变量
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1
+
+# 安装系统依赖
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+# 复制requirements.txt
+COPY requirements.txt .
+
+# 安装Python依赖
+RUN pip install --no-cache-dir -r requirements.txt
+
+# 复制应用代码
+COPY . .
+
+# 暴露端口（假设Flask应用运行在5000端口）
+EXPOSE 8888
+
+# 使用gunicorn启动应用
+CMD ["gunicorn", "--bind", "0.0.0.0:8888", "--workers", "4", "web_server:app"] 

auth_middleware.py

@@ -0,0 +1,79 @@
+from functools import wraps
+from flask import request, jsonify
+import os
+import time
+import hashlib
+import hmac
+
+
+def get_api_key():
+    return os.getenv('API_KEY', 'UZXJfw3YNX80DLfN')
+
+
+def require_api_key(f):
+    """需要API密钥验证的装饰器"""
+    @wraps(f)
+    def decorated_function(*args, **kwargs):
+        api_key = request.headers.get('X-API-Key')
+        if not api_key:
+            return jsonify({'error': '缺少API密钥'}), 401
+
+        if api_key != get_api_key():
+            return jsonify({'error': '无效的API密钥'}), 403
+
+        return f(*args, **kwargs)
+    return decorated_function
+
+
+def generate_hmac_signature(data, secret_key=None):
+    if secret_key is None:
+        secret_key = os.getenv('HMAC_SECRET', 'default_hmac_secret_for_development')
+
+    if isinstance(data, dict):
+        # 对字典进行排序，确保相同的数据产生相同的签名
+        data = '&'.join(f"{k}={v}" for k, v in sorted(data.items()))
+
+    # 使用HMAC-SHA256生成签名
+    signature = hmac.new(
+        secret_key.encode(),
+        data.encode(),
+        hashlib.sha256
+    ).hexdigest()
+
+    return signature
+
+
+def verify_hmac_signature(request_signature, data, secret_key=None):
+    expected_signature = generate_hmac_signature(data, secret_key)
+    return hmac.compare_digest(request_signature, expected_signature)
+
+
+def require_hmac_auth(f):
+    """需要HMAC认证的装饰器"""
+    @wraps(f)
+    def decorated_function(*args, **kwargs):
+        request_signature = request.headers.get('X-HMAC-Signature')
+        if not request_signature:
+            return jsonify({'error': '缺少HMAC签名'}), 401
+
+        # 获取请求数据
+        data = request.get_json(silent=True) or {}
+
+        # 添加时间戳防止重放攻击
+        timestamp = request.headers.get('X-Timestamp')
+        if not timestamp:
+            return jsonify({'error': '缺少时间戳'}), 401
+
+        # 验证时间戳有效性（有效期5分钟）
+        current_time = int(time.time())
+        if abs(current_time - int(timestamp)) > 300:
+            return jsonify({'error': '时间戳已过期'}), 401
+
+        # 将时间戳加入验证数据
+        verification_data = {**data, 'timestamp': timestamp}
+
+        # 验证签名
+        if not verify_hmac_signature(request_signature, verification_data):
+            return jsonify({'error': '签名无效'}), 403
+        return f(*args, **kwargs)
+    return decorated_function

capital_flow_analyzer.py

@@ -0,0 +1,588 @@
+# capital_flow_analyzer.py
+import logging
+import traceback
+import akshare as ak
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+
+
+class CapitalFlowAnalyzer:
+    def __init__(self):
+        self.data_cache = {}
+
+        # 设置日志记录
+        logging.basicConfig(level=logging.INFO,
+                            format='%(asctime)s - %(levelname)s - %(message)s')
+        self.logger = logging.getLogger(__name__)
+
+    def get_concept_fund_flow(self, period="10日排行"):
+        """获取概念/行业资金流向数据"""
+        try:
+            self.logger.info(f"Getting concept fund flow for period: {period}")
+
+            # 检查缓存
+            cache_key = f"concept_fund_flow_{period}"
+            if cache_key in self.data_cache:
+                cache_time, cached_data = self.data_cache[cache_key]
+                # 如果在最近一小时内有缓存数据，则返回缓存数据
+                if (datetime.now() - cache_time).total_seconds() < 3600:
+                    return cached_data
+
+            # 从akshare获取数据
+            concept_data = ak.stock_fund_flow_concept(symbol=period)
+
+            # 处理数据
+            result = []
+            for _, row in concept_data.iterrows():
+                try:
+                    # 列名可能有所不同，所以我们使用灵活的方法
+                    item = {
+                        "rank": int(row.get("序号", 0)),
+                        "sector": row.get("行业", ""),
+                        "company_count": int(row.get("公司家数", 0)),
+                        "sector_index": float(row.get("行业指数", 0)),
+                        "change_percent": self._parse_percent(row.get("阶段涨跌幅", "0%")),
+                        "inflow": float(row.get("流入资金", 0)),
+                        "outflow": float(row.get("流出资金", 0)),
+                        "net_flow": float(row.get("净额", 0))
+                    }
+                    result.append(item)
+                except Exception as e:
+                    # self.logger.warning(f"Error processing row in concept fund flow: {str(e)}")
+                    continue
+
+            # 缓存结果
+            self.data_cache[cache_key] = (datetime.now(), result)
+
+            return result
+        except Exception as e:
+            self.logger.error(f"Error getting concept fund flow: {str(e)}")
+            self.logger.error(traceback.format_exc())
+            # 如果API调用失败则返回模拟数据
+            return self._generate_mock_concept_fund_flow(period)
+
+    def get_individual_fund_flow_rank(self, period="10日"):
+        """获取个股资金流向排名"""
+        try:
+            self.logger.info(f"Getting individual fund flow ranking for period: {period}")
+
+            # 检查缓存
+            cache_key = f"individual_fund_flow_rank_{period}"
+            if cache_key in self.data_cache:
+                cache_time, cached_data = self.data_cache[cache_key]
+                # 如果在最近一小时内有缓存数据，则返回缓存数据
+                if (datetime.now() - cache_time).total_seconds() < 3600:
+                    return cached_data
+
+            # 从akshare获取数据
+            stock_data = ak.stock_individual_fund_flow_rank(indicator=period)
+
+            # 处理数据
+            result = []
+            for _, row in stock_data.iterrows():
+                try:
+                    # 根据不同时间段设置列名前缀
+                    period_prefix = "" if period == "今日" else f"{period}"
+
+                    item = {
+                        "rank": int(row.get("序号", 0)),
+                        "code": row.get("代码", ""),
+                        "name": row.get("名称", ""),
+                        "price": float(row.get("最新价", 0)),
+                        "change_percent": float(row.get(f"{period_prefix}涨跌幅", 0)),
+                        "main_net_inflow": float(row.get(f"{period_prefix}主力净流入-净额", 0)),
+                        "main_net_inflow_percent": float(row.get(f"{period_prefix}主力净流入-净占比", 0)),
+                        "super_large_net_inflow": float(row.get(f"{period_prefix}超大单净流入-净额", 0)),
+                        "super_large_net_inflow_percent": float(row.get(f"{period_prefix}超大单净流入-净占比", 0)),
+                        "large_net_inflow": float(row.get(f"{period_prefix}大单净流入-净额", 0)),
+                        "large_net_inflow_percent": float(row.get(f"{period_prefix}大单净流入-净占比", 0)),
+                        "medium_net_inflow": float(row.get(f"{period_prefix}中单净流入-净额", 0)),
+                        "medium_net_inflow_percent": float(row.get(f"{period_prefix}中单净流入-净占比", 0)),
+                        "small_net_inflow": float(row.get(f"{period_prefix}小单净流入-净额", 0)),
+                        "small_net_inflow_percent": float(row.get(f"{period_prefix}小单净流入-净占比", 0))
+                    }
+                    result.append(item)
+                except Exception as e:
+                    self.logger.warning(f"Error processing row in individual fund flow rank: {str(e)}")
+                    continue
+
+            # 缓存结果
+            self.data_cache[cache_key] = (datetime.now(), result)
+
+            return result
+        except Exception as e:
+            self.logger.error(f"Error getting individual fund flow ranking: {str(e)}")
+            self.logger.error(traceback.format_exc())
+            # 如果API调用失败则返回模拟数据
+            return self._generate_mock_individual_fund_flow_rank(period)
+
+    def get_individual_fund_flow(self, stock_code, market_type="", re_date="10日"):
+        """获取个股资金流向数据"""
+        try:
+            self.logger.info(f"Getting fund flow for stock: {stock_code}, market: {market_type}")
+
+            # 检查缓存
+            cache_key = f"individual_fund_flow_{stock_code}_{market_type}"
+            if cache_key in self.data_cache:
+                cache_time, cached_data = self.data_cache[cache_key]
+                # 如果在一小时内有缓存数据，则返回缓存数据
+                if (datetime.now() - cache_time).total_seconds() < 3600:
+                    return cached_data
+
+            # 如果未提供市场类型，则根据股票代码判断
+            if not market_type:
+                if stock_code.startswith('6'):
+                    market_type = "sh"
+                elif stock_code.startswith('0') or stock_code.startswith('3'):
+                    market_type = "sz"
+                else:
+                    market_type = "sh"  # Default to Shanghai
+
+            # 从akshare获取数据
+            flow_data = ak.stock_individual_fund_flow(stock=stock_code, market=market_type)
+
+            # 处理数据
+            result = {
+                "stock_code": stock_code,
+                "data": []
+            }
+
+            for _, row in flow_data.iterrows():
+                try:
+                    item = {
+                        "date": row.get("日期", ""),
+                        "price": float(row.get("收盘价", 0)),
+                        "change_percent": float(row.get("涨跌幅", 0)),
+                        "main_net_inflow": float(row.get("主力净流入-净额", 0)),
+                        "main_net_inflow_percent": float(row.get("主力净流入-净占比", 0)),
+                        "super_large_net_inflow": float(row.get("超大单净流入-净额", 0)),
+                        "super_large_net_inflow_percent": float(row.get("超大单净流入-净占比", 0)),
+                        "large_net_inflow": float(row.get("大单净流入-净额", 0)),
+                        "large_net_inflow_percent": float(row.get("大单净流入-净占比", 0)),
+                        "medium_net_inflow": float(row.get("中单净流入-净额", 0)),
+                        "medium_net_inflow_percent": float(row.get("中单净流入-净占比", 0)),
+                        "small_net_inflow": float(row.get("小单净流入-净额", 0)),
+                        "small_net_inflow_percent": float(row.get("小单净流入-净占比", 0))
+                    }
+                    result["data"].append(item)
+                except Exception as e:
+                    self.logger.warning(f"Error processing row in individual fund flow: {str(e)}")
+                    continue
+
+            # 计算汇总统计数据
+            if result["data"]:
+                # 最近数据 (最近10天)
+                recent_data = result["data"][:min(10, len(result["data"]))]
+
+                result["summary"] = {
+                    "recent_days": len(recent_data),
+                    "total_main_net_inflow": sum(item["main_net_inflow"] for item in recent_data),
+                    "avg_main_net_inflow_percent": np.mean(
+                        [item["main_net_inflow_percent"] for item in recent_data]),
+                    "positive_days": sum(1 for item in recent_data if item["main_net_inflow"] > 0),
+                    "negative_days": sum(1 for item in recent_data if item["main_net_inflow"] <= 0)
+                }
+
+            # Cache the result
+            self.data_cache[cache_key] = (datetime.now(), result)
+
+            return result
+        except Exception as e:
+            self.logger.error(f"Error getting individual fund flow: {str(e)}")
+            self.logger.error(traceback.format_exc())
+            # 如果API调用失败则返回模拟数据
+            return self._generate_mock_individual_fund_flow(stock_code, market_type)
+
+    def get_sector_stocks(self, sector):
+        """获取特定行业的股票"""
+        try:
+            self.logger.info(f"Getting stocks for sector: {sector}")
+
+            # 检查缓存
+            cache_key = f"sector_stocks_{sector}"
+            if cache_key in self.data_cache:
+                cache_time, cached_data = self.data_cache[cache_key]
+                # 如果在一小时内有缓存数据，则返回缓存数据
+                if (datetime.now() - cache_time).total_seconds() < 3600:
+                    return cached_data
+
+            # 尝试从akshare获取数据
+            try:
+                # For industry sectors (using 东方财富 interface)
+                stocks = ak.stock_board_industry_cons_em(symbol=sector)
+
+                # 提取股票列表
+                if not stocks.empty and '代码' in stocks.columns:
+                    result = []
+                    for _, row in stocks.iterrows():
+                        try:
+                            item = {
+                                "code": row.get("代码", ""),
+                                "name": row.get("名称", ""),
+                                "price": float(row.get("最新价", 0)),
+                                "change_percent": float(row.get("涨跌幅", 0)) if "涨跌幅" in row else 0,
+                                "main_net_inflow": 0,  # We'll get this data separately if needed
+                                "main_net_inflow_percent": 0  # We'll get this data separately if needed
+                            }
+                            result.append(item)
+                        except Exception as e:
+                            # self.logger.warning(f"Error processing row in sector stocks: {str(e)}")
+                            continue
+
+                    # 缓存结果
+                    self.data_cache[cache_key] = (datetime.now(), result)
+                    return result
+            except Exception as e:
+                self.logger.warning(f"Failed to get sector stocks from API: {str(e)}")
+                # 降级到模拟数据
+
+            # 如果到达这里，说明无法从API获取数据，返回模拟数据
+            result = self._generate_mock_sector_stocks(sector)
+            self.data_cache[cache_key] = (datetime.now(), result)
+            return result
+
+        except Exception as e:
+            self.logger.error(f"Error getting sector stocks: {str(e)}")
+            self.logger.error(traceback.format_exc())
+            # 如果API调用失败则返回模拟数据
+            return self._generate_mock_sector_stocks(sector)
+
+    def calculate_capital_flow_score(self, stock_code, market_type=""):
+        """计算股票资金流向评分"""
+        try:
+            self.logger.info(f"Calculating capital flow score for stock: {stock_code}")
+
+            # 获取个股资金流向数据
+            fund_flow = self.get_individual_fund_flow(stock_code, market_type)
+
+            if not fund_flow or not fund_flow.get("data") or not fund_flow.get("summary"):
+                return {
+                    "total": 0,
+                    "main_force": 0,
+                    "large_order": 0,
+                    "small_order": 0,
+                    "details": {}
+                }
+
+            # Extract summary statistics
+            summary = fund_flow["summary"]
+            recent_days = summary["recent_days"]
+            total_main_net_inflow = summary["total_main_net_inflow"]
+            avg_main_net_inflow_percent = summary["avg_main_net_inflow_percent"]
+            positive_days = summary["positive_days"]
+
+            # Calculate main force score (0-40)
+            main_force_score = 0
+
+            # 基于净流入百分比的评分
+            if avg_main_net_inflow_percent > 3:
+                main_force_score += 20
+            elif avg_main_net_inflow_percent > 1:
+                main_force_score += 15
+            elif avg_main_net_inflow_percent > 0:
+                main_force_score += 10
+
+            # 基于上涨天数的评分
+            positive_ratio = positive_days / recent_days if recent_days > 0 else 0
+            if positive_ratio > 0.7:
+                main_force_score += 20
+            elif positive_ratio > 0.5:
+                main_force_score += 15
+            elif positive_ratio > 0.3:
+                main_force_score += 10
+
+            # 计算大单评分（0-30分）
+            large_order_score = 0
+
+            # 分析超大单和大单交易
+            recent_super_large = [item["super_large_net_inflow"] for item in
+                                   fund_flow["data"][:recent_days]]
+            recent_large = [item["large_net_inflow"] for item in fund_flow["data"][:recent_days]]
+
+            super_large_positive = sum(1 for x in recent_super_large if x > 0)
+            large_positive = sum(1 for x in recent_large if x > 0)
+
+            # 基于超大单的评分
+            super_large_ratio = super_large_positive / recent_days if recent_days > 0 else 0
+            if super_large_ratio > 0.7:
+                large_order_score += 15
+            elif super_large_ratio > 0.5:
+                large_order_score += 10
+            elif super_large_ratio > 0.3:
+                large_order_score += 5
+
+            # 基于大单的评分
+            large_ratio = large_positive / recent_days if recent_days > 0 else 0
+            if large_ratio > 0.7:
+                large_order_score += 15
+            elif large_ratio > 0.5:
+                large_order_score += 10
+            elif large_ratio > 0.3:
+                large_order_score += 5
+
+            # 计算小单评分（0-30分）
+            small_order_score = 0
+
+            # 分析中单和小单交易
+            recent_medium = [item["medium_net_inflow"] for item in fund_flow["data"][:recent_days]]
+            recent_small = [item["small_net_inflow"] for item in fund_flow["data"][:recent_days]]
+
+            medium_positive = sum(1 for x in recent_medium if x > 0)
+            small_positive = sum(1 for x in recent_small if x > 0)
+
+            # 基于中单的评分
+            medium_ratio = medium_positive / recent_days if recent_days > 0 else 0
+            if medium_ratio > 0.7:
+                small_order_score += 15
+            elif medium_ratio > 0.5:
+                small_order_score += 10
+            elif medium_ratio > 0.3:
+                small_order_score += 5
+
+            # 基于小单的评分
+            small_ratio = small_positive / recent_days if recent_days > 0 else 0
+            if small_ratio > 0.7:
+                small_order_score += 15
+            elif small_ratio > 0.5:
+                small_order_score += 10
+            elif small_ratio > 0.3:
+                small_order_score += 5
+
+            # 计算总评分
+            total_score = main_force_score + large_order_score + small_order_score
+
+            return {
+                "total": total_score,
+                "main_force": main_force_score,
+                "large_order": large_order_score,
+                "small_order": small_order_score,
+                "details": fund_flow
+            }
+        except Exception as e:
+            self.logger.error(f"Error calculating capital flow score: {str(e)}")
+            self.logger.error(traceback.format_exc())
+            return {
+                "total": 0,
+                "main_force": 0,
+                "large_order": 0,
+                "small_order": 0,
+                "details": {},
+                "error": str(e)
+            }
+
+    def _parse_percent(self, percent_str):
+        """将百分比字符串转换为浮点数"""
+        try:
+            if isinstance(percent_str, str) and '%' in percent_str:
+                return float(percent_str.replace('%', ''))
+            return float(percent_str)
+        except (ValueError, TypeError):
+            return 0.0
+
+    def _generate_mock_concept_fund_flow(self, period):
+        """生成模拟概念资金流向数据"""
+        # self.logger.warning(f"Generating mock concept fund flow data for period: {period}")
+
+        sectors = [
+            "新能源", "医药", "半导体", "芯片", "人工智能", "大数据", "云计算", "5G",
+            "汽车", "消费", "金融", "互联网", "游戏", "农业", "化工", "建筑", "军工",
+            "钢铁", "有色金属", "煤炭", "石油"
+        ]
+
+        result = []
+        for i, sector in enumerate(sectors):
+            # 随机数据 - 前半部分为正，后半部分为负
+            is_positive = i < len(sectors) // 2
+
+            inflow = round(np.random.uniform(10, 50), 2) if is_positive else round(
+                np.random.uniform(5, 20), 2)
+            outflow = round(np.random.uniform(5, 20), 2) if is_positive else round(
+                np.random.uniform(10, 50), 2)
+            net_flow = round(inflow - outflow, 2)
+
+            change_percent = round(np.random.uniform(0, 5), 2) if is_positive else round(
+                np.random.uniform(-5, 0), 2)
+
+            item = {
+                "rank": i + 1,
+                "sector": sector,
+                "company_count": np.random.randint(10, 100),
+                "sector_index": round(np.random.uniform(1000, 5000), 2),
+                "change_percent": change_percent,
+                "inflow": inflow,
+                "outflow": outflow,
+                "net_flow": net_flow
+            }
+            result.append(item)
+
+        # 按净流入降序排序
+        return sorted(result, key=lambda x: x["net_flow"], reverse=True)
+
+    def _generate_mock_individual_fund_flow_rank(self, period):
+        """生成模拟个股资金流向排名数据"""
+        # self.logger.warning(f"Generating mock individual fund flow ranking data for period: {period}")
+
+        # Sample stock data
+        stocks = [
+            {"code": "600000", "name": "浦发银行"}, {"code": "600036", "name": "招商银行"},
+            {"code": "601318", "name": "中国平安"}, {"code": "600519", "name": "贵州茅台"},
+            {"code": "000858", "name": "五粮液"}, {"code": "000333", "name": "美的集团"},
+            {"code": "600276", "name": "恒瑞医药"}, {"code": "601888", "name": "中国中免"},
+            {"code": "600030", "name": "中信证券"}, {"code": "601166", "name": "兴业银行"},
+            {"code": "600887", "name": "伊利股份"}, {"code": "601398", "name": "工商银行"},
+            {"code": "600028", "name": "中国石化"}, {"code": "601988", "name": "中国银行"},
+            {"code": "601857", "name": "中国石油"}, {"code": "600019", "name": "宝钢股份"},
+            {"code": "600050", "name": "中国联通"}, {"code": "601328", "name": "交通银行"},
+            {"code": "601668", "name": "中国建筑"}, {"code": "601288", "name": "农业银行"}
+        ]
+
+        result = []
+        for i, stock in enumerate(stocks):
+            # 随机数据 - 前半部分为正，后半部分为负
+            is_positive = i < len(stocks) // 2
+
+            main_net_inflow = round(np.random.uniform(1e6, 5e7), 2) if is_positive else round(
+                np.random.uniform(-5e7, -1e6), 2)
+            main_net_inflow_percent = round(np.random.uniform(1, 10), 2) if is_positive else round(
+                np.random.uniform(-10, -1), 2)
+
+            super_large_net_inflow = round(main_net_inflow * np.random.uniform(0.3, 0.5), 2)
+            super_large_net_inflow_percent = round(main_net_inflow_percent * np.random.uniform(0.3, 0.5), 2)
+
+            large_net_inflow = round(main_net_inflow * np.random.uniform(0.3, 0.5), 2)
+            large_net_inflow_percent = round(main_net_inflow_percent * np.random.uniform(0.3, 0.5), 2)
+
+            medium_net_inflow = round(np.random.uniform(-1e6, 1e6), 2)
+            medium_net_inflow_percent = round(np.random.uniform(-2, 2), 2)
+
+            small_net_inflow = round(np.random.uniform(-1e6, 1e6), 2)
+            small_net_inflow_percent = round(np.random.uniform(-2, 2), 2)
+
+            change_percent = round(np.random.uniform(0, 5), 2) if is_positive else round(np.random.uniform(-5, 0), 2)
+
+            item = {
+                "rank": i + 1,
+                "code": stock["code"],
+                "name": stock["name"],
+                "price": round(np.random.uniform(10, 100), 2),
+                "change_percent": change_percent,
+                "main_net_inflow": main_net_inflow,
+                "main_net_inflow_percent": main_net_inflow_percent,
+                "super_large_net_inflow": super_large_net_inflow,
+                "super_large_net_inflow_percent": super_large_net_inflow_percent,
+                "large_net_inflow": large_net_inflow,
+                "large_net_inflow_percent": large_net_inflow_percent,
+                "medium_net_inflow": medium_net_inflow,
+                "medium_net_inflow_percent": medium_net_inflow_percent,
+                "small_net_inflow": small_net_inflow,
+                "small_net_inflow_percent": small_net_inflow_percent
+            }
+            result.append(item)
+
+        # 按主力净流入降序排序
+        return sorted(result, key=lambda x: x["main_net_inflow"], reverse=True)
+
+    def _generate_mock_individual_fund_flow(self, stock_code, market_type):
+        """生成模拟个股资金流向数据"""
+        # self.logger.warning(f"Generating mock individual fund flow data for stock: {stock_code}")
+
+        # 生成30天的模拟数据
+        end_date = datetime.now()
+
+        result = {
+            "stock_code": stock_code,
+            "data": []
+        }
+
+        # 创建模拟价格趋势（使用合理的随机游走）
+        base_price = np.random.uniform(10, 100)
+        current_price = base_price
+
+        for i in range(30):
+            date = (end_date - timedelta(days=i)).strftime('%Y-%m-%d')
+
+            # 随机价格变化（-2%到+2%）
+            change_percent = np.random.uniform(-2, 2)
+            price = round(current_price * (1 + change_percent / 100), 2)
+            current_price = price
+
+            # 随机资金流向数据，与价格变化有一定相关性
+            is_positive = change_percent > 0
+
+            main_net_inflow = round(np.random.uniform(1e5, 5e6), 2) if is_positive else round(
+                np.random.uniform(-5e6, -1e5), 2)
+            main_net_inflow_percent = round(np.random.uniform(1, 5), 2) if is_positive else round(
+                np.random.uniform(-5, -1), 2)
+
+            super_large_net_inflow = round(main_net_inflow * np.random.uniform(0.3, 0.5), 2)
+            super_large_net_inflow_percent = round(main_net_inflow_percent * np.random.uniform(0.3, 0.5), 2)
+
+            large_net_inflow = round(main_net_inflow * np.random.uniform(0.3, 0.5), 2)
+            large_net_inflow_percent = round(main_net_inflow_percent * np.random.uniform(0.3, 0.5), 2)
+
+            medium_net_inflow = round(np.random.uniform(-1e5, 1e5), 2)
+            medium_net_inflow_percent = round(np.random.uniform(-2, 2), 2)
+
+            small_net_inflow = round(np.random.uniform(-1e5, 1e5), 2)
+            small_net_inflow_percent = round(np.random.uniform(-2, 2), 2)
+
+            item = {
+                "date": date,
+                "price": price,
+                "change_percent": round(change_percent, 2),
+                "main_net_inflow": main_net_inflow,
+                "main_net_inflow_percent": main_net_inflow_percent,
+                "super_large_net_inflow": super_large_net_inflow,
+                "super_large_net_inflow_percent": super_large_net_inflow_percent,
+                "large_net_inflow": large_net_inflow,
+                "large_net_inflow_percent": large_net_inflow_percent,
+                "medium_net_inflow": medium_net_inflow,
+                "medium_net_inflow_percent": medium_net_inflow_percent,
+                "small_net_inflow": small_net_inflow,
+                "small_net_inflow_percent": small_net_inflow_percent
+            }
+            result["data"].append(item)
+
+        # 按日期降序排序（最新的在前）
+        result["data"].sort(key=lambda x: x["date"], reverse=True)
+
+        # 计算汇总统计数据
+        recent_data = result["data"][:10]
+
+        result["summary"] = {
+            "recent_days": len(recent_data),
+            "total_main_net_inflow": sum(item["main_net_inflow"] for item in recent_data),
+            "avg_main_net_inflow_percent": np.mean([item["main_net_inflow_percent"] for item in recent_data]),
+            "positive_days": sum(1 for item in recent_data if item["main_net_inflow"] > 0),
+            "negative_days": sum(1 for item in recent_data if item["main_net_inflow"] <= 0)
+        }
+
+        return result
+
+    def _generate_mock_sector_stocks(self, sector):
+        """生成模拟行业股票数据"""
+        # self.logger.warning(f"Generating mock sector stocks for: {sector}")
+
+        # 要生成的股票数量
+        num_stocks = np.random.randint(20, 50)
+
+        result = []
+        for i in range(num_stocks):
+            prefix = "6" if np.random.random() > 0.5 else "0"
+            stock_code = prefix + str(100000 + i).zfill(5)[-5:]
+
+            change_percent = round(np.random.uniform(-5, 5), 2)
+
+            item = {
+                "code": stock_code,
+                "name": f"{sector}股票{i + 1}",
+                "price": round(np.random.uniform(10, 100), 2),
+                "change_percent": change_percent,
+                "main_net_inflow": round(np.random.uniform(-1e6, 1e6), 2),
+                "main_net_inflow_percent": round(np.random.uniform(-5, 5), 2)
+            }
+            result.append(item)
+
+        # 按主力净流入降序排序
+        return sorted(result, key=lambda x: x["main_net_inflow"], reverse=True)

database.py

@@ -0,0 +1,102 @@
+import os
+from sqlalchemy import create_engine, Column, Integer, String, Float, DateTime, Text, JSON
+from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy.orm import sessionmaker
+from datetime import datetime
+
+# 读取配置
+DATABASE_URL = os.getenv('DATABASE_URL', 'sqlite:///data/stock_analyzer.db')
+USE_DATABASE = os.getenv('USE_DATABASE', 'False').lower() == 'true'
+
+# 创建引擎
+engine = create_engine(DATABASE_URL)
+Base = declarative_base()
+
+
+# 定义模型
+class StockInfo(Base):
+    __tablename__ = 'stock_info'
+
+    id = Column(Integer, primary_key=True)
+    stock_code = Column(String(10), nullable=False, index=True)
+    stock_name = Column(String(50))
+    market_type = Column(String(5))
+    industry = Column(String(50))
+    updated_at = Column(DateTime, default=datetime.now, onupdate=datetime.now)
+
+    def to_dict(self):
+        return {
+            'stock_code': self.stock_code,
+            'stock_name': self.stock_name,
+            'market_type': self.market_type,
+            'industry': self.industry,
+            'updated_at': self.updated_at.strftime('%Y-%m-%d %H:%M:%S') if self.updated_at else None
+        }
+
+
+class AnalysisResult(Base):
+    __tablename__ = 'analysis_results'
+
+    id = Column(Integer, primary_key=True)
+    stock_code = Column(String(10), nullable=False, index=True)
+    market_type = Column(String(5))
+    analysis_date = Column(DateTime, default=datetime.now)
+    score = Column(Float)
+    recommendation = Column(String(100))
+    technical_data = Column(JSON)
+    fundamental_data = Column(JSON)
+    capital_flow_data = Column(JSON)
+    ai_analysis = Column(Text)
+
+    def to_dict(self):
+        return {
+            'stock_code': self.stock_code,
+            'market_type': self.market_type,
+            'analysis_date': self.analysis_date.strftime('%Y-%m-%d %H:%M:%S') if self.analysis_date else None,
+            'score': self.score,
+            'recommendation': self.recommendation,
+            'technical_data': self.technical_data,
+            'fundamental_data': self.fundamental_data,
+            'capital_flow_data': self.capital_flow_data,
+            'ai_analysis': self.ai_analysis
+        }
+
+
+class Portfolio(Base):
+    __tablename__ = 'portfolios'
+
+    id = Column(Integer, primary_key=True)
+    user_id = Column(String(50), nullable=False, index=True)
+    name = Column(String(100))
+    created_at = Column(DateTime, default=datetime.now)
+    updated_at = Column(DateTime, default=datetime.now, onupdate=datetime.now)
+    stocks = Column(JSON)  # 存储股票列表的JSON
+
+    def to_dict(self):
+        return {
+            'id': self.id,
+            'user_id': self.user_id,
+            'name': self.name,
+            'created_at': self.created_at.strftime('%Y-%m-%d %H:%M:%S') if self.created_at else None,
+            'updated_at': self.updated_at.strftime('%Y-%m-%d %H:%M:%S') if self.updated_at else None,
+            'stocks': self.stocks
+        }
+
+
+# 创建会话工厂
+Session = sessionmaker(bind=engine)
+
+
+# 初始化数据库
+def init_db():
+    Base.metadata.create_all(engine)
+
+
+# 获取数据库会话
+def get_session():
+    return Session()
+
+
+# 如果启用数据库，则初始化
+if USE_DATABASE:
+    init_db()

fundamental_analyzer.py

@@ -0,0 +1,157 @@
+# -*- coding: utf-8 -*-
+"""
+智能分析系统（股票） - 股票市场数据分析系统
+开发者：熊猫大侠
+版本：v2.1.0
+许可证：MIT License
+"""
+# fundamental_analyzer.py
+import akshare as ak
+import pandas as pd
+import numpy as np
+
+
+class FundamentalAnalyzer:
+    def __init__(self):
+        """初始化基础分析类"""
+        self.data_cache = {}
+
+    def get_financial_indicators(self, stock_code):
+        """获取财务指标数据"""
+        try:
+            # 获取基本财务指标
+            financial_data = ak.stock_financial_analysis_indicator(symbol=stock_code,start_year="2022")
+
+            # 获取最新估值指标
+            valuation = ak.stock_value_em(symbol=stock_code)
+
+            # 整合数据
+            indicators = {
+                'pe_ttm': float(valuation['PE(TTM)'].iloc[0]),
+                'pb': float(valuation['市净率'].iloc[0]),
+                'ps_ttm': float(valuation['市销率'].iloc[0]),
+                'roe': float(financial_data['加权净资产收益率(%)'].iloc[0]),
+                'gross_margin': float(financial_data['销售毛利率(%)'].iloc[0]),
+                'net_profit_margin': float(financial_data['总资产净利润率(%)'].iloc[0]),
+                'debt_ratio': float(financial_data['资产负债率(%)'].iloc[0])
+            }
+
+            return indicators
+        except Exception as e:
+            print(f"获取财务指标出错: {str(e)}")
+            return {}
+
+    def get_growth_data(self, stock_code):
+        """获取成长性数据"""
+        try:
+            # 获取历年财务数据
+            financial_data = ak.stock_financial_abstract(symbol=stock_code)
+
+            # 计算各项成长率
+            revenue = financial_data['营业收入'].astype(float)
+            net_profit = financial_data['净利润'].astype(float)
+
+            growth = {
+                'revenue_growth_3y': self._calculate_cagr(revenue, 3),
+                'profit_growth_3y': self._calculate_cagr(net_profit, 3),
+                'revenue_growth_5y': self._calculate_cagr(revenue, 5),
+                'profit_growth_5y': self._calculate_cagr(net_profit, 5)
+            }
+
+            return growth
+        except Exception as e:
+            print(f"获取成长数据出错: {str(e)}")
+            return {}
+
+    def _calculate_cagr(self, series, years):
+        """计算复合年增长率"""
+        if len(series) < years:
+            return None
+
+        latest = series.iloc[0]
+        earlier = series.iloc[min(years, len(series) - 1)]
+
+        if earlier <= 0:
+            return None
+
+        return ((latest / earlier) ** (1 / years) - 1) * 100
+
+    def calculate_fundamental_score(self, stock_code):
+        """计算基本面综合评分"""
+        indicators = self.get_financial_indicators(stock_code)
+        growth = self.get_growth_data(stock_code)
+
+        # 估值评分 (30分)
+        valuation_score = 0
+        if 'pe_ttm' in indicators and indicators['pe_ttm'] > 0:
+            pe = indicators['pe_ttm']
+            if pe < 15:
+                valuation_score += 25
+            elif pe < 25:
+                valuation_score += 20
+            elif pe < 35:
+                valuation_score += 15
+            elif pe < 50:
+                valuation_score += 10
+            else:
+                valuation_score += 5
+
+        # 财务健康评分 (40分)
+        financial_score = 0
+        if 'roe' in indicators:
+            roe = indicators['roe']
+            if roe > 20:
+                financial_score += 15
+            elif roe > 15:
+                financial_score += 12
+            elif roe > 10:
+                financial_score += 8
+            elif roe > 5:
+                financial_score += 4
+
+        if 'debt_ratio' in indicators:
+            debt_ratio = indicators['debt_ratio']
+            if debt_ratio < 30:
+                financial_score += 15
+            elif debt_ratio < 50:
+                financial_score += 10
+            elif debt_ratio < 70:
+                financial_score += 5
+
+        # 成长性评分 (30分)
+        growth_score = 0
+        if 'revenue_growth_3y' in growth and growth['revenue_growth_3y']:
+            rev_growth = growth['revenue_growth_3y']
+            if rev_growth > 30:
+                growth_score += 15
+            elif rev_growth > 20:
+                growth_score += 12
+            elif rev_growth > 10:
+                growth_score += 8
+            elif rev_growth > 0:
+                growth_score += 4
+
+        if 'profit_growth_3y' in growth and growth['profit_growth_3y']:
+            profit_growth = growth['profit_growth_3y']
+            if profit_growth > 30:
+                growth_score += 15
+            elif profit_growth > 20:
+                growth_score += 12
+            elif profit_growth > 10:
+                growth_score += 8
+            elif profit_growth > 0:
+                growth_score += 4
+
+        # 计算总分
+        total_score = valuation_score + financial_score + growth_score
+
+        return {
+            'total': total_score,
+            'valuation': valuation_score,
+            'financial_health': financial_score,
+            'growth': growth_score,
+            'details': {
+                'indicators': indicators,
+                'growth': growth
+            }
+        }

index_industry_analyzer.py

@@ -0,0 +1,275 @@
+# index_industry_analyzer.py
+import akshare as ak
+import pandas as pd
+import numpy as np
+import threading
+
+
+class IndexIndustryAnalyzer:
+    def __init__(self, analyzer):
+        self.analyzer = analyzer
+        self.data_cache = {}
+
+    def analyze_index(self, index_code, limit=30):
+        """分析指数整体情况"""
+        try:
+            cache_key = f"index_{index_code}"
+            if cache_key in self.data_cache:
+                cache_time, cached_result = self.data_cache[cache_key]
+                # 如果缓存时间在1小时内，直接返回
+                if (pd.Timestamp.now() - cache_time).total_seconds() < 3600:
+                    return cached_result
+
+            # 获取指数成分股
+            if index_code == '000300':
+                # 沪深300成分股
+                stocks = ak.index_stock_cons_weight_csindex(symbol="000300")
+                index_name = "沪深300"
+            elif index_code == '000905':
+                # 中证500成分股
+                stocks = ak.index_stock_cons_weight_csindex(symbol="000905")
+                index_name = "中证500"
+            elif index_code == '000852':
+                # 中证1000成分股
+                stocks = ak.index_stock_cons_weight_csindex(symbol="000852")
+                index_name = "中证1000"
+            elif index_code == '000001':
+                # 上证指数
+                stocks = ak.index_stock_cons_weight_csindex(symbol="000001")
+                index_name = "上证指数"
+            else:
+                return {"error": "不支持的指数代码"}
+
+            # 提取股票代码列表和权重
+            stock_list = []
+            if '成分券代码' in stocks.columns:
+                stock_list = stocks['成分券代码'].tolist()
+                weights = stocks['权重(%)'].tolist() if '权重(%)' in stocks.columns else [1] * len(stock_list)
+            else:
+                return {"error": "获取指数成分股失败"}
+
+            # 限制分析的股票数量以提高性能
+            if limit and len(stock_list) > limit:
+                # 按权重排序，取前limit只权重最大的股票
+                stock_weights = list(zip(stock_list, weights))
+                stock_weights.sort(key=lambda x: x[1], reverse=True)
+                stock_list = [s[0] for s in stock_weights[:limit]]
+                weights = [s[1] for s in stock_weights[:limit]]
+
+            # 多线程分析股票
+            results = []
+            threads = []
+            results_lock = threading.Lock()
+
+            def analyze_stock(stock_code, weight):
+                try:
+                    # 分析股票
+                    result = self.analyzer.quick_analyze_stock(stock_code)
+                    result['weight'] = weight
+
+                    with results_lock:
+                        results.append(result)
+                except Exception as e:
+                    print(f"分析股票 {stock_code} 时出错: {str(e)}")
+
+            # 创建并启动线程
+            for i, stock_code in enumerate(stock_list):
+                weight = weights[i] if i < len(weights) else 1
+                thread = threading.Thread(target=analyze_stock, args=(stock_code, weight))
+                threads.append(thread)
+                thread.start()
+
+            # 等待所有线程完成
+            for thread in threads:
+                thread.join()
+
+            # 计算指数整体情况
+            total_weight = sum([r.get('weight', 1) for r in results])
+
+            # 计算加权评分
+            index_score = 0
+            if total_weight > 0:
+                index_score = sum([r.get('score', 0) * r.get('weight', 1) for r in results]) / total_weight
+
+            # 计算其他指标
+            up_count = sum(1 for r in results if r.get('price_change', 0) > 0)
+            down_count = sum(1 for r in results if r.get('price_change', 0) < 0)
+            flat_count = len(results) - up_count - down_count
+
+            # 计算涨跌股比例
+            up_ratio = up_count / len(results) if len(results) > 0 else 0
+
+            # 计算加权平均涨跌幅
+            weighted_change = 0
+            if total_weight > 0:
+                weighted_change = sum([r.get('price_change', 0) * r.get('weight', 1) for r in results]) / total_weight
+
+            # 按评分对股票排序
+            results.sort(key=lambda x: x.get('score', 0), reverse=True)
+
+            # 整理结果
+            index_analysis = {
+                "index_code": index_code,
+                "index_name": index_name,
+                "score": round(index_score, 2),
+                "stock_count": len(results),
+                "up_count": up_count,
+                "down_count": down_count,
+                "flat_count": flat_count,
+                "up_ratio": up_ratio,
+                "weighted_change": weighted_change,
+                "top_stocks": results[:5] if len(results) >= 5 else results,
+                "results": results
+            }
+
+            # 缓存结果
+            self.data_cache[cache_key] = (pd.Timestamp.now(), index_analysis)
+
+            return index_analysis
+
+        except Exception as e:
+            print(f"分析指数整体情况时出错: {str(e)}")
+            return {"error": f"分析指数时出错: {str(e)}"}
+
+    def analyze_industry(self, industry, limit=30):
+        """分析行业整体情况"""
+        try:
+            cache_key = f"industry_{industry}"
+            if cache_key in self.data_cache:
+                cache_time, cached_result = self.data_cache[cache_key]
+                # 如果缓存时间在1小时内，直接返回
+                if (pd.Timestamp.now() - cache_time).total_seconds() < 3600:
+                    return cached_result
+
+            # 获取行业成分股
+            stocks = ak.stock_board_industry_cons_em(symbol=industry)
+
+            # 提取股票代码列表
+            stock_list = stocks['代码'].tolist() if '代码' in stocks.columns else []
+
+            if not stock_list:
+                return {"error": "获取行业成分股失败"}
+
+            # 限制分析的股票数量以提高性能
+            if limit and len(stock_list) > limit:
+                stock_list = stock_list[:limit]
+
+            # 多线程分析股票
+            results = []
+            threads = []
+            results_lock = threading.Lock()
+
+            def analyze_stock(stock_code):
+                try:
+                    # 分析股票
+                    result = self.analyzer.quick_analyze_stock(stock_code)
+
+                    with results_lock:
+                        results.append(result)
+                except Exception as e:
+                    print(f"分析股票 {stock_code} 时出错: {str(e)}")
+
+            # 创建并启动线程
+            for stock_code in stock_list:
+                thread = threading.Thread(target=analyze_stock, args=(stock_code,))
+                threads.append(thread)
+                thread.start()
+
+            # 等待所有线程完成
+            for thread in threads:
+                thread.join()
+
+            # 计算行业整体情况
+            if not results:
+                return {"error": "分析行业股票失败"}
+
+            # 计算平均评分
+            industry_score = sum([r.get('score', 0) for r in results]) / len(results)
+
+            # 计算其他指标
+            up_count = sum(1 for r in results if r.get('price_change', 0) > 0)
+            down_count = sum(1 for r in results if r.get('price_change', 0) < 0)
+            flat_count = len(results) - up_count - down_count
+
+            # 计算涨跌股比例
+            up_ratio = up_count / len(results)
+
+            # 计算平均涨跌幅
+            avg_change = sum([r.get('price_change', 0) for r in results]) / len(results)
+
+            # 按评分对股票排序
+            results.sort(key=lambda x: x.get('score', 0), reverse=True)
+
+            # 整理结果
+            industry_analysis = {
+                "industry": industry,
+                "score": round(industry_score, 2),
+                "stock_count": len(results),
+                "up_count": up_count,
+                "down_count": down_count,
+                "flat_count": flat_count,
+                "up_ratio": up_ratio,
+                "avg_change": avg_change,
+                "top_stocks": results[:5] if len(results) >= 5 else results,
+                "results": results
+            }
+
+            # 缓存结果
+            self.data_cache[cache_key] = (pd.Timestamp.now(), industry_analysis)
+
+            return industry_analysis
+
+        except Exception as e:
+            print(f"分析行业整体情况时出错: {str(e)}")
+            return {"error": f"分析行业时出错: {str(e)}"}
+
+    def compare_industries(self, limit=10):
+        """比较不同行业的表现"""
+        try:
+            # 获取行业板块数据
+            industry_data = ak.stock_board_industry_name_em()
+
+            # 提取行业名称列表
+            industries = industry_data['板块名称'].tolist() if '板块名称' in industry_data.columns else []
+
+            if not industries:
+                return {"error": "获取行业列表失败"}
+
+            # 限制分析的行业数量
+            industries = industries[:limit] if limit else industries
+
+            # 分析各行业情况
+            industry_results = []
+
+            for industry in industries:
+                try:
+                    # 简化分析，只获取基本指标
+                    industry_info = ak.stock_board_industry_hist_em(symbol=industry, period="3m")
+
+                    # 计算行业涨跌幅
+                    if not industry_info.empty:
+                        latest = industry_info.iloc[0]
+                        change = latest['涨跌幅'] if '涨跌幅' in latest.index else 0
+
+                        industry_results.append({
+                            "industry": industry,
+                            "change": change,
+                            "volume": latest['成交量'] if '成交量' in latest.index else 0,
+                            "turnover": latest['成交额'] if '成交额' in latest.index else 0
+                        })
+                except Exception as e:
+                    print(f"分析行业 {industry} 时出错: {str(e)}")
+
+            # 按涨跌幅排序
+            industry_results.sort(key=lambda x: x.get('change', 0), reverse=True)
+
+            return {
+                "count": len(industry_results),
+                "top_industries": industry_results[:5] if len(industry_results) >= 5 else industry_results,
+                "bottom_industries": industry_results[-5:] if len(industry_results) >= 5 else [],
+                "results": industry_results
+            }
+
+        except Exception as e:
+            print(f"比较行业表现时出错: {str(e)}")
+            return {"error": f"比较行业表现时出错: {str(e)}"}

industry_analyzer.py

@@ -0,0 +1,533 @@
+# -*- coding: utf-8 -*-
+"""
+智能分析系统（股票） - 股票市场数据分析系统
+开发者：熊猫大侠
+版本：v2.1.0
+许可证：MIT License
+"""
+# industry_analyzer.py
+import logging
+import random
+import akshare as ak
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+
+
+class IndustryAnalyzer:
+    def __init__(self):
+        """初始化行业分析类"""
+        self.data_cache = {}
+        self.industry_code_map = {}  # 缓存行业名称到代码的映射
+
+        # 设置日志记录
+        logging.basicConfig(level=logging.INFO,
+                            format='%(asctime)s - %(levelname)s - %(message)s')
+        self.logger = logging.getLogger(__name__)
+
+    def get_industry_fund_flow(self, symbol="即时"):
+        """获取行业资金流向数据"""
+        try:
+            # 缓存键
+            cache_key = f"industry_fund_flow_{symbol}"
+
+            # 检查缓存
+            if cache_key in self.data_cache:
+                cache_time, cached_data = self.data_cache[cache_key]
+                # 如果缓存时间在30分钟内，直接返回
+                if (datetime.now() - cache_time).total_seconds() < 1800:
+                    self.logger.info(f"从缓存获取行业资金流向数据: {symbol}")
+                    return cached_data
+
+            # 获取行业资金流向数据
+            self.logger.info(f"从API获取行业资金流向数据: {symbol}")
+            fund_flow_data = ak.stock_fund_flow_industry(symbol=symbol)
+
+            # 打印列名以便调试
+            self.logger.info(f"行业资金流向数据列名: {fund_flow_data.columns.tolist()}")
+
+            # 转换为字典列表
+            result = []
+
+            if symbol == "即时":
+                for _, row in fund_flow_data.iterrows():
+                    try:
+                        # 安全地将值转换为对应的类型
+                        item = {
+                            "rank": self._safe_int(row["序号"]),
+                            "industry": str(row["行业"]),
+                            "index": self._safe_float(row["行业指数"]),
+                            "change": self._safe_percent(row["行业-涨跌幅"]),
+                            "inflow": self._safe_float(row["流入资金"]),
+                            "outflow": self._safe_float(row["流出资金"]),
+                            "netFlow": self._safe_float(row["净额"]),
+                            "companyCount": self._safe_int(row["公司家数"])
+                        }
+
+                        # 添加领涨股相关数据，如果存在
+                        if "领涨股" in row:
+                            item["leadingStock"] = str(row["领涨股"])
+                        if "领涨股-涨跌幅" in row:
+                            item["leadingStockChange"] = self._safe_percent(row["领涨股-涨跌幅"])
+                        if "当前价" in row:
+                            item["leadingStockPrice"] = self._safe_float(row["当前价"])
+
+                        result.append(item)
+                    except Exception as e:
+                        self.logger.warning(f"处理行业资金流向数据行时出错: {str(e)}")
+                        continue
+            else:
+                for _, row in fund_flow_data.iterrows():
+                    try:
+                        item = {
+                            "rank": self._safe_int(row["序号"]),
+                            "industry": str(row["行业"]),
+                            "companyCount": self._safe_int(row["公司家数"]),
+                            "index": self._safe_float(row["行业指数"]),
+                            "change": self._safe_percent(row["阶段涨跌幅"]),
+                            "inflow": self._safe_float(row["流入资金"]),
+                            "outflow": self._safe_float(row["流出资金"]),
+                            "netFlow": self._safe_float(row["净额"])
+                        }
+                        result.append(item)
+                    except Exception as e:
+                        self.logger.warning(f"处理行业资金流向数据行时出错: {str(e)}")
+                        continue
+
+            # 缓存结果
+            self.data_cache[cache_key] = (datetime.now(), result)
+
+            return result
+
+        except Exception as e:
+            self.logger.error(f"获取行业资金流向数据失败: {str(e)}")
+            # 返回更详细的错误信息，包括堆栈跟踪
+            import traceback
+            self.logger.error(traceback.format_exc())
+            return []
+
+    def _safe_float(self, value):
+        """安全地将值转换为浮点数"""
+        try:
+            if pd.isna(value):
+                return 0.0
+            return float(value)
+        except:
+            return 0.0
+
+    def _safe_int(self, value):
+        """安全地将值转换为整数"""
+        try:
+            if pd.isna(value):
+                return 0
+            return int(value)
+        except:
+            return 0
+
+    def _safe_percent(self, value):
+        """安全地将百分比值转换为字符串格式"""
+        try:
+            if pd.isna(value):
+                return "0.00"
+
+            # 如果是字符串并包含%，移除%符号
+            if isinstance(value, str) and "%" in value:
+                return value.replace("%", "")
+
+            # 如果是数值，直接转换成字符串
+            return str(float(value))
+        except:
+            return "0.00"
+
+    def _get_industry_code(self, industry_name):
+        """获取行业名称对应的板块代码"""
+        try:
+            # 如果已经缓存了行业代码映射，直接使用
+            if not self.industry_code_map:
+                # 获取东方财富行业板块名称及代码
+                industry_list = ak.stock_board_industry_name_em()
+
+                # 创建行业名称到代码的映射
+                for _, row in industry_list.iterrows():
+                    if '板块名称' in industry_list.columns and '板块代码' in industry_list.columns:
+                        name = row['板块名称']
+                        code = row['板块代码']
+                        self.industry_code_map[name] = code
+
+                self.logger.info(f"成功获取到 {len(self.industry_code_map)} 个行业代码映射")
+
+            # 尝试精确匹配
+            if industry_name in self.industry_code_map:
+                return self.industry_code_map[industry_name]
+
+            # 尝试模糊匹配
+            for name, code in self.industry_code_map.items():
+                if industry_name in name or name in industry_name:
+                    self.logger.info(f"行业名称 '{industry_name}' 模糊匹配到 '{name}'，代码: {code}")
+                    return code
+
+            # 如果找不到匹配项，则返回None
+            self.logger.warning(f"未找到行业 '{industry_name}' 对应的代码")
+            return None
+
+        except Exception as e:
+            self.logger.error(f"获取行业代码时出错: {str(e)}")
+            import traceback
+            self.logger.error(traceback.format_exc())
+            return None
+
+    def get_industry_stocks(self, industry):
+        """获取行业成分股"""
+        try:
+            # 缓存键
+            cache_key = f"industry_stocks_{industry}"
+
+            # 检查缓存
+            if cache_key in self.data_cache:
+                cache_time, cached_data = self.data_cache[cache_key]
+                # 如果缓存时间在1小时内，直接返回
+                if (datetime.now() - cache_time).total_seconds() < 3600:
+                    self.logger.info(f"从缓存获取行业成分股: {industry}")
+                    return cached_data
+
+            # 获取行业成分股
+            self.logger.info(f"获取 {industry} 行业成分股")
+
+            result = []
+            try:
+                # 1. 首先尝试直接使用行业名称
+                try:
+                    stocks = ak.stock_board_industry_cons_em(symbol=industry)
+                    self.logger.info(f"使用行业名称 '{industry}' 成功获取成分股")
+                except Exception as direct_error:
+                    self.logger.warning(f"使用行业名称获取成分股失败: {str(direct_error)}")
+                    # 2. 尝试使用行业代码
+                    industry_code = self._get_industry_code(industry)
+                    if industry_code:
+                        self.logger.info(f"尝试使用行业代码 {industry_code} 获取成分股")
+                        stocks = ak.stock_board_industry_cons_em(symbol=industry_code)
+                    else:
+                        # 如果无法获取行业代码，抛出异常，进入模拟数据生成
+                        raise ValueError(f"无法找到行业 '{industry}' 对应的代码")
+
+                # 打印列名以便调试
+                self.logger.info(f"行业成分股数据列名: {stocks.columns.tolist()}")
+
+                # 转换为字典列表
+                if not stocks.empty:
+                    for _, row in stocks.iterrows():
+                        try:
+                            item = {
+                                "code": str(row["代码"]),
+                                "name": str(row["名称"]),
+                                "price": self._safe_float(row["最新价"]),
+                                "change": self._safe_float(row["涨跌幅"]),
+                                "change_amount": self._safe_float(row["涨跌额"]) if "涨跌额" in row else 0.0,
+                                "volume": self._safe_float(row["成交量"]) if "成交量" in row else 0.0,
+                                "turnover": self._safe_float(row["成交额"]) if "成交额" in row else 0.0,
+                                "amplitude": self._safe_float(row["振幅"]) if "振幅" in row else 0.0,
+                                "turnover_rate": self._safe_float(row["换手率"]) if "换手率" in row else 0.0
+                            }
+                            result.append(item)
+                        except Exception as e:
+                            self.logger.warning(f"处理行业成分股数据行时出错: {str(e)}")
+                            continue
+
+            except Exception as e:
+                # 3. 如果上述方法都失败，生成模拟数据
+                self.logger.warning(f"无法通过API获取行业成分股，使用模拟数据: {str(e)}")
+                result = self._generate_mock_industry_stocks(industry)
+
+            # 缓存结果
+            self.data_cache[cache_key] = (datetime.now(), result)
+
+            return result
+
+        except Exception as e:
+            self.logger.error(f"获取行业成分股失败: {str(e)}")
+            import traceback
+            self.logger.error(traceback.format_exc())
+            return []
+
+    def _generate_mock_industry_stocks(self, industry):
+        """生成模拟的行业成分股数据"""
+        self.logger.info(f"生成行业 {industry} 的模拟成分股数据")
+
+        # 使用来自资金流向的行业数据获取该行业的基本信息
+        fund_flow_data = self.get_industry_fund_flow("即时")
+        industry_data = next((item for item in fund_flow_data if item["industry"] == industry), None)
+
+        company_count = 20  # 默认值
+        if industry_data and "companyCount" in industry_data:
+            company_count = min(industry_data["companyCount"], 30)  # 限制最多30只股票
+
+        # 生成模拟股票
+        result = []
+        for i in range(company_count):
+            # 生成6位数字的股票代码，确保前缀是0或6
+            prefix = "6" if i % 2 == 0 else "0"
+            code = prefix + str(100000 + i).zfill(5)[-5:]
+
+            # 生成股票价格和涨跌幅
+            price = round(random.uniform(10, 100), 2)
+            change = round(random.uniform(-5, 5), 2)
+
+            # 生成成交量和成交额
+            volume = round(random.uniform(100000, 10000000))
+            turnover = round(volume * price / 10000, 2)  # 转换为万元
+
+            # 生成换手率和振幅
+            turnover_rate = round(random.uniform(0.5, 5), 2)
+            amplitude = round(random.uniform(1, 10), 2)
+
+            item = {
+                "code": code,
+                "name": f"{industry}股{i + 1}",
+                "price": price,
+                "change": change,
+                "change_amount": round(price * change / 100, 2),
+                "volume": volume,
+                "turnover": turnover,
+                "amplitude": amplitude,
+                "turnover_rate": turnover_rate
+            }
+            result.append(item)
+
+        # 按涨跌幅排序
+        result.sort(key=lambda x: x["change"], reverse=True)
+
+        return result
+
+    def get_industry_detail(self, industry):
+        """获取行业详细信息"""
+        try:
+            # 获取行业资金流向数据
+            fund_flow_data = self.get_industry_fund_flow("即时")
+            industry_data = next((item for item in fund_flow_data if item["industry"] == industry), None)
+
+            if not industry_data:
+                return None
+
+            # 获取历史资金流向数据
+            history_data = []
+
+            for period in ["3日排行", "5日排行", "10日排行", "20日排行"]:
+                period_data = self.get_industry_fund_flow(period)
+                industry_period_data = next((item for item in period_data if item["industry"] == industry), None)
+
+                if industry_period_data:
+                    days = int(period.replace("日排行", ""))
+                    date = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
+
+                    history_data.append({
+                        "date": date,
+                        "inflow": industry_period_data["inflow"],
+                        "outflow": industry_period_data["outflow"],
+                        "netFlow": industry_period_data["netFlow"],
+                        "change": industry_period_data["change"]
+                    })
+
+            # 添加即时数据
+            history_data.append({
+                "date": datetime.now().strftime("%Y-%m-%d"),
+                "inflow": industry_data["inflow"],
+                "outflow": industry_data["outflow"],
+                "netFlow": industry_data["netFlow"],
+                "change": industry_data["change"]
+            })
+
+            # 按日期排序
+            history_data.sort(key=lambda x: x["date"])
+
+            # 计算行��评分
+            score = self.calculate_industry_score(industry_data, history_data)
+
+            # 生成投资建议
+            recommendation = self.generate_industry_recommendation(score, industry_data, history_data)
+
+            # 构建结果
+            result = {
+                "industry": industry,
+                "index": industry_data["index"],
+                "change": industry_data["change"],
+                "companyCount": industry_data["companyCount"],
+                "inflow": industry_data["inflow"],
+                "outflow": industry_data["outflow"],
+                "netFlow": industry_data["netFlow"],
+                "leadingStock": industry_data.get("leadingStock", ""),
+                "leadingStockChange": industry_data.get("leadingStockChange", ""),
+                "leadingStockPrice": industry_data.get("leadingStockPrice", 0),
+                "score": score,
+                "recommendation": recommendation,
+                "flowHistory": history_data
+            }
+
+            return result
+
+        except Exception as e:
+            self.logger.error(f"获取行业详细信息失败: {str(e)}")
+            import traceback
+            self.logger.error(traceback.format_exc())
+            return None
+
+    def calculate_industry_score(self, industry_data, history_data):
+        """计算行业评分"""
+        try:
+            # 基础分数为50分
+            score = 50
+
+            # 根据涨跌幅增减分数（-10到+10）
+            change = float(industry_data["change"])
+            if change > 3:
+                score += 10
+            elif change > 1:
+                score += 5
+            elif change < -3:
+                score -= 10
+            elif change < -1:
+                score -= 5
+
+            # 根据资金流向增减分数（-20到+20）
+            netFlow = float(industry_data["netFlow"])
+
+            if netFlow > 5:
+                score += 20
+            elif netFlow > 2:
+                score += 15
+            elif netFlow > 0:
+                score += 10
+            elif netFlow < -5:
+                score -= 20
+            elif netFlow < -2:
+                score -= 15
+            elif netFlow < 0:
+                score -= 10
+
+            # 根据历史资金流向趋势增减分数（-10到+10）
+            if len(history_data) >= 2:
+                net_flow_trend = 0
+                for i in range(1, len(history_data)):
+                    if float(history_data[i]["netFlow"]) > float(history_data[i - 1]["netFlow"]):
+                        net_flow_trend += 1
+                    else:
+                        net_flow_trend -= 1
+
+                if net_flow_trend > 0:
+                    score += 10
+                elif net_flow_trend < 0:
+                    score -= 10
+
+            # 限制分数在0-100之间
+            score = max(0, min(100, score))
+
+            return round(score)
+
+        except Exception as e:
+            self.logger.error(f"计算行业评分时出错: {str(e)}")
+            return 50
+
+    def generate_industry_recommendation(self, score, industry_data, history_data):
+        """生成行业投资建议"""
+        try:
+            if score >= 80:
+                return "行业景气度高，资金持续流入，建议积极配置"
+            elif score >= 60:
+                return "行业表现良好，建议适当加仓"
+            elif score >= 40:
+                return "行业表现一般，建议谨慎参与"
+            else:
+                return "行业下行趋势明显，建议减持规避风险"
+
+        except Exception as e:
+            self.logger.error(f"生成行业投资建议时出错: {str(e)}")
+            return "无法生成投资建议"
+
+    def compare_industries(self, limit=10):
+        """比较不同行业的表现"""
+        try:
+            # 获取行业板块数据
+            industry_data = ak.stock_board_industry_name_em()
+
+            # 提取行业名称列表
+            industries = industry_data['板块名称'].tolist() if '板块名称' in industry_data.columns else []
+
+            if not industries:
+                return {"error": "获取行业列表失败"}
+
+            # 限制分析的行业数量
+            industries = industries[:limit] if limit else industries
+
+            # 分析各行业情况
+            industry_results = []
+
+            for industry in industries:
+                try:
+                    # 尝试获取行业板块代码
+                    industry_code = None
+                    for _, row in industry_data.iterrows():
+                        if row['板块名称'] == industry:
+                            industry_code = row['板块代码']
+                            break
+
+                    if not industry_code:
+                        self.logger.warning(f"未找到行业 {industry} 的板块代码")
+                        continue
+
+                    # 尝试使用不同的参数来获取行业数据 - 不使用"3m"
+                    try:
+                        # 尝试不使用period参数
+                        industry_info = ak.stock_board_industry_hist_em(symbol=industry_code)
+                    except Exception as e1:
+                        try:
+                            # 尝试使用daily参数
+                            industry_info = ak.stock_board_industry_hist_em(symbol=industry_code, period="daily")
+                        except Exception as e2:
+                            self.logger.warning(f"分析行业 {industry} 历史数据失败: {str(e1)}, {str(e2)}")
+                            continue
+
+                    # 计算行业涨跌幅
+                    if not industry_info.empty:
+                        latest = industry_info.iloc[0]
+
+                        # 尝试获取涨跌幅，列名可能有变化
+                        change = 0.0
+                        if '涨跌幅' in latest.index:
+                            change = latest['涨跌幅']
+                        elif '涨跌幅' in industry_info.columns:
+                            change = latest['涨跌幅']
+
+                        # 尝试获取成交量和成交额
+                        volume = 0.0
+                        turnover = 0.0
+                        if '成交量' in latest.index:
+                            volume = latest['成交量']
+                        elif '成交量' in industry_info.columns:
+                            volume = latest['成交量']
+
+                        if '成交额' in latest.index:
+                            turnover = latest['成交额']
+                        elif '成交额' in industry_info.columns:
+                            turnover = latest['成交额']
+
+                        industry_results.append({
+                            "industry": industry,
+                            "change": float(change) if change else 0.0,
+                            "volume": float(volume) if volume else 0.0,
+                            "turnover": float(turnover) if turnover else 0.0
+                        })
+                except Exception as e:
+                    self.logger.error(f"分析行业 {industry} 时出错: {str(e)}")
+
+            # 按涨跌幅排序
+            industry_results.sort(key=lambda x: x.get('change', 0), reverse=True)
+
+            return {
+                "count": len(industry_results),
+                "top_industries": industry_results[:5] if len(industry_results) >= 5 else industry_results,
+                "bottom_industries": industry_results[-5:] if len(industry_results) >= 5 else [],
+                "results": industry_results
+            }
+
+        except Exception as e:
+            self.logger.error(f"比较行业表现时出错: {str(e)}")
+            return {"error": f"比较行业表现时出错: {str(e)}"}

industry_api_endpoints.py

@@ -0,0 +1,9 @@
+# -*- coding: utf-8 -*-
+"""
+智能分析系统（股票） - 股票市场数据分析系统
+开发者：熊猫大侠
+版本：v2.1.0
+许可证：MIT License
+"""
+# industry_api_endpoints.py
+# 预留接口

requirements.txt

@@ -0,0 +1,32 @@
+numpy>=1.24.0
+pandas==2.2.2
+scipy>=1.13.0,<1.14.0
+akshare==1.15.94
+tqdm==4.67.1
+openai==0.28.0
+requests==2.32.3
+python-dotenv==1.0.1
+flask==3.1.0
+loguru==0.7.2
+matplotlib==3.9.2
+seaborn==0.13.2
+ipython>=7.34.0
+beautifulsoup4==4.12.3
+html5lib==1.1
+lxml==4.9.4
+jsonpath==0.82.2
+openpyxl==3.1.5
+flask_swagger_ui
+sqlalchemy
+flask-cors
+flask-caching
+# 新增依赖
+gunicorn==20.1.0  # 生产环境WSGI服务器
+PyYAML==6.0  # YAML支持
+scikit-learn==1.2.2  # 机器学习库(用于预测模型)
+statsmodels==0.13.5  # 统计模型(用于时间序列分析)
+pytest==7.3.1  # 测试框架
+
+# 部署工具
+supervisor==4.2.5  # 进程管理
+redis==4.5.4  # 可选的缓存后端

risk_monitor.py

@@ -0,0 +1,375 @@
+# -*- coding: utf-8 -*-
+"""
+智能分析系统（股票） - 股票市场数据分析系统
+开发者：熊猫大侠
+版本：v2.1.0
+许可证：MIT License
+"""
+# risk_monitor.py
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+
+class RiskMonitor:
+    def __init__(self, analyzer):
+        self.analyzer = analyzer
+
+    def analyze_stock_risk(self, stock_code, market_type='A'):
+        """分析单只股票的风险"""
+        try:
+            # 获取股票数据和技术指标
+            df = self.analyzer.get_stock_data(stock_code, market_type)
+            df = self.analyzer.calculate_indicators(df)
+
+            # 计算各类风险指标
+            volatility_risk = self._analyze_volatility_risk(df)
+            trend_risk = self._analyze_trend_risk(df)
+            reversal_risk = self._analyze_reversal_risk(df)
+            volume_risk = self._analyze_volume_risk(df)
+
+            # 综合评估总体风险
+            total_risk_score = (
+                    volatility_risk['score'] * 0.3 +
+                    trend_risk['score'] * 0.3 +
+                    reversal_risk['score'] * 0.25 +
+                    volume_risk['score'] * 0.15
+            )
+
+            # 确定风险等级
+            if total_risk_score >= 80:
+                risk_level = "极高"
+            elif total_risk_score >= 60:
+                risk_level = "高"
+            elif total_risk_score >= 40:
+                risk_level = "中等"
+            elif total_risk_score >= 20:
+                risk_level = "低"
+            else:
+                risk_level = "极低"
+
+            # 生成风险警报
+            alerts = []
+
+            if volatility_risk['score'] >= 70:
+                alerts.append({
+                    "type": "volatility",
+                    "level": "高",
+                    "message": f"波动率风险较高 ({volatility_risk['value']:.2f}%)，可能面临大幅波动"
+                })
+
+            if trend_risk['score'] >= 70:
+                alerts.append({
+                    "type": "trend",
+                    "level": "高",
+                    "message": f"趋势风险较高，当前处于{trend_risk['trend']}趋势，可能面临加速下跌"
+                })
+
+            if reversal_risk['score'] >= 70:
+                alerts.append({
+                    "type": "reversal",
+                    "level": "高",
+                    "message": f"趋势反转风险较高，技术指标显示可能{reversal_risk['direction']}反转"
+                })
+
+            if volume_risk['score'] >= 70:
+                alerts.append({
+                    "type": "volume",
+                    "level": "高",
+                    "message": f"成交量异常，{volume_risk['pattern']}，可能预示价格波动"
+                })
+
+            return {
+                "total_risk_score": total_risk_score,
+                "risk_level": risk_level,
+                "volatility_risk": volatility_risk,
+                "trend_risk": trend_risk,
+                "reversal_risk": reversal_risk,
+                "volume_risk": volume_risk,
+                "alerts": alerts
+            }
+
+        except Exception as e:
+            print(f"分析股票风险出错: {str(e)}")
+            return {
+                "error": f"分析风险时出错: {str(e)}"
+            }
+
+    def _analyze_volatility_risk(self, df):
+        """分析波动率风险"""
+        # 计算近期波动率
+        recent_volatility = df.iloc[-1]['Volatility']
+
+        # 计算波动率变化
+        avg_volatility = df['Volatility'].mean()
+        volatility_change = recent_volatility / avg_volatility - 1
+
+        # 评估风险分数
+        if recent_volatility > 5 and volatility_change > 0.5:
+            score = 90  # 极高风险
+        elif recent_volatility > 4 and volatility_change > 0.3:
+            score = 75  # 高风险
+        elif recent_volatility > 3 and volatility_change > 0.1:
+            score = 60  # 中高风险
+        elif recent_volatility > 2:
+            score = 40  # 中等风险
+        elif recent_volatility > 1:
+            score = 20  # 低风险
+        else:
+            score = 0  # 极低风险
+
+        return {
+            "score": score,
+            "value": recent_volatility,
+            "change": volatility_change * 100,
+            "risk_level": "高" if score >= 60 else "中" if score >= 30 else "低"
+        }
+
+    def _analyze_trend_risk(self, df):
+        """分析趋势风险"""
+        # 获取均线数据
+        ma5 = df.iloc[-1]['MA5']
+        ma20 = df.iloc[-1]['MA20']
+        ma60 = df.iloc[-1]['MA60']
+
+        # 判断当前趋势
+        if ma5 < ma20 < ma60:
+            trend = "下降"
+
+            # 判断下跌加速程度
+            ma5_ma20_gap = (ma20 - ma5) / ma20 * 100
+
+            if ma5_ma20_gap > 5:
+                score = 90  # 极高风险
+            elif ma5_ma20_gap > 3:
+                score = 75  # 高风险
+            elif ma5_ma20_gap > 1:
+                score = 60  # 中高风险
+            else:
+                score = 50  # 中等风险
+
+        elif ma5 > ma20 > ma60:
+            trend = "上升"
+            score = 20  # 低风险
+        else:
+            trend = "盘整"
+            score = 40  # 中等风险
+
+        return {
+            "score": score,
+            "trend": trend,
+            "risk_level": "高" if score >= 60 else "中" if score >= 30 else "低"
+        }
+
+    def _analyze_reversal_risk(self, df):
+        """分析趋势反转风险"""
+        # 获取最新指标
+        rsi = df.iloc[-1]['RSI']
+        macd = df.iloc[-1]['MACD']
+        signal = df.iloc[-1]['Signal']
+        price = df.iloc[-1]['close']
+        ma20 = df.iloc[-1]['MA20']
+
+        # 判断潜在趋势反转信号
+        reversal_signals = 0
+
+        # RSI超买/超卖
+        if rsi > 75:
+            reversal_signals += 1
+            direction = "向下"
+        elif rsi < 25:
+            reversal_signals += 1
+            direction = "向上"
+        else:
+            direction = "无明确方向"
+
+        # MACD死叉/金叉
+        if macd > signal and df.iloc[-2]['MACD'] <= df.iloc[-2]['Signal']:
+            reversal_signals += 1
+            direction = "向上"
+        elif macd < signal and df.iloc[-2]['MACD'] >= df.iloc[-2]['Signal']:
+            reversal_signals += 1
+            direction = "向下"
+
+        # 价格与均线关系
+        if price > ma20 * 1.1:
+            reversal_signals += 1
+            direction = "向下"
+        elif price < ma20 * 0.9:
+            reversal_signals += 1
+            direction = "向上"
+
+        # 评估风险分数
+        if reversal_signals >= 3:
+            score = 90  # 极高风险
+        elif reversal_signals == 2:
+            score = 70  # 高风险
+        elif reversal_signals == 1:
+            score = 40  # 中等风险
+        else:
+            score = 10  # 低风险
+
+        return {
+            "score": score,
+            "reversal_signals": reversal_signals,
+            "direction": direction,
+            "risk_level": "高" if score >= 60 else "中" if score >= 30 else "低"
+        }
+
+    def _analyze_volume_risk(self, df):
+        """分析成交量风险"""
+        # 计算成交量变化
+        recent_volume = df.iloc[-1]['volume']
+        avg_volume = df['volume'].rolling(window=20).mean().iloc[-1]
+        volume_ratio = recent_volume / avg_volume
+
+        # 判断成交量模式
+        if volume_ratio > 3:
+            pattern = "成交量暴增"
+            score = 90  # 极高风险
+        elif volume_ratio > 2:
+            pattern = "成交量显著放大"
+            score = 70  # 高风险
+        elif volume_ratio > 1.5:
+            pattern = "成交量温和放大"
+            score = 50  # 中等风险
+        elif volume_ratio < 0.5:
+            pattern = "成交量萎缩"
+            score = 40  # 中低风险
+        else:
+            pattern = "成交量正常"
+            score = 20  # 低风险
+
+        # 价格与成交量背离分析
+        price_change = (df.iloc[-1]['close'] - df.iloc[-5]['close']) / df.iloc[-5]['close']
+        volume_change = (recent_volume - df.iloc[-5]['volume']) / df.iloc[-5]['volume']
+
+        if price_change > 0.05 and volume_change < -0.3:
+            pattern = "价量背离(价格上涨但量能萎缩)"
+            score = max(score, 80)  # 提高风险评分
+        elif price_change < -0.05 and volume_change < -0.3:
+            pattern = "价量同向(价格下跌且量能萎缩)"
+            score = max(score, 70)  # 提高风险评分
+        elif price_change < -0.05 and volume_change > 0.5:
+            pattern = "价量同向(价格下跌且量能放大)"
+            score = max(score, 85)  # 提高风险评分
+
+        return {
+            "score": score,
+            "volume_ratio": volume_ratio,
+            "pattern": pattern,
+            "risk_level": "高" if score >= 60 else "中" if score >= 30 else "低"
+        }
+
+    def analyze_portfolio_risk(self, portfolio):
+        """分析投资组合整体风险"""
+        try:
+            if not portfolio or len(portfolio) == 0:
+                return {"error": "投资组合为空"}
+
+            # 分析每只股票的风险
+            stock_risks = {}
+            total_weight = 0
+            weighted_risk_score = 0
+
+            for stock in portfolio:
+                stock_code = stock.get('stock_code')
+                weight = stock.get('weight', 1)
+                market_type = stock.get('market_type', 'A')
+
+                if not stock_code:
+                    continue
+
+                # 分析股票风险
+                risk = self.analyze_stock_risk(stock_code, market_type)
+                stock_risks[stock_code] = risk
+
+                # 计算加权风险分数
+                total_weight += weight
+                weighted_risk_score += risk.get('total_risk_score', 50) * weight
+
+            # 计算组合总风险分数
+            if total_weight > 0:
+                portfolio_risk_score = weighted_risk_score / total_weight
+            else:
+                portfolio_risk_score = 0
+
+            # 确定风险等级
+            if portfolio_risk_score >= 80:
+                risk_level = "极高"
+            elif portfolio_risk_score >= 60:
+                risk_level = "高"
+            elif portfolio_risk_score >= 40:
+                risk_level = "中等"
+            elif portfolio_risk_score >= 20:
+                risk_level = "低"
+            else:
+                risk_level = "极低"
+
+            # 收集高风险股票
+            high_risk_stocks = [
+                {
+                    "stock_code": code,
+                    "risk_score": risk.get('total_risk_score', 0),
+                    "risk_level": risk.get('risk_level', '未知')
+                }
+                for code, risk in stock_risks.items()
+                if risk.get('total_risk_score', 0) >= 60
+            ]
+
+            # 收集所有风险警报
+            all_alerts = []
+            for code, risk in stock_risks.items():
+                for alert in risk.get('alerts', []):
+                    all_alerts.append({
+                        "stock_code": code,
+                        **alert
+                    })
+
+            # 分析风险集中度
+            risk_concentration = self._analyze_risk_concentration(portfolio, stock_risks)
+
+            return {
+                "portfolio_risk_score": portfolio_risk_score,
+                "risk_level": risk_level,
+                "high_risk_stocks": high_risk_stocks,
+                "alerts": all_alerts,
+                "risk_concentration": risk_concentration,
+                "stock_risks": stock_risks
+            }
+
+        except Exception as e:
+            print(f"分析投资组合风险出错: {str(e)}")
+            return {
+                "error": f"分析投资组合风险时出错: {str(e)}"
+            }
+
+    def _analyze_risk_concentration(self, portfolio, stock_risks):
+        """分析风险集中度"""
+        # 分析行业集中度
+        industries = {}
+        for stock in portfolio:
+            stock_code = stock.get('stock_code')
+            stock_info = self.analyzer.get_stock_info(stock_code)
+            industry = stock_info.get('行业', '未知')
+            weight = stock.get('weight', 1)
+
+            if industry in industries:
+                industries[industry] += weight
+            else:
+                industries[industry] = weight
+
+        # 找出权重最大的行业
+        max_industry = max(industries.items(), key=lambda x: x[1]) if industries else ('未知', 0)
+
+        # 计算高风险股票总权重
+        high_risk_weight = 0
+        for stock in portfolio:
+            stock_code = stock.get('stock_code')
+            if stock_code in stock_risks and stock_risks[stock_code].get('total_risk_score', 0) >= 60:
+                high_risk_weight += stock.get('weight', 1)
+
+        return {
+            "max_industry": max_industry[0],
+            "max_industry_weight": max_industry[1],
+            "high_risk_weight": high_risk_weight
+        }

scenario_predictor.py

@@ -0,0 +1,202 @@
+# -*- coding: utf-8 -*-
+"""
+智能分析系统（股票） - 股票市场数据分析系统
+开发者：熊猫大侠
+版本：v2.1.0
+许可证：MIT License
+"""
+# scenario_predictor.py
+import os
+import numpy as np
+import pandas as pd
+from datetime import datetime, timedelta
+import openai
+"""
+
+"""
+
+class ScenarioPredictor:
+    def __init__(self, analyzer, openai_api_key=None, openai_model=None):
+        self.analyzer = analyzer
+        self.openai_api_key = os.getenv('OPENAI_API_KEY', os.getenv('OPENAI_API_KEY'))
+        self.openai_api_url = os.getenv('OPENAI_API_URL')
+        self.openai_model = os.getenv('OPENAI_API_MODEL', 'gemini-2.0-pro-exp-02-05')
+
+
+    def generate_scenarios(self, stock_code, market_type='A', days=60):
+        """生成乐观、中性、悲观三种市场情景预测"""
+        try:
+            # 获取股票数据和技术指标
+            df = self.analyzer.get_stock_data(stock_code, market_type)
+            df = self.analyzer.calculate_indicators(df)
+
+            # 获取股票信息
+            stock_info = self.analyzer.get_stock_info(stock_code)
+
+            # 计算基础数据
+            current_price = df.iloc[-1]['close']
+            avg_volatility = df['Volatility'].mean()
+
+            # 根据历史波动率计算情景
+            scenarios = self._calculate_scenarios(df, days)
+
+            # 使用AI生成各情景的分析
+            if self.openai_api_key:
+                ai_analysis = self._generate_ai_analysis(stock_code, stock_info, df, scenarios)
+                scenarios.update(ai_analysis)
+
+            return scenarios
+        except Exception as e:
+            print(f"生成情景预测出错: {str(e)}")
+            return {}
+
+    def _calculate_scenarios(self, df, days):
+        """基于历史数据计算三种情景的价格预测"""
+        current_price = df.iloc[-1]['close']
+
+        # 计算历史波动率和移动均线
+        volatility = df['Volatility'].mean() / 100  # 转换为小数
+        daily_volatility = volatility / np.sqrt(252)  # 转换为日波动率
+        ma20 = df.iloc[-1]['MA20']
+        ma60 = df.iloc[-1]['MA60']
+
+        # 计算乐观情景（上涨至压力位或突破）
+        optimistic_return = 0.15  # 15%上涨
+        if df.iloc[-1]['BB_upper'] > current_price:
+            optimistic_target = df.iloc[-1]['BB_upper'] * 1.05  # 突破上轨5%
+        else:
+            optimistic_target = current_price * (1 + optimistic_return)
+
+        # 计算中性情景（震荡，围绕当前价格或20日均线波动）
+        neutral_target = (current_price + ma20) / 2
+
+        # 计算悲观情景（下跌至支撑位或跌破）
+        pessimistic_return = -0.12  # 12%下跌
+        if df.iloc[-1]['BB_lower'] < current_price:
+            pessimistic_target = df.iloc[-1]['BB_lower'] * 0.95  # 跌破下轨5%
+        else:
+            pessimistic_target = current_price * (1 + pessimistic_return)
+
+        # 计算预期时间
+        time_periods = np.arange(1, days + 1)
+
+        # 生成乐观路径
+        opt_path = [current_price]
+        for _ in range(days):
+            daily_return = (optimistic_target / current_price) ** (1 / days) - 1
+            random_component = np.random.normal(0, daily_volatility)
+            new_price = opt_path[-1] * (1 + daily_return + random_component / 2)
+            opt_path.append(new_price)
+
+        # 生成中性路径
+        neu_path = [current_price]
+        for _ in range(days):
+            daily_return = (neutral_target / current_price) ** (1 / days) - 1
+            random_component = np.random.normal(0, daily_volatility)
+            new_price = neu_path[-1] * (1 + daily_return + random_component)
+            neu_path.append(new_price)
+
+        # 生成悲观路径
+        pes_path = [current_price]
+        for _ in range(days):
+            daily_return = (pessimistic_target / current_price) ** (1 / days) - 1
+            random_component = np.random.normal(0, daily_volatility)
+            new_price = pes_path[-1] * (1 + daily_return + random_component / 2)
+            pes_path.append(new_price)
+
+        # 生成日期序列
+        start_date = datetime.now()
+        dates = [(start_date + timedelta(days=i)).strftime('%Y-%m-%d') for i in range(days + 1)]
+
+        # 组织结果
+        return {
+            'current_price': current_price,
+            'optimistic': {
+                'target_price': optimistic_target,
+                'change_percent': (optimistic_target / current_price - 1) * 100,
+                'path': dict(zip(dates, opt_path))
+            },
+            'neutral': {
+                'target_price': neutral_target,
+                'change_percent': (neutral_target / current_price - 1) * 100,
+                'path': dict(zip(dates, neu_path))
+            },
+            'pessimistic': {
+                'target_price': pessimistic_target,
+                'change_percent': (pessimistic_target / current_price - 1) * 100,
+                'path': dict(zip(dates, pes_path))
+            }
+        }
+
+    def _generate_ai_analysis(self, stock_code, stock_info, df, scenarios):
+        """使用AI生成各情景的分析说明"""
+        try:
+            openai.api_key = self.openai_api_key
+            openai.api_base = self.openai_api_url
+
+            # 提取关键数据
+            current_price = df.iloc[-1]['close']
+            ma5 = df.iloc[-1]['MA5']
+            ma20 = df.iloc[-1]['MA20']
+            ma60 = df.iloc[-1]['MA60']
+            rsi = df.iloc[-1]['RSI']
+            macd = df.iloc[-1]['MACD']
+            signal = df.iloc[-1]['Signal']
+
+            # 构建提示词
+            prompt = f"""分析股票{stock_code}（{stock_info.get('股票名称', '未知')}）的三种市场情景:
+
+1. 当前数据:
+   - 当前价格: {current_price}
+   - 均线: MA5={ma5}, MA20={ma20}, MA60={ma60}
+   - RSI: {rsi}
+   - MACD: {macd}, Signal: {signal}
+
+2. 预测目标价:
+   - 乐观情景: {scenarios['optimistic']['target_price']:.2f} ({scenarios['optimistic']['change_percent']:.2f}%)
+   - 中性情景: {scenarios['neutral']['target_price']:.2f} ({scenarios['neutral']['change_percent']:.2f}%)
+   - 悲观情景: {scenarios['pessimistic']['target_price']:.2f} ({scenarios['pessimistic']['change_percent']:.2f}%)
+
+请为每种情景提供简短分析(每种情景100字以内)，包括可能的触发条件和风险因素。格式为JSON:
+{{
+  "optimistic_analysis": "乐观情景分析...",
+  "neutral_analysis": "中性情景分析...",
+  "pessimistic_analysis": "悲观情景分析..."
+}}
+"""
+
+            # 调用AI API
+            response = openai.ChatCompletion.create(
+                model=self.openai_model,
+                messages=[
+                    {"role": "system", "content": "你是专业的股票分析师，擅长技术分析和情景预测。"},
+                    {"role": "user", "content": prompt}
+                ],
+                temperature=0.7
+            )
+
+            # 解析AI回复
+            import json
+            try:
+                analysis = json.loads(response.choices[0].message.content)
+                return analysis
+            except:
+                # 如果解析失败，尝试从文本中提取JSON
+                import re
+                json_match = re.search(r'```json\s*([\s\S]*?)\s*```', response.choices[0].message.content)
+                if json_match:
+                    json_str = json_match.group(1)
+                    return json.loads(json_str)
+                else:
+                    return {
+                        "optimistic_analysis": "乐观情景分析暂无",
+                        "neutral_analysis": "中性情景分析暂无",
+                        "pessimistic_analysis": "悲观情景分析暂无"
+                    }
+        except Exception as e:
+            print(f"生成AI分析出错: {str(e)}")
+            return {
+                "optimistic_analysis": "乐观情景分析暂无",
+                "neutral_analysis": "中性情景分析暂无",
+                "pessimistic_analysis": "悲观情景分析暂无"
+            }

start.sh

@@ -0,0 +1,271 @@
+#!/bin/bash
+
+# 智能分析系统管理脚本
+# 功能：启动、停止、重启和监控系统服务
+
+# 配置
+APP_NAME="智能分析系统"
+APP_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
+PYTHON_CMD="python"
+SERVER_SCRIPT="web_server.py"
+PID_FILE="${APP_DIR}/.server.pid"
+LOG_FILE="${APP_DIR}/server.log"
+MONITOR_INTERVAL=30  # 监控检查间隔（秒）
+
+# 颜色配置
+RED='\033[0;31m'
+GREEN='\033[0;32m'
+YELLOW='\033[0;33m'
+BLUE='\033[0;34m'
+NC='\033[0m' # 无颜色
+
+# 函数：显示帮助信息
+show_help() {
+    echo -e "${BLUE}${APP_NAME}管理脚本${NC}"
+    echo "使用方法: $0 [命令]"
+    echo ""
+    echo "命令:"
+    echo "  start       启动服务"
+    echo "  stop        停止服务"
+    echo "  restart     重启服务"
+    echo "  status      查看服务状态"
+    echo "  monitor     以守护进程方式监控服务"
+    echo "  logs        查看日志"
+    echo "  help        显示此帮助信息"
+}
+
+# 函数：检查前置条件
+check_prerequisites() {
+    # 检查Python是否已安装
+    if ! command -v $PYTHON_CMD &> /dev/null; then
+        echo -e "${RED}错误: 未找到Python命令。请确保Python已安装且在PATH中。${NC}"
+        exit 1
+    fi
+
+    # 检查server脚本是否存在
+    if [ ! -f "${APP_DIR}/${SERVER_SCRIPT}" ]; then
+        echo -e "${RED}错误: 未找到服务器脚本 ${SERVER_SCRIPT}。${NC}"
+        echo -e "${YELLOW}当前目录: $(pwd)${NC}"
+        exit 1
+    fi
+}
+
+# 函数：获取进程ID
+get_pid() {
+    if [ -f "$PID_FILE" ]; then
+        local pid=$(cat "$PID_FILE")
+        if ps -p $pid > /dev/null; then
+            echo $pid
+            return 0
+        fi
+    fi
+    # 尝试通过进程名查找
+    local pid=$(pgrep -f "python.*${SERVER_SCRIPT}" 2>/dev/null)
+    if [ -n "$pid" ]; then
+        echo $pid
+        return 0
+    fi
+    echo ""
+    return 1
+}
+
+# 函数：启动服务
+start_server() {
+    local pid=$(get_pid)
+    if [ -n "$pid" ]; then
+        echo -e "${YELLOW}警告: 服务已在运行 (PID: $pid)${NC}"
+        return 0
+    fi
+
+    echo -e "${BLUE}正在启动${APP_NAME}...${NC}"
+    cd "$APP_DIR"
+
+    # 使用nohup在后台启动服务
+    nohup $PYTHON_CMD $SERVER_SCRIPT > "$LOG_FILE" 2>&1 &
+    local new_pid=$!
+
+    # 保存PID到文件
+    echo $new_pid > "$PID_FILE"
+
+    # 等待几秒检查服务是否成功启动
+    sleep 3
+    if ps -p $new_pid > /dev/null; then
+        echo -e "${GREEN}${APP_NAME}已成功启动 (PID: $new_pid)${NC}"
+        return 0
+    else
+        echo -e "${RED}启动${APP_NAME}失败。查看日志获取更多信息: ${LOG_FILE}${NC}"
+        return 1
+    fi
+}
+
+# 函数：停止服务
+stop_server() {
+    local pid=$(get_pid)
+    if [ -z "$pid" ]; then
+        echo -e "${YELLOW}服务未运行${NC}"
+        return 0
+    fi
+
+    echo -e "${BLUE}正在停止${APP_NAME} (PID: $pid)...${NC}"
+
+    # 尝试优雅地停止服务
+    kill -15 $pid
+
+    # 等待服务停止
+    local max_wait=10
+    local waited=0
+    while ps -p $pid > /dev/null && [ $waited -lt $max_wait ]; do
+        sleep 1
+        waited=$((waited + 1))
+        echo -ne "${YELLOW}等待服务停止 $waited/$max_wait ${NC}\r"
+    done
+    echo ""
+
+    # 如果服务仍在运行，强制停止
+    if ps -p $pid > /dev/null; then
+        echo -e "${YELLOW}服务未响应优雅停止请求，正在强制终止...${NC}"
+        kill -9 $pid
+        sleep 1
+    fi
+
+    # 检查服务是否已停止
+    if ps -p $pid > /dev/null; then
+        echo -e "${RED}无法停止服务 (PID: $pid)${NC}"
+        return 1
+    else
+        echo -e "${GREEN}服务已成功停止${NC}"
+        rm -f "$PID_FILE"
+        return 0
+    fi
+}
+
+# 函数：重启服务
+restart_server() {
+    echo -e "${BLUE}正在重启${APP_NAME}...${NC}"
+    stop_server
+    sleep 2
+    start_server
+}
+
+# 函数：检查服务状态
+check_status() {
+    local pid=$(get_pid)
+    if [ -n "$pid" ]; then
+        local uptime=$(ps -o etime= -p $pid)
+        local mem=$(ps -o %mem= -p $pid)
+        local cpu=$(ps -o %cpu= -p $pid)
+
+        echo -e "${GREEN}${APP_NAME}正在运行${NC}"
+        echo -e "  PID:     ${BLUE}$pid${NC}"
+        echo -e "  运行时间: ${BLUE}$uptime${NC}"
+        echo -e "  内存使用: ${BLUE}$mem%${NC}"
+        echo -e "  CPU使用:  ${BLUE}$cpu%${NC}"
+        echo -e "  日志文件: ${BLUE}$LOG_FILE${NC}"
+        return 0
+    else
+        echo -e "${YELLOW}${APP_NAME}未运行${NC}"
+        return 1
+    fi
+}
+
+# 函数：监控服务
+monitor_server() {
+    echo -e "${BLUE}开始监控${APP_NAME}...${NC}"
+    echo -e "${BLUE}监控日志将写入: ${LOG_FILE}.monitor${NC}"
+    echo -e "${YELLOW}按 Ctrl+C 停止监控${NC}"
+
+    # 在后台启动监控
+    (
+        while true; do
+            local pid=$(get_pid)
+            local timestamp=$(date '+%Y-%m-%d %H:%M:%S')
+
+            if [ -z "$pid" ]; then
+                echo "$timestamp - 服务未运行，正在重启..." >> "${LOG_FILE}.monitor"
+                cd "$APP_DIR"
+                $PYTHON_CMD $SERVER_SCRIPT >> "$LOG_FILE" 2>&1 &
+                local new_pid=$!
+                echo $new_pid > "$PID_FILE"
+                echo "$timestamp - 服务已重启 (PID: $new_pid)" >> "${LOG_FILE}.monitor"
+            else
+                # 检查服务是否响应 (可以通过访问服务API实现)
+                local is_responsive=true
+
+                # 这里可以添加额外的健康检查逻辑
+                # 例如：使用curl检查API是否响应
+                # if ! curl -s http://localhost:8888/health > /dev/null; then
+                #     is_responsive=false
+                # fi
+
+                if [ "$is_responsive" = false ]; then
+                    echo "$timestamp - 服务无响应，正在重启..." >> "${LOG_FILE}.monitor"
+                    kill -9 $pid
+                    sleep 2
+                    cd "$APP_DIR"
+                    $PYTHON_CMD $SERVER_SCRIPT >> "$LOG_FILE" 2>&1 &
+                    local new_pid=$!
+                    echo $new_pid > "$PID_FILE"
+                    echo "$timestamp - 服务已重启 (PID: $new_pid)" >> "${LOG_FILE}.monitor"
+                fi
+            fi
+
+            sleep $MONITOR_INTERVAL
+        done
+    ) &
+
+    # 保存监控进程PID
+    MONITOR_PID=$!
+    echo $MONITOR_PID > "${APP_DIR}/.monitor.pid"
+    echo -e "${GREEN}监控进程已启动 (PID: $MONITOR_PID)${NC}"
+
+    # 捕获Ctrl+C以停止监控
+    trap 'kill $MONITOR_PID; echo -e "${YELLOW}监控已停止${NC}"; rm -f "${APP_DIR}/.monitor.pid"' INT
+
+    # 等待监控进程
+    wait $MONITOR_PID
+}
+
+# 函数：查看日志
+view_logs() {
+    if [ ! -f "$LOG_FILE" ]; then
+        echo -e "${YELLOW}日志文件不存在: ${LOG_FILE}${NC}"
+        return 1
+    fi
+
+    echo -e "${BLUE}显示最新的日志内容 (按Ctrl+C退出)${NC}"
+    tail -f "$LOG_FILE"
+}
+
+# 主函数
+main() {
+    check_prerequisites
+
+    local command=${1:-"help"}
+
+    case $command in
+        start)
+            start_server
+            ;;
+        stop)
+            stop_server
+            ;;
+        restart)
+            restart_server
+            ;;
+        status)
+            check_status
+            ;;
+        monitor)
+            monitor_server
+            ;;
+        logs)
+            view_logs
+            ;;
+        *)
+            show_help
+            ;;
+    esac
+}
+
+# 执行主函数
+main "$@"

static/swagger.json

@@ -0,0 +1,573 @@
+{
+  "swagger": "2.0",
+  "info": {
+    "title": "股票智能分析系统 API",
+    "description": "股票智能分析系统的REST API文档",
+    "version": "2.1.0"
+  },
+  "host": "localhost:8888",
+  "basePath": "/",
+  "schemes": ["http", "https"],
+  "paths": {
+    "/analyze": {
+      "post": {
+        "summary": "分析股票",
+        "description": "分析单只或多只股票",
+        "parameters": [
+          {
+            "name": "body",
+            "in": "body",
+            "required": true,
+            "schema": {
+              "type": "object",
+              "properties": {
+                "stock_codes": {
+                  "type": "array",
+                  "items": {
+                    "type": "string"
+                  },
+                  "example": ["600519", "000858"]
+                },
+                "market_type": {
+                  "type": "string",
+                  "example": "A"
+                }
+              }
+            }
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功分析股票"
+          },
+          "400": {
+            "description": "请求参数错误"
+          },
+          "500": {
+            "description": "服务器内部错误"
+          }
+        }
+      }
+    },
+    "/api/start_stock_analysis": {
+      "post": {
+        "summary": "启动股票分析任务",
+        "description": "启动异步股票分析任务",
+        "parameters": [
+          {
+            "name": "body",
+            "in": "body",
+            "required": true,
+            "schema": {
+              "type": "object",
+              "properties": {
+                "stock_code": {
+                  "type": "string",
+                  "example": "600519"
+                },
+                "market_type": {
+                  "type": "string",
+                  "example": "A"
+                }
+              }
+            }
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功启动分析任务"
+          }
+        }
+      }
+    },
+"/api/analysis_status/{task_id}": {
+      "get": {
+        "summary": "获取分析任务状态",
+        "description": "获取异步分析任务的状态和结果",
+        "parameters": [
+          {
+            "name": "task_id",
+            "in": "path",
+            "required": true,
+            "type": "string"
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取任务状态和结果"
+          },
+          "404": {
+            "description": "找不到指定的任务"
+          }
+        }
+      }
+    },
+    "/api/stock_data": {
+      "get": {
+        "summary": "获取股票数据",
+        "description": "获取股票历史数据和技术指标",
+        "parameters": [
+          {
+            "name": "stock_code",
+            "in": "query",
+            "required": true,
+            "type": "string"
+          },
+          {
+            "name": "market_type",
+            "in": "query",
+            "required": false,
+            "type": "string",
+            "default": "A"
+          },
+          {
+            "name": "period",
+            "in": "query",
+            "required": false,
+            "type": "string",
+            "enum": ["1m", "3m", "6m", "1y"],
+            "default": "1y"
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取股票数据"
+          },
+          "400": {
+            "description": "请求参数错误"
+          },
+          "500": {
+            "description": "服务器内部错误"
+          }
+        }
+      }
+    },
+    "/api/start_market_scan": {
+      "post": {
+        "summary": "启动市场扫描任务",
+        "description": "启动异步市场扫描任务",
+        "parameters": [
+          {
+            "name": "body",
+            "in": "body",
+            "required": true,
+            "schema": {
+              "type": "object",
+              "properties": {
+                "stock_list": {
+                  "type": "array",
+                  "items": {
+                    "type": "string"
+                  },
+                  "example": ["600519", "000858"]
+                },
+                "min_score": {
+                  "type": "integer",
+                  "example": 60
+                },
+                "market_type": {
+                  "type": "string",
+                  "example": "A"
+                }
+              }
+            }
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功启动扫描任务"
+          }
+        }
+      }
+    },
+    "/api/scan_status/{task_id}": {
+      "get": {
+        "summary": "���取扫描任务状态",
+        "description": "获取异步扫描任务的状态和结果",
+        "parameters": [
+          {
+            "name": "task_id",
+            "in": "path",
+            "required": true,
+            "type": "string"
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取任务状态和结果"
+          },
+          "404": {
+            "description": "找不到指定的任务"
+          }
+        }
+      }
+    },
+    "/api/index_stocks": {
+      "get": {
+        "summary": "获取指数成分股",
+        "description": "获取指定指数的成分股列表",
+        "parameters": [
+          {
+            "name": "index_code",
+            "in": "query",
+            "required": true,
+            "type": "string",
+            "example": "000300"
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取指数成分股"
+          },
+          "400": {
+            "description": "请求参数错误"
+          },
+          "500": {
+            "description": "服务器内部错误"
+          }
+        }
+      }
+    },
+    "/api/industry_stocks": {
+      "get": {
+        "summary": "获取行业成分股",
+        "description": "获取指定行业的成分股列表",
+        "parameters": [
+          {
+            "name": "industry",
+            "in": "query",
+            "required": true,
+            "type": "string",
+            "example": "银行"
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取行业成分股"
+          },
+          "400": {
+            "description": "请求参数错误"
+          },
+          "500": {
+            "description": "服务器内部错误"
+          }
+        }
+      }
+    },
+    "/api/fundamental_analysis": {
+      "post": {
+        "summary": "基本面分析",
+        "description": "获取股票的基本面分析结果",
+        "parameters": [
+          {
+            "name": "body",
+            "in": "body",
+            "required": true,
+            "schema": {
+              "type": "object",
+              "properties": {
+                "stock_code": {
+                  "type": "string",
+                  "example": "600519"
+                }
+              }
+            }
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取基本面分析结果"
+          }
+        }
+      }
+    },
+    "/api/capital_flow": {
+      "post": {
+        "summary": "资金流向分析",
+        "description": "获取股票的资金流向分析结果",
+        "parameters": [
+          {
+            "name": "body",
+            "in": "body",
+            "required": true,
+            "schema": {
+              "type": "object",
+              "properties": {
+                "stock_code": {
+                  "type": "string",
+                  "example": "600519"
+                },
+                "days": {
+                  "type": "integer",
+                  "example": 10
+                }
+              }
+            }
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取资金流向分析结果"
+          }
+        }
+      }
+    },
+    "/api/scenario_predict": {
+      "post": {
+        "summary": "情景预测",
+        "description": "获取股票的多情景预测结果",
+        "parameters": [
+          {
+            "name": "body",
+            "in": "body",
+            "required": true,
+            "schema": {
+              "type": "object",
+              "properties": {
+                "stock_code": {
+                  "type": "string",
+                  "example": "600519"
+                },
+                "market_type": {
+                  "type": "string",
+                  "example": "A"
+                },
+                "days": {
+                  "type": "integer",
+                  "example": 60
+                }
+              }
+            }
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取情景预测结果"
+          }
+        }
+      }
+    },
+    "/api/qa": {
+      "post": {
+        "summary": "智能问答",
+        "description": "获取股票相关问题的智能回答",
+        "parameters": [
+          {
+            "name": "body",
+            "in": "body",
+            "required": true,
+            "schema": {
+              "type": "object",
+              "properties": {
+                "stock_code": {
+                  "type": "string",
+                  "example": "600519"
+                },
+                "question": {
+                  "type": "string",
+                  "example": "这只股票的主要支撑位是多少？"
+                },
+                "market_type": {
+                  "type": "string",
+                  "example": "A"
+                }
+              }
+            }
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取智能回答"
+          }
+        }
+      }
+    },
+    "/api/risk_analysis": {
+      "post": {
+        "summary": "风险分析",
+        "description": "获取股票的风险分析结果",
+        "parameters": [
+          {
+            "name": "body",
+            "in": "body",
+            "required": true,
+            "schema": {
+              "type": "object",
+              "properties": {
+                "stock_code": {
+                  "type": "string",
+                  "example": "600519"
+                },
+                "market_type": {
+                  "type": "string",
+                  "example": "A"
+                }
+              }
+            }
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取风险分析结果"
+          }
+        }
+      }
+    },
+    "/api/portfolio_risk": {
+      "post": {
+        "summary": "投资组合风险分析",
+        "description": "获取投资组合的整体风险分析结果",
+        "parameters": [
+          {
+            "name": "body",
+            "in": "body",
+            "required": true,
+            "schema": {
+              "type": "object",
+              "properties": {
+                "portfolio": {
+                  "type": "array",
+                  "items": {
+                    "type": "object",
+                    "properties": {
+                      "stock_code": {
+                        "type": "string"
+                      },
+                      "weight": {
+                        "type": "number"
+                      },
+                      "market_type": {
+                        "type": "string"
+                      }
+                    }
+                  },
+                  "example": [
+                    {
+                      "stock_code": "600519",
+                      "weight": 30,
+                      "market_type": "A"
+                    },
+                    {
+                      "stock_code": "000858",
+                      "weight": 20,
+                      "market_type": "A"
+                    }
+                  ]
+                }
+              }
+            }
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取投资组合风险分析结果"
+          }
+        }
+      }
+    },
+    "/api/index_analysis": {
+      "get": {
+        "summary": "指数分析",
+        "description": "获取指数的整体分析结果",
+        "parameters": [
+          {
+            "name": "index_code",
+            "in": "query",
+            "required": true,
+            "type": "string",
+            "example": "000300"
+          },
+          {
+            "name": "limit",
+            "in": "query",
+            "required": false,
+            "type": "integer",
+            "example": 30
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取指数分析结果"
+          }
+        }
+      }
+    },
+    "/api/industry_analysis": {
+      "get": {
+        "summary": "行业分析",
+        "description": "获取行业的整体分析结果",
+        "parameters": [
+          {
+            "name": "industry",
+            "in": "query",
+            "required": true,
+            "type": "string",
+            "example": "银行"
+          },
+          {
+            "name": "limit",
+            "in": "query",
+            "required": false,
+            "type": "integer",
+            "example": 30
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取行业分析结果"
+          }
+        }
+      }
+    },
+    "/api/industry_compare": {
+      "get": {
+        "summary": "行业比较",
+        "description": "比较不同行业的表现",
+        "parameters": [
+          {
+            "name": "limit",
+            "in": "query",
+            "required": false,
+            "type": "integer",
+            "example": 10
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取行业比较结果"
+          }
+        }
+      }
+    }
+  },
+  "definitions": {
+    "Stock": {
+      "type": "object",
+      "properties": {
+        "stock_code": {
+          "type": "string"
+        },
+        "stock_name": {
+          "type": "string"
+        },
+        "price": {
+          "type": "number"
+        },
+        "price_change": {
+          "type": "number"
+        }
+      }
+    },
+    "AnalysisResult": {
+      "type": "object",
+      "properties": {
+        "score": {
+          "type": "number"
+        },
+        "recommendation": {
+          "type": "string"
+        }
+      }
+    }
+  }
+}

stock_analyzer.py

@@ -0,0 +1,2131 @@
+# -*- coding: utf-8 -*-
+"""
+智能分析系统（股票） - 股票市场数据分析系统
+修改：熊猫大侠
+版本：v2.1.0
+许可证：MIT License
+"""
+# stock_analyzer.py
+import time
+import traceback
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+import os
+import requests
+from typing import Dict, List, Optional, Tuple
+from dotenv import load_dotenv
+import logging
+import math
+import json
+import threading
+
+# 线程局部存储
+thread_local = threading.local()
+
+
+class StockAnalyzer:
+    """
+    股票分析器 - 原有API保持不变，内部实现增强
+    """
+
+    def __init__(self, initial_cash=1000000):
+        # 设置日志
+        logging.basicConfig(level=logging.INFO,
+                            format='%(asctime)s - %(levelname)s - %(message)s')
+        self.logger = logging.getLogger(__name__)
+
+        # 加载环境变量
+        load_dotenv()
+
+        # 设置 OpenAI API (原 Gemini API)
+        self.openai_api_key = os.getenv('OPENAI_API_KEY', os.getenv('OPENAI_API_KEY'))
+        self.openai_api_url = os.getenv('OPENAI_API_URL', 'https://api.openai.com/v1')
+        self.openai_model = os.getenv('OPENAI_API_MODEL', 'gemini-2.0-pro-exp-02-05')
+        self.news_model = os.getenv('NEWS_MODEL')
+
+        # 配置参数
+        self.params = {
+            'ma_periods': {'short': 5, 'medium': 20, 'long': 60},
+            'rsi_period': 14,
+            'bollinger_period': 20,
+            'bollinger_std': 2,
+            'volume_ma_period': 20,
+            'atr_period': 14
+        }
+
+        # 添加缓存初始化
+        self.data_cache = {}
+
+        # JSON匹配标志
+        self.json_match_flag = True
+    def get_stock_data(self, stock_code, market_type='A', start_date=None, end_date=None):
+        """获取股票数据"""
+        import akshare as ak
+
+        self.logger.info(f"开始获取股票 {stock_code} 数据，市场类型: {market_type}")
+
+        cache_key = f"{stock_code}_{market_type}_{start_date}_{end_date}_price"
+        if cache_key in self.data_cache:
+            cached_df = self.data_cache[cache_key]
+            # 创建一个副本以避免修改缓存数据
+            # 并确保副本的日期类型为datetime
+            result = cached_df.copy()
+            # If 'date' column exists but is not datetime, convert it
+            if 'date' in result.columns and not pd.api.types.is_datetime64_any_dtype(result['date']):
+                try:
+                    result['date'] = pd.to_datetime(result['date'])
+                except Exception as e:
+                    self.logger.warning(f"无法将日期列转换为datetime格式: {str(e)}")
+            return result
+
+        if start_date is None:
+            start_date = (datetime.now() - timedelta(days=365)).strftime('%Y%m%d')
+        if end_date is None:
+            end_date = datetime.now().strftime('%Y%m%d')
+
+        try:
+            # 根据市场类型获取数据
+            if market_type == 'A':
+                df = ak.stock_zh_a_hist(
+                    symbol=stock_code,
+                    start_date=start_date,
+                    end_date=end_date,
+                    adjust="qfq"
+                )
+            elif market_type == 'HK':
+                df = ak.stock_hk_daily(
+                    symbol=stock_code,
+                    adjust="qfq"
+                )
+            elif market_type == 'US':
+                df = ak.stock_us_hist(
+                    symbol=stock_code,
+                    start_date=start_date,
+                    end_date=end_date,
+                    adjust="qfq"
+                )
+            else:
+                raise ValueError(f"不支持的市场类型: {market_type}")
+
+            # 重命名列名以匹配分析需求
+            df = df.rename(columns={
+                "日期": "date",
+                "开盘": "open",
+                "收盘": "close",
+                "最高": "high",
+                "最低": "low",
+                "成交量": "volume",
+                "成交额": "amount"
+            })
+
+            # 确保日期格式正确
+            df['date'] = pd.to_datetime(df['date'])
+
+            # 数据类型转换
+            numeric_columns = ['open', 'close', 'high', 'low', 'volume']
+            for col in numeric_columns:
+                if col in df.columns:
+                    df[col] = pd.to_numeric(df[col], errors='coerce')
+
+            # 删除空值
+            df = df.dropna()
+
+            result = df.sort_values('date')
+
+            # 缓存原始数据（包含datetime类型）
+            self.data_cache[cache_key] = result.copy()
+
+            return result
+
+        except Exception as e:
+            self.logger.error(f"获取股票数据失败: {e}")
+            raise Exception(f"获取股票数据失败: {e}")
+
+    def get_north_flow_history(self, stock_code, start_date=None, end_date=None):
+        """获取单个股票的北向资金历史持股数据"""
+        try:
+            import akshare as ak
+
+            # 获取历史持股数据
+            if start_date is None and end_date is None:
+                # 默认获取近90天数据
+                north_hist_data = ak.stock_hsgt_hist_em(symbol=stock_code)
+            else:
+                north_hist_data = ak.stock_hsgt_hist_em(symbol=stock_code, start_date=start_date, end_date=end_date)
+
+            if north_hist_data.empty:
+                return {"history": []}
+
+            # 转换为列表格式返回
+            history = []
+            for _, row in north_hist_data.iterrows():
+                history.append({
+                    "date": row.get('日期', ''),
+                    "holding": float(row.get('持股数', 0)) if '持股数' in row else 0,
+                    "ratio": float(row.get('持股比例', 0)) if '持股比例' in row else 0,
+                    "change": float(row.get('持股变动', 0)) if '持股变动' in row else 0,
+                    "market_value": float(row.get('持股市值', 0)) if '持股市值' in row else 0
+                })
+
+            return {"history": history}
+        except Exception as e:
+            self.logger.error(f"获取北向资金历史数据出错: {str(e)}")
+            return {"history": []}
+
+    def calculate_ema(self, series, period):
+        """计算指数移动平均线"""
+        return series.ewm(span=period, adjust=False).mean()
+
+    def calculate_rsi(self, series, period):
+        """计算RSI指标"""
+        delta = series.diff()
+        gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
+        loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
+        rs = gain / loss
+        return 100 - (100 / (1 + rs))
+
+    def calculate_macd(self, series):
+        """计算MACD指标"""
+        exp1 = series.ewm(span=12, adjust=False).mean()
+        exp2 = series.ewm(span=26, adjust=False).mean()
+        macd = exp1 - exp2
+        signal = macd.ewm(span=9, adjust=False).mean()
+        hist = macd - signal
+        return macd, signal, hist
+
+    def calculate_bollinger_bands(self, series, period, std_dev):
+        """计算布林带"""
+        middle = series.rolling(window=period).mean()
+        std = series.rolling(window=period).std()
+        upper = middle + (std * std_dev)
+        lower = middle - (std * std_dev)
+        return upper, middle, lower
+
+    def calculate_atr(self, df, period):
+        """计算ATR指标"""
+        high = df['high']
+        low = df['low']
+        close = df['close'].shift(1)
+
+        tr1 = high - low
+        tr2 = abs(high - close)
+        tr3 = abs(low - close)
+
+        tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
+        return tr.rolling(window=period).mean()
+
+    def format_indicator_data(self, df):
+        """格式化指标数据，控制小数位数"""
+
+        # 格式化价格数据 (2位小数)
+        price_columns = ['open', 'close', 'high', 'low', 'MA5', 'MA20', 'MA60', 'BB_upper', 'BB_middle', 'BB_lower']
+        for col in price_columns:
+            if col in df.columns:
+                df[col] = df[col].round(2)
+
+        # 格式化MACD相关指标 (3位小数)
+        macd_columns = ['MACD', 'Signal', 'MACD_hist']
+        for col in macd_columns:
+            if col in df.columns:
+                df[col] = df[col].round(3)
+
+        # 格式化其他技术指标 (2位小数)
+        other_columns = ['RSI', 'Volatility', 'ROC', 'Volume_Ratio']
+        for col in other_columns:
+            if col in df.columns:
+                df[col] = df[col].round(2)
+
+        return df
+
+    def calculate_indicators(self, df):
+        """计算技术指标"""
+
+        try:
+            # 计算移动平均线
+            df['MA5'] = self.calculate_ema(df['close'], self.params['ma_periods']['short'])
+            df['MA20'] = self.calculate_ema(df['close'], self.params['ma_periods']['medium'])
+            df['MA60'] = self.calculate_ema(df['close'], self.params['ma_periods']['long'])
+
+            # 计算RSI
+            df['RSI'] = self.calculate_rsi(df['close'], self.params['rsi_period'])
+
+            # 计算MACD
+            df['MACD'], df['Signal'], df['MACD_hist'] = self.calculate_macd(df['close'])
+
+            # 计算布林带
+            df['BB_upper'], df['BB_middle'], df['BB_lower'] = self.calculate_bollinger_bands(
+                df['close'],
+                self.params['bollinger_period'],
+                self.params['bollinger_std']
+            )
+
+            # 成交量分析
+            df['Volume_MA'] = df['volume'].rolling(window=self.params['volume_ma_period']).mean()
+            df['Volume_Ratio'] = df['volume'] / df['Volume_MA']
+
+            # 计算ATR和波动率
+            df['ATR'] = self.calculate_atr(df, self.params['atr_period'])
+            df['Volatility'] = df['ATR'] / df['close'] * 100
+
+            # 动量指标
+            df['ROC'] = df['close'].pct_change(periods=10) * 100
+
+            # 格式化数据
+            df = self.format_indicator_data(df)
+
+            return df
+
+        except Exception as e:
+            self.logger.error(f"计算技术指标时出错: {str(e)}")
+            raise
+
+    def calculate_score(self, df, market_type='A'):
+        """
+        计算股票评分 - 使用时空共振交易系统增强
+        根据不同的市场特征调整评分权重和标准
+        """
+        try:
+            score = 0
+            latest = df.iloc[-1]
+            prev_days = min(30, len(df) - 1)  # Get the most recent 30 days or all available data
+
+            # 时空共振框架 - 维度1：多时间框架分析
+            # 基础权重配置
+            weights = {
+                'trend': 0.30,  # 趋势因子权重（日线级别）
+                'volatility': 0.15,  # 波动率因子权重
+                'technical': 0.25,  # 技术指标因子权重
+                'volume': 0.20,  # 成交量因子权重（能量守恒维度）
+                'momentum': 0.10  # 动量因子权重（周线级别）
+            }
+
+            # 根据市场类型调整权重（维度1：时间框架嵌套）
+            if market_type == 'US':
+                # 美股优先考虑长期趋势
+                weights['trend'] = 0.35
+                weights['volatility'] = 0.10
+                weights['momentum'] = 0.15
+            elif market_type == 'HK':
+                # 港股调整波动率和成交量权重
+                weights['volatility'] = 0.20
+                weights['volume'] = 0.25
+
+            # 1. 趋势评分（最高30分）- 日线级别分析
+            trend_score = 0
+
+            # 均线评估 - "三线形态"分析
+            if latest['MA5'] > latest['MA20'] and latest['MA20'] > latest['MA60']:
+                # 完美多头排列（维度1：日线形态）
+                trend_score += 15
+            elif latest['MA5'] > latest['MA20']:
+                # 短期上升趋势（维度1：5分钟形态）
+                trend_score += 10
+            elif latest['MA20'] > latest['MA60']:
+                # 中期上升趋势
+                trend_score += 5
+
+            # 价格位置评估
+            if latest['close'] > latest['MA5']:
+                trend_score += 5
+            if latest['close'] > latest['MA20']:
+                trend_score += 5
+            if latest['close'] > latest['MA60']:
+                trend_score += 5
+
+            # 确保不超过最高分数限制
+            trend_score = min(30, trend_score)
+
+            # 2. 波动率评分（最高15分）- 维度2：过滤
+            volatility_score = 0
+
+            # 适度的波动率最理想
+            volatility = latest['Volatility']
+            if 1.0 <= volatility <= 2.5:
+                # 最佳波动率范围
+                volatility_score += 15
+            elif 2.5 < volatility <= 4.0:
+                # 较高波动率，次优选择
+                volatility_score += 10
+            elif volatility < 1.0:
+                # 波动率过低，缺乏能量
+                volatility_score += 5
+            else:
+                # 波动率过高，风险较大
+                volatility_score += 0
+
+            # 3. 技术指标评分（最高25分）- "峰值检测系统"
+            technical_score = 0
+
+            # RSI指标评估（10分）
+            rsi = latest['RSI']
+            if 40 <= rsi <= 60:
+                # 中性区域，趋势稳定
+                technical_score += 7
+            elif 30 <= rsi < 40 or 60 < rsi <= 70:
+                # 阈值区域，可能出现反转信号
+                technical_score += 10
+            elif rsi < 30:
+                # 超卖区域，可能出现买入机会
+                technical_score += 8
+            elif rsi > 70:
+                # 超买区域，可能存在卖出风险
+                technical_score += 2
+
+            # MACD指标评估（10分）- "峰值预警信号"
+            if latest['MACD'] > latest['Signal'] and latest['MACD_hist'] > 0:
+                # MACD金叉且柱状图为正
+                technical_score += 10
+            elif latest['MACD'] > latest['Signal']:
+                # MACD金叉
+                technical_score += 8
+            elif latest['MACD'] < latest['Signal'] and latest['MACD_hist'] < 0:
+                # MACD死叉且柱状图为负
+                technical_score += 0
+            elif latest['MACD_hist'] > df.iloc[-2]['MACD_hist']:
+                # MACD柱状图增长，可能出现反转信号
+                technical_score += 5
+
+            # 布林带位置评估（5分）
+            bb_position = (latest['close'] - latest['BB_lower']) / (latest['BB_upper'] - latest['BB_lower'])
+            if 0.3 <= bb_position <= 0.7:
+                # 价格在布林带中间区域，趋势稳定
+                technical_score += 3
+            elif bb_position < 0.2:
+                # 价格接近下轨，可能超卖
+                technical_score += 5
+            elif bb_position > 0.8:
+                # 价格接近上轨，可能超买
+                technical_score += 1
+
+            # 确保最大分数限制
+            technical_score = min(25, technical_score)
+
+            # 4. 成交量评分（最高20分）- "能量守恒维度"
+            volume_score = 0
+
+            # 成交量趋势分析
+            recent_vol_ratio = [df.iloc[-i]['Volume_Ratio'] for i in range(1, min(6, len(df)))]
+            avg_vol_ratio = sum(recent_vol_ratio) / len(recent_vol_ratio)
+
+            if avg_vol_ratio > 1.5 and latest['close'] > df.iloc[-2]['close']:
+                # 成交量放大且价格上涨 - "成交量能量阈值突破"
+                volume_score += 20
+            elif avg_vol_ratio > 1.2 and latest['close'] > df.iloc[-2]['close']:
+                # 成交量和价格同步上涨
+                volume_score += 15
+            elif avg_vol_ratio < 0.8 and latest['close'] < df.iloc[-2]['close']:
+                # 成交量和价格同步下跌，可能是健康回调
+                volume_score += 10
+            elif avg_vol_ratio > 1.2 and latest['close'] < df.iloc[-2]['close']:
+                # 成交量增加但价格下跌，可能存在较大卖压
+                volume_score += 0
+            else:
+                # 其他情况
+                volume_score += 8
+
+            # 5. 动量评分（最高10分）- 维度1：周线级别
+            momentum_score = 0
+
+            # ROC动量指标
+            roc = latest['ROC']
+            if roc > 5:
+                # Strong upward momentum
+                momentum_score += 10
+            elif 2 <= roc <= 5:
+                # Moderate upward momentum
+                momentum_score += 8
+            elif 0 <= roc < 2:
+                # Weak upward momentum
+                momentum_score += 5
+            elif -2 <= roc < 0:
+                # Weak downward momentum
+                momentum_score += 3
+            else:
+                # Strong downward momentum
+                momentum_score += 0
+
+            # 根据加权因子计算总分 - “共振公式”
+            final_score = (
+                    trend_score * weights['trend'] / 0.30 +
+                    volatility_score * weights['volatility'] / 0.15 +
+                    technical_score * weights['technical'] / 0.25 +
+                    volume_score * weights['volume'] / 0.20 +
+                    momentum_score * weights['momentum'] / 0.10
+            )
+
+            # 特殊市场调整 - “市场适应机制”
+            if market_type == 'US':
+                # 美国市场额外调整因素
+                # 检查是否为财报季
+                is_earnings_season = self._is_earnings_season()
+                if is_earnings_season:
+                    # Earnings season has higher volatility, adjust score certainty
+                    final_score = 0.9 * final_score + 5  # Slight regression to the mean
+
+            elif market_type == 'HK':
+                # 港股特殊调整
+                # 检查A股联动效应
+                a_share_linkage = self._check_a_share_linkage(df)
+                if a_share_linkage > 0.7:  # High linkage
+                    # 根据大陆市场情绪调整
+                    mainland_sentiment = self._get_mainland_market_sentiment()
+                    if mainland_sentiment > 0:
+                        final_score += 5
+                    else:
+                        final_score -= 5
+
+            # Ensure score remains within 0-100 range
+            final_score = max(0, min(100, round(final_score)))
+
+            # Store sub-scores for display
+            self.score_details = {
+                'trend': trend_score,
+                'volatility': volatility_score,
+                'technical': technical_score,
+                'volume': volume_score,
+                'momentum': momentum_score,
+                'total': final_score
+            }
+
+            return final_score
+
+        except Exception as e:
+            self.logger.error(f"Error calculating score: {str(e)}")
+            # Return neutral score on error
+            return 50
+
+    def calculate_position_size(self, stock_code, risk_percent=2.0, stop_loss_percent=5.0):
+        """
+        根据风险管理原则计算最佳仓位大小
+        实施时空共振系统的“仓位大小公式”
+
+        参数:
+            stock_code: 要分析的股票代码
+            risk_percent: 在此交易中承担风险的总资本百分比（默认为2％）
+            stop_loss_percent: 从入场点的止损百分比（默认为5��）
+
+        返回:
+            仓位大小占总资本的百分比
+        """
+        try:
+            # Get stock data
+            df = self.get_stock_data(stock_code)
+            df = self.calculate_indicators(df)
+
+            # 获取波动率因子（来自维度3：能量守恒）
+            latest = df.iloc[-1]
+            volatility = latest['Volatility']
+
+            # 计算波动率调整因子（较高波动率=较小仓位）
+            volatility_factor = 1.0
+            if volatility > 4.0:
+                volatility_factor = 0.6  # Reduce position for high volatility stocks
+            elif volatility > 2.5:
+                volatility_factor = 0.8  # Slightly reduce position
+            elif volatility < 1.0:
+                volatility_factor = 1.2  # Can increase position for low volatility stocks
+
+            # Calculate position size using risk formula
+            # 公式：position_size = (风险金额) / (止损 * 波动率因子)
+            position_size = (risk_percent) / (stop_loss_percent * volatility_factor)
+
+            # 限制最大仓位为25％以实现多元化
+            position_size = min(position_size, 25.0)
+
+            return position_size
+
+        except Exception as e:
+            self.logger.error(f"Error calculating position size: {str(e)}")
+            # 返回保守的默认仓位大小（出错时）
+            return 5.0
+
+    def get_recommendation(self, score, market_type='A', technical_data=None, news_data=None):
+        """
+        根据得分和附加信息生成投资建议
+        使用时空共振交易系统策略增强
+        """
+        try:
+            # 1. Base recommendation logic - Dynamic threshold adjustment based on score
+            if score >= 85:
+                base_recommendation = '强烈建议买入'
+                confidence = 'high'
+                action = 'strong_buy'
+            elif score >= 70:
+                base_recommendation = '建议买入'
+                confidence = 'medium_high'
+                action = 'buy'
+            elif score >= 55:
+                base_recommendation = '谨慎买入'
+                confidence = 'medium'
+                action = 'cautious_buy'
+            elif score >= 45:
+                base_recommendation = '持观望态度'
+                confidence = 'medium'
+                action = 'hold'
+            elif score >= 30:
+                base_recommendation = '谨慎持有'
+                confidence = 'medium'
+                action = 'cautious_hold'
+            elif score >= 15:
+                base_recommendation = '建议减仓'
+                confidence = 'medium_high'
+                action = 'reduce'
+            else:
+                base_recommendation = '建议卖出'
+                confidence = 'high'
+                action = 'sell'
+
+            # 2. Consider market characteristics (Dimension 1: Timeframe Nesting)
+            market_adjustment = ""
+            if market_type == 'US':
+                # US market adjustment factors
+                if self._is_earnings_season():
+                    if confidence == 'high' or confidence == 'medium_high':
+                        confidence = 'medium'
+                        market_adjustment = "（财报季临近，波动可能加大，建议适当控制仓位）"
+
+            elif market_type == 'HK':
+                # HK market adjustment factors
+                mainland_sentiment = self._get_mainland_market_sentiment()
+                if mainland_sentiment < -0.3 and (action == 'buy' or action == 'strong_buy'):
+                    action = 'cautious_buy'
+                    confidence = 'medium'
+                    market_adjustment = "（受大陆市场情绪影响，建议控制风险）"
+
+            elif market_type == 'A':
+                # A-share specific adjustment factors
+                if technical_data and 'Volatility' in technical_data:
+                    vol = technical_data.get('Volatility', 0)
+                    if vol > 4.0 and (action == 'buy' or action == 'strong_buy'):
+                        action = 'cautious_buy'
+                        confidence = 'medium'
+                        market_adjustment = "（市场波动较大，建议分批买入）"
+
+            # 3. Consider market sentiment (Dimension 2: Filtering)
+            sentiment_adjustment = ""
+            if news_data and 'market_sentiment' in news_data:
+                sentiment = news_data.get('market_sentiment', 'neutral')
+
+                if sentiment == 'bullish' and action in ['hold', 'cautious_hold']:
+                    action = 'cautious_buy'
+                    sentiment_adjustment = "（市场氛围积极，可适当提高仓位）"
+
+                elif sentiment == 'bearish' and action in ['buy', 'cautious_buy']:
+                    action = 'hold'
+                    sentiment_adjustment = "（市场氛围悲观，建议等���更好买点）"
+            elif self.json_match_flag==False:
+                import re
+
+                # 如果JSON解析失败，尝试从原始内容中匹配市场情绪
+                sentiment_pattern = r'(bullish|neutral|bearish)'
+                sentiment_match = re.search(sentiment_pattern, news_data.get('original_content', ''))
+                
+                if sentiment_match:
+                    sentiment_map = {
+                        'bullish': 'bullish',
+                        'neutral': 'neutral',
+                        'bearish': 'bearish'
+                    }
+                    sentiment = sentiment_map.get(sentiment_match.group(1), 'neutral')
+                    
+                    if sentiment == 'bullish' and action in ['hold', 'cautious_hold']:
+                        action = 'cautious_buy'
+                        sentiment_adjustment = "（市场氛围积极，可适当提高仓位）"
+                    elif sentiment == 'bearish' and action in ['buy', 'cautious_buy']:
+                        action = 'hold'
+                        sentiment_adjustment = "（市场氛围悲观，建议等待更好买点）"
+
+                    
+            # 4. Technical indicators adjustment (Dimension 2: "Peak Detection System")
+            technical_adjustment = ""
+            if technical_data:
+                rsi = technical_data.get('RSI', 50)
+                macd_signal = technical_data.get('MACD_signal', 'neutral')
+
+                # RSI overbought/oversold adjustment
+                if rsi > 80 and action in ['buy', 'strong_buy']:
+                    action = 'hold'
+                    technical_adjustment = "（RSI指标显示超买，建议等待回调）"
+                elif rsi < 20 and action in ['sell', 'reduce']:
+                    action = 'hold'
+                    technical_adjustment = "（RSI指标显示超卖，可能存在反弹机会）"
+
+                # MACD signal adjustment
+                if macd_signal == 'bullish' and action in ['hold', 'cautious_hold']:
+                    action = 'cautious_buy'
+                    if not technical_adjustment:
+                        technical_adjustment = "（MACD显示买入信号）"
+                elif macd_signal == 'bearish' and action in ['cautious_buy', 'buy']:
+                    action = 'hold'
+                    if not technical_adjustment:
+                        technical_adjustment = "（MACD显示卖出信号）"
+
+            # 5. Convert adjusted action to final recommendation
+            action_to_recommendation = {
+                'strong_buy': '强烈建议买入',
+                'buy': '建议买入',
+                'cautious_buy': '谨慎买入',
+                'hold': '持观望态度',
+                'cautious_hold': '谨慎持有',
+                'reduce': '建议减仓',
+                'sell': '建议卖出'
+            }
+
+            final_recommendation = action_to_recommendation.get(action, base_recommendation)
+
+            # 6. Combine all adjustment factors
+            adjustments = " ".join(filter(None, [market_adjustment, sentiment_adjustment, technical_adjustment]))
+
+            if adjustments:
+                return f"{final_recommendation} {adjustments}"
+            else:
+                return final_recommendation
+
+        except Exception as e:
+            self.logger.error(f"Error generating investment recommendation: {str(e)}")
+            # Return safe default recommendation on error
+            return "无法提供明确建议，请结合多种因素谨慎决策"
+
+    def check_consecutive_losses(self, trade_history, max_consecutive_losses=3):
+        """
+        实施“冷静期风险控制” - 连续亏损后停止交易
+
+        参数:
+            trade_history: 最近交易结果列表 (True 表示盈利, False 表示亏损)
+            max_consecutive_losses: 允许的最大连续亏损次数
+
+        返回:
+            Boolean: True 如果应该暂停交易, False 如果可以继续交易
+        """
+        consecutive_losses = 0
+
+        # Count consecutive losses from most recent trades
+        for trade in reversed(trade_history):
+            if not trade:  # If trade is a loss
+                consecutive_losses += 1
+            else:
+                break  # Break on first profitable trade
+
+        # Return True if we've hit max consecutive losses
+        return consecutive_losses >= max_consecutive_losses
+
+    def check_profit_taking(self, current_profit_percent, threshold=20.0):
+        """
+        当回报超过阈值时，实施获利了结机制
+        属于“能量守恒维度”的一部分
+
+        参数:
+            current_profit_percent: 当前利润百分比
+            threshold: 用于获利了结的利润百分比阈值
+
+        返回:
+            Float: 减少仓位的百分比 (0.0-1.0)
+        """
+        if current_profit_percent >= threshold:
+            # If profit exceeds threshold, suggest reducing position by 50%
+            return 0.5
+
+        return 0.0  # No position reduction recommended
+
+    def _is_earnings_season(self):
+        """检查当前是否处于财报季(辅助函数)"""
+        from datetime import datetime
+        current_month = datetime.now().month
+        # 美股财报季大致在1月、4月、7月和10月
+        return current_month in [1, 4, 7, 10]
+
+    def _check_a_share_linkage(self, df, window=20):
+        """检查港股与A股的联动性(辅助函数)"""
+        # 该函数需要获取对应的A股指数数据
+        # 简化版实现:
+        try:
+            # 获取恒生指数与上证指数的相关系数
+            # 实际实现中需要获取真实数据
+            correlation = 0.6  # 示例值
+            return correlation
+        except:
+            return 0.5  # 默认中等关联度
+
+    def _get_mainland_market_sentiment(self):
+        """获取中国大陆市场情绪(辅助函数)"""
+        # 实际实现中需要分析上证指数、北向资金等因素
+        try:
+            # 简化版实现，返回-1到1之间的值，1表示积极情绪
+            sentiment = 0.2  # 示例值
+            return sentiment
+        except:
+            return 0  # 默认中性情绪
+
+    def get_stock_news(self, stock_code, market_type='A', limit=5):
+        """
+        获取股票相关新闻和实时信息，通过OpenAI API调用function calling方式获取
+        参数:
+            stock_code: 股票代码
+            market_type: 市场类型 (A/HK/US)
+            limit: 返回的新闻条数上限
+        返回:
+            包含新闻和公告的字典
+        """
+        try:
+            self.logger.info(f"获取股票 {stock_code} 的相关新闻和信息")
+
+            # 缓存键
+            cache_key = f"{stock_code}_{market_type}_news"
+            if cache_key in self.data_cache and (
+                    datetime.now() - self.data_cache[cache_key]['timestamp']).seconds < 3600:
+                # 缓存1小时内的数据
+                return self.data_cache[cache_key]['data']
+
+            # 获取股票基本信息
+            stock_info = self.get_stock_info(stock_code)
+            stock_name = stock_info.get('股票名称', '未知')
+            industry = stock_info.get('行业', '未知')
+
+            # 构建新闻查询的prompt
+            market_name = "A股" if market_type == 'A' else "港股" if market_type == 'HK' else "美股"
+            query = f"""请帮我搜索以下股票的最新相关新闻和信息:
+            股票名称: {stock_name}
+            股票代码: {stock_code}
+            市场: {market_name}
+            行业: {industry}
+            
+            请使用search_news工具搜索相关新闻，然后只需要返回JSON格式。
+            按照以下格式的JSON数据返回:
+            {{
+                "news": [
+                    {{"title": "新闻标题", "date": "YYYY-MM-DD", "source": "新闻来源", "summary": "新闻摘要"}},
+                    ...
+                ],
+                "announcements": [
+                    {{"title": "公告标题", "date": "YYYY-MM-DD", "type": "公告类型"}},
+                    ...
+                ],
+                "industry_news": [
+                    {{"title": "行业新闻标题", "date": "YYYY-MM-DD", "summary": "新闻摘要"}},
+                    ...
+                ],
+                "market_sentiment": "市场情绪(bullish/slightly_bullish/neutral/slightly_bearish/bearish)"
+            }}
+            注意只返回json数据，不要返回其他内容。
+            """
+
+            # 定义函数调用工具
+            tools = [
+                {
+                    "type": "function",
+                    "function": {
+                        "name": "search_news",
+                        "description": "搜索股票相关的新闻和信息",
+                        "parameters": {
+                            "type": "object",
+                            "properties": {
+                                "query": {
+                                    "type": "string",
+                                    "description": "搜索查询词，用于查找相关新闻"
+                                }
+                            },
+                            "required": ["query"]
+                        }
+                    }
+                }
+            ]
+
+            # 使用线程和队列添加超时控制
+            import queue
+            import threading
+            import json
+            import openai
+            import requests
+
+            result_queue = queue.Queue()
+
+            def search_news(query):
+                """实际执行搜索的函数"""
+                try:
+                    # 获取SERP API密钥
+                    serp_api_key = os.getenv('SERP_API_KEY')
+                    if not serp_api_key:
+                        self.logger.error("未找到SERP_API_KEY环境变量")
+                        return {"error": "未配置搜索API密钥"}
+                    
+                    # 构建搜索查询
+                    search_query = f"{stock_name} {stock_code} {market_name} 最新新闻 公告"
+                    
+                    # 调用SERP API
+                    url = "https://serpapi.com/search"
+                    params = {
+                        "engine": "google",
+                        "q": search_query,
+                        "api_key": serp_api_key,
+                        "tbm": "nws",  # 新闻搜索
+                        "num": limit * 2  # 获取更多结果以便筛选
+                    }
+                    
+                    response = requests.get(url, params=params)
+                    search_results = response.json()
+                    
+                    # 提取新闻结果
+                    news_results = []
+                    if "news_results" in search_results:
+                        for item in search_results["news_results"][:limit]:
+                            news_results.append({
+                                "title": item.get("title", ""),
+                                "date": item.get("date", ""),
+                                "source": item.get("source", ""),
+                                "link": item.get("link", ""),
+                                "snippet": item.get("snippet", "")
+                            })
+                    
+                    # 构建行业新闻查询
+                    industry_query = f"{industry} {market_name} 行业动态 最新消息"
+                    industry_params = {
+                        "engine": "google",
+                        "q": industry_query,
+                        "api_key": serp_api_key,
+                        "tbm": "nws",
+                        "num": limit
+                    }
+                    
+                    industry_response = requests.get(url, params=industry_params)
+                    industry_results = industry_response.json()
+                    
+                    # 提取行业新闻
+                    industry_news = []
+                    if "news_results" in industry_results:
+                        for item in industry_results["news_results"][:limit]:
+                            industry_news.append({
+                                "title": item.get("title", ""),
+                                "date": item.get("date", ""),
+                                "source": item.get("source", ""),
+                                "summary": item.get("snippet", "")
+                            })
+                    
+                    # 获取公告信息 (可能需要专门的API或网站爬取)
+                    # 这里简化处理，实际应用中可能需要更复杂的逻辑
+                    announcements = []
+                    
+                    # 分析市场情绪
+                    # 简单实现：基于新闻标题和摘要的关键词分析
+                    sentiment_keywords = {
+                        'bullish': ['上涨', '增长', '利好', '突破', '强势', '看好', '机会', '利润'],
+                        'slightly_bullish': ['回升', '改善', '企稳', '向好', '期待'],
+                        'neutral': ['稳定', '平稳', '持平', '不变'],
+                        'slightly_bearish': ['回调', '承压', '谨慎', '风险', '下滑'],
+                        'bearish': ['下跌', '亏损', '跌破', '利空', '警惕', '危机', '崩盘']
+                    }
+                    
+                    # 计算情绪得分
+                    sentiment_scores = {k: 0 for k in sentiment_keywords.keys()}
+                    all_text = " ".join([n.get("title", "") + " " + n.get("snippet", "") for n in news_results])
+                    
+                    for sentiment, keywords in sentiment_keywords.items():
+                        for keyword in keywords:
+                            if keyword in all_text:
+                                sentiment_scores[sentiment] += 1
+                    
+                    # 确定主导情绪
+                    if not sentiment_scores or all(score == 0 for score in sentiment_scores.values()):
+                        market_sentiment = "neutral"
+                    else:
+                        market_sentiment = max(sentiment_scores.items(), key=lambda x: x[1])[0]
+                    
+                    return {
+                        "news": news_results,
+                        "announcements": announcements,
+                        "industry_news": industry_news,
+                        "market_sentiment": market_sentiment
+                    }
+                    
+                except Exception as e:
+                    self.logger.error(f"搜索新闻时出错: {str(e)}")
+                    return {"error": str(e)}
+
+            def call_api():
+                try:
+                    messages = [{"role": "user", "content": query}]
+                    
+                    # 第一步：调用模型，让它决定使用工具
+                    response = openai.ChatCompletion.create(
+                        model=self.news_model,
+                        messages=messages,
+                        tools=tools,
+                        tool_choice="auto",
+                        temperature=0.7,
+                        max_tokens=1000,
+                        stream=False,
+                        timeout=120
+                    )
+                    
+                    # 检查是否有工具调用
+                    message = response["choices"][0]["message"]
+                    
+                    if "tool_calls" in message:
+                        # 处理工具调用
+                        tool_calls = message["tool_calls"]
+                        
+                        # 准备新的消息列表，包含工具调用结果
+                        messages.append(message)  # 添加助手的消息
+                        
+                        for tool_call in tool_calls:
+                            function_name = tool_call["function"]["name"]
+                            function_args = json.loads(tool_call["function"]["arguments"])
+                            
+                            # 执行搜索
+                            if function_name == "search_news":
+                                search_query = function_args.get("query", f"{stock_name} {stock_code} 新闻")
+                                function_response = search_news(search_query)
+                                
+                                # 添加工具响应到消息
+                                messages.append({
+                                    "tool_call_id": tool_call["id"],
+                                    "role": "tool",
+                                    "name": function_name,
+                                    "content": json.dumps(function_response, ensure_ascii=False)
+                                })
+                        
+                        # 第二步：让模型处理搜索结果并生成最终响应
+                        second_response = openai.ChatCompletion.create(
+                            model=self.news_model,
+                            messages=messages,
+                            temperature=0.7,
+                            max_tokens=4000,
+                            stream=False,
+                            timeout=120
+                        )
+                        
+                        result_queue.put(second_response)
+                    else:
+                        # 如果模型没有选择使用工具，直接使用第一次响应
+                        result_queue.put(response)
+                        
+                except Exception as e:
+                    result_queue.put(e)
+
+            # 启动API调用线程
+            api_thread = threading.Thread(target=call_api)
+            api_thread.daemon = True
+            api_thread.start()
+
+            # 等待结果，最多等待240秒
+            try:
+                result = result_queue.get(timeout=240)
+
+                # 检查结果是否为异常
+                if isinstance(result, Exception):
+                    self.logger.error(f"获取新闻API调用失败: {str(result)}")
+                    raise result
+
+                # 提取回复内容
+                content = result["choices"][0]["message"]["content"].strip()
+
+                # 尝试解析JSON，但如果失败则保留原始内容
+                try:
+                    # 尝试直接解析JSON
+                    news_data = json.loads(content)
+                except json.JSONDecodeError:
+                    # 如果直接解析失败，尝试提取JSON部分
+                    import re
+                    json_match = re.search(r'```json\s*([\s\S]*?)\s*```', content)
+                    if json_match:
+                        json_str = json_match.group(1)
+                        news_data = json.loads(json_str)
+                        self.json_match_flag = True
+                    else:
+                        # 如果仍然无法提取JSON，尝试直接返回响应
+                        self.logger.info(f"无法提取JSON，直接返回响应{content}")
+                        self.json_match_flag = False
+                        news_data = {}
+                        news_data['original_content'] = content
+
+                # 确保数据结构完整
+                if not isinstance(news_data, dict):
+                    news_data = {}
+                
+                for key in ['news', 'announcements', 'industry_news']:
+                    if key not in news_data:
+                        news_data[key] = []
+                
+                if 'market_sentiment' not in news_data:
+                    news_data['market_sentiment'] = 'neutral'
+
+                # 添加时间戳
+                news_data['timestamp'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+
+                # 缓存结果
+                self.data_cache[cache_key] = {
+                    'data': news_data,
+                    'timestamp': datetime.now()
+                }
+
+                return news_data
+
+            except queue.Empty:
+                self.logger.warning("获取新闻API调用超时")
+                return {
+                    'news': [],
+                    'announcements': [],
+                    'industry_news': [],
+                    'market_sentiment': 'neutral',
+                    'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+                }
+            except Exception as e:
+                self.logger.error(f"处理新闻数据时出错: {str(e)}")
+                return {
+                    'news': [],
+                    'announcements': [],
+                    'industry_news': [],
+                    'market_sentiment': 'neutral',
+                    'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+                }
+
+        except Exception as e:
+            self.logger.error(f"获取股票新闻时出错: {str(e)}")
+            # 出错时返回空结果
+            return {
+                'news': [],
+                'announcements': [],
+                'industry_news': [],
+                'market_sentiment': 'neutral',
+                'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+            }
+
+    # def get_recommendation(self, score, market_type='A', technical_data=None, news_data=None):
+    #     """
+    #     根据得分和附加信息给出平滑的投资建议
+    #
+    #     参数:
+    #         score: 股票综合评分 (0-100)
+    #         market_type: 市场类型 (A/HK/US)
+    #         technical_data: 技术指标数据 (可选)
+    #         news_data: 新闻和市场情绪数据 (可选)
+    #
+    #     返回:
+    #         投资建议字符串
+    #     """
+    #     try:
+    #         # 1. 基础建议逻辑 - 基于分数的平滑建议
+    #         if score >= 85:
+    #             base_recommendation = '强烈建议买入'
+    #             confidence = 'high'
+    #             action = 'strong_buy'
+    #         elif score >= 70:
+    #             base_recommendation = '建议买入'
+    #             confidence = 'medium_high'
+    #             action = 'buy'
+    #         elif score >= 55:
+    #             base_recommendation = '谨慎买入'
+    #             confidence = 'medium'
+    #             action = 'cautious_buy'
+    #         elif score >= 45:
+    #             base_recommendation = '持观望态度'
+    #             confidence = 'medium'
+    #             action = 'hold'
+    #         elif score >= 30:
+    #             base_recommendation = '谨慎持有'
+    #             confidence = 'medium'
+    #             action = 'cautious_hold'
+    #         elif score >= 15:
+    #             base_recommendation = '建议减仓'
+    #             confidence = 'medium_high'
+    #             action = 'reduce'
+    #         else:
+    #             base_recommendation = '建议卖出'
+    #             confidence = 'high'
+    #             action = 'sell'
+    #
+    #         # 2. 考虑市场特性
+    #         market_adjustment = ""
+    #         if market_type == 'US':
+    #             # 美股调整因素
+    #             if self._is_earnings_season():
+    #                 if confidence == 'high' or confidence == 'medium_high':
+    #                     confidence = 'medium'
+    #                     market_adjustment = "（财报季临近，波动可能加大，建议适当控制仓位）"
+    #
+    #         elif market_type == 'HK':
+    #             # 港股调整因素
+    #             mainland_sentiment = self._get_mainland_market_sentiment()
+    #             if mainland_sentiment < -0.3 and (action == 'buy' or action == 'strong_buy'):
+    #                 action = 'cautious_buy'
+    #                 confidence = 'medium'
+    #                 market_adjustment = "（受大陆市场情绪影响，建议控制风险）"
+    #
+    #         elif market_type == 'A':
+    #             # A股特有调整因素
+    #             if technical_data and 'Volatility' in technical_data:
+    #                 vol = technical_data.get('Volatility', 0)
+    #                 if vol > 4.0 and (action == 'buy' or action == 'strong_buy'):
+    #                     action = 'cautious_buy'
+    #                     confidence = 'medium'
+    #                     market_adjustment = "（市场波动较大，建议分批买入）"
+    #
+    #         # 3. 考虑市场���绪
+    #         sentiment_adjustment = ""
+    #         if news_data and 'market_sentiment' in news_data:
+    #             sentiment = news_data.get('market_sentiment', 'neutral')
+    #
+    #             if sentiment == 'bullish' and action in ['hold', 'cautious_hold']:
+    #                 action = 'cautious_buy'
+    #                 sentiment_adjustment = "（市场氛围积极，可适当提高仓位）"
+    #
+    #             elif sentiment == 'bearish' and action in ['buy', 'cautious_buy']:
+    #                 action = 'hold'
+    #                 sentiment_adjustment = "（市场氛围悲观，建议等待更好买点）"
+    #
+    #         # 4. 技术指标微调
+    #         technical_adjustment = ""
+    #         if technical_data:
+    #             rsi = technical_data.get('RSI', 50)
+    #             macd_signal = technical_data.get('MACD_signal', 'neutral')
+    #
+    #             # RSI超买超卖调整
+    #             if rsi > 80 and action in ['buy', 'strong_buy']:
+    #                 action = 'hold'
+    #                 technical_adjustment = "（RSI指标显示超买，建议等待回调）"
+    #             elif rsi < 20 and action in ['sell', 'reduce']:
+    #                 action = 'hold'
+    #                 technical_adjustment = "（RSI指标显示超卖，可能存在反弹机会）"
+    #
+    #             # MACD信号调整
+    #             if macd_signal == 'bullish' and action in ['hold', 'cautious_hold']:
+    #                 action = 'cautious_buy'
+    #                 if not technical_adjustment:
+    #                     technical_adjustment = "（MACD显示买入信号）"
+    #             elif macd_signal == 'bearish' and action in ['cautious_buy', 'buy']:
+    #                 action = 'hold'
+    #                 if not technical_adjustment:
+    #                     technical_adjustment = "（MACD显示卖出信号）"
+    #
+    #         # 5. 根据调整后的action转换为最终建议
+    #         action_to_recommendation = {
+    #             'strong_buy': '强烈建议买入',
+    #             'buy': '建议买入',
+    #             'cautious_buy': '谨慎买入',
+    #             'hold': '持观望态度',
+    #             'cautious_hold': '谨慎持有',
+    #             'reduce': '建议减仓',
+    #             'sell': '建议卖出'
+    #         }
+    #
+    #         final_recommendation = action_to_recommendation.get(action, base_recommendation)
+    #
+    #         # 6. 组合所有调整因素
+    #         adjustments = " ".join(filter(None, [market_adjustment, sentiment_adjustment, technical_adjustment]))
+    #
+    #         if adjustments:
+    #             return f"{final_recommendation} {adjustments}"
+    #         else:
+    #             return final_recommendation
+    #
+    #     except Exception as e:
+    #         self.logger.error(f"生成投资建议时出错: {str(e)}")
+    #         # 出错时返回安全的默认建议
+    #         return "无法提供明确建议，请结合多种因素谨慎决策"
+
+    # 原有API：使用 OpenAI 替代 Gemini
+    # def get_ai_analysis(self, df, stock_code):
+    #     """使用AI进行分析"""
+    #     try:
+    #         import openai
+    #         import threading
+    #         import queue
+
+    #         # 设置API密钥和基础URL
+    #         openai.api_key = self.openai_api_key
+    #         openai.api_base = self.openai_api_url
+
+    #         recent_data = df.tail(14).to_dict('records')
+    #         technical_summary = {
+    #             'trend': 'upward' if df.iloc[-1]['MA5'] > df.iloc[-1]['MA20'] else 'downward',
+    #             'volatility': f"{df.iloc[-1]['Volatility']:.2f}%",
+    #             'volume_trend': 'increasing' if df.iloc[-1]['Volume_Ratio'] > 1 else 'decreasing',
+    #             'rsi_level': df.iloc[-1]['RSI']
+    #         }
+
+    #         prompt = f"""分析股票{stock_code}：
+    # 技术指标概要：{technical_summary}
+    # 近14日交易数据：{recent_data}
+
+    # 请提供：
+    # 1.趋势分析（包含支撑位和压力位）
+    # 2.成交量分析及其含义
+    # 3.风险评估（包含波动率分析）
+    # 4.短期和中期目标价位
+    # 5.关键技术位分析
+    # 6.具体交易建议（包含止损位）
+
+    # 请基于技术指标和市场动态进行分析,给出具体数据支持。"""
+
+    #         messages = [{"role": "user", "content": prompt}]
+
+    #         # 使用线程和队列添加超时控制
+    #         result_queue = queue.Queue()
+
+    #         def call_api():
+    #             try:
+    #                 response = openai.ChatCompletion.create(
+    #                     model=self.openai_model,
+    #                     messages=messages,
+    #                     temperature=1,
+    #                     max_tokens=4000,
+    #                     stream = False
+    #                 )
+    #                 result_queue.put(response)
+    #             except Exception as e:
+    #                 result_queue.put(e)
+
+    #         # 启动API调用线程
+    #         api_thread = threading.Thread(target=call_api)
+    #         api_thread.daemon = True
+    #         api_thread.start()
+
+    #         # 等待结果，最多等待20秒
+    #         try:
+    #             result = result_queue.get(timeout=20)
+
+    #             # 检查结果是否为异常
+    #             if isinstance(result, Exception):
+    #                 raise result
+
+    #             # 提取助理回复
+    #             assistant_reply = result["choices"][0]["message"]["content"].strip()
+    #             return assistant_reply
+
+    #         except queue.Empty:
+    #             return "AI分析超时，无法获取分析结果。请稍后再试。"
+    #         except Exception as e:
+    #             return f"AI分析过程中发生错误: {str(e)}"
+
+    #     except Exception as e:
+    #         self.logger.error(f"AI分析发生错误: {str(e)}")
+    #         return "AI分析过程中发生错误，请稍后再试"
+
+    def get_ai_analysis(self, df, stock_code, market_type='A'):
+        """
+        使用AI进行增强分析
+        结合技术指标、实时新闻和行业信息
+
+        参数:
+            df: 股票历史数据DataFrame
+            stock_code: 股票代码
+            market_type: 市场类型(A/HK/US)
+
+        返回:
+            AI生成的分析报告文本
+        """
+        try:
+            import openai
+            import threading
+            import queue
+
+            # 设置API密钥和基础URL
+            openai.api_key = self.openai_api_key
+            openai.api_base = self.openai_api_url
+
+            # 1. 获取最近K线数据
+            recent_data = df.tail(20).to_dict('records')
+
+            # 2. 计算技术指标摘要
+            technical_summary = {
+                'trend': 'upward' if df.iloc[-1]['MA5'] > df.iloc[-1]['MA20'] else 'downward',
+                'volatility': f"{df.iloc[-1]['Volatility']:.2f}%",
+                'volume_trend': 'increasing' if df.iloc[-1]['Volume_Ratio'] > 1 else 'decreasing',
+                'rsi_level': df.iloc[-1]['RSI'],
+                'macd_signal': 'bullish' if df.iloc[-1]['MACD'] > df.iloc[-1]['Signal'] else 'bearish',
+                'bb_position': self._calculate_bb_position(df)
+            }
+
+            # 3. 获取支撑压力位
+            sr_levels = self.identify_support_resistance(df)
+
+            # 4. 获取股票基本信息
+            stock_info = self.get_stock_info(stock_code)
+            stock_name = stock_info.get('股票名称', '未知')
+            industry = stock_info.get('行业', '未知')
+
+            # 5. 获取相关新闻和实时信息 - 整合get_stock_news
+            self.logger.info(f"获取 {stock_code} 的相关新闻和市场信息")
+            news_data = self.get_stock_news(stock_code, market_type)
+
+            # 6. 评分分解
+            score = self.calculate_score(df, market_type)
+            score_details = getattr(self, 'score_details', {'total': score})
+
+            # 7. 获取投资建议
+            # 传递技术指标和新闻数据给get_recommendation函数
+            tech_data = {
+                'RSI': technical_summary['rsi_level'],
+                'MACD_signal': technical_summary['macd_signal'],
+                'Volatility': df.iloc[-1]['Volatility']
+            }
+            recommendation = self.get_recommendation(score, market_type, tech_data, news_data)
+
+            # 8. 构建更全面的prompt
+            prompt = f"""作为专业的股票分析师，请对{stock_name}({stock_code})进行全面分析:
+
+    1. 基本信息:
+       - 股票名称: {stock_name}
+       - 股票代码: {stock_code}
+       - 行业: {industry}
+       - 市场类型: {"A股" if market_type == 'A' else "港股" if market_type == 'HK' else "美股"}
+
+    2. 技术指标摘要:
+       - 趋势: {technical_summary['trend']}
+       - 波动率: {technical_summary['volatility']}
+       - 成交量趋势: {technical_summary['volume_trend']}
+       - RSI: {technical_summary['rsi_level']:.2f}
+       - MACD信号: {technical_summary['macd_signal']}
+       - 布林带位置: {technical_summary['bb_position']}
+
+    3. 支撑与压力位:
+       - 短期支撑位: {', '.join([str(level) for level in sr_levels['support_levels']['short_term']])}
+       - 中期支撑位: {', '.join([str(level) for level in sr_levels['support_levels']['medium_term']])}
+       - 短期压力位: {', '.join([str(level) for level in sr_levels['resistance_levels']['short_term']])}
+       - 中期压力位: {', '.join([str(level) for level in sr_levels['resistance_levels']['medium_term']])}
+
+    4. 综合评分: {score_details['total']}分
+       - 趋势评分: {score_details.get('trend', 0)}
+       - 波动率评分: {score_details.get('volatility', 0)}
+       - 技术指标评分: {score_details.get('technical', 0)}
+       - 成交量评分: {score_details.get('volume', 0)}
+       - 动量评分: {score_details.get('momentum', 0)}
+
+    5. 投资建议: {recommendation}"""
+
+            # 检查是否有JSON解析失败的情况
+            if hasattr(self, 'json_match_flag') and not self.json_match_flag and 'original_content' in news_data:
+                # 如果JSON解析失败，直接使用原始内容
+                prompt += f"""
+
+    6. 相关新闻和市场信息:
+    {news_data.get('original_content', '无法获取相关新闻')}
+    """
+            else:
+                # 正常情况下使用格式化的新闻数据
+                prompt += f"""
+
+    6. 近期相关新闻:
+    {self._format_news_for_prompt(news_data.get('news', []))}
+
+    7. 公司公告:
+    {self._format_announcements_for_prompt(news_data.get('announcements', []))}
+
+    8. 行业动态:
+    {self._format_news_for_prompt(news_data.get('industry_news', []))}
+
+    9. 市场情绪: {news_data.get('market_sentiment', 'neutral')}
+
+    请提供以下内容:
+    1. 技术面分析 - 详细分析价格走势、支撑压力位、主要技术指标的信号
+    2. 行业和市场环境 - 结合新闻和行业动态分析公司所处环境
+    3. 风险因素 - 识别潜在风险点
+    4. 具体交易策略 - 给出明确的买入/卖出建议，包括入场点、止损位和目标价位
+    5. 短期(1周)、中期(1-3个月)和长期(半年)展望
+
+    请基于数据给出客观分析，不要过度乐观或悲观。分析应该包含具体数据和百分比，避免模糊表述。
+    """
+
+            messages = [{"role": "user", "content": prompt}]
+
+            # 使用线程和队列添加超时控制
+            result_queue = queue.Queue()
+
+            def call_api():
+                try:
+                    response = openai.ChatCompletion.create(
+                        model=self.openai_model,
+                        messages=messages,
+                        temperature=0.8,
+                        max_tokens=4000,
+                        stream=False,
+                        timeout=180
+                    )
+                    result_queue.put(response)
+                except Exception as e:
+                    result_queue.put(e)
+
+            # 启动API调用线程
+            api_thread = threading.Thread(target=call_api)
+            api_thread.daemon = True
+            api_thread.start()
+
+            # 等待结果，最多等待240秒
+            try:
+                result = result_queue.get(timeout=240)
+
+                # 检查结果是否为异常
+                if isinstance(result, Exception):
+                    raise result
+
+                # 提取助理回复
+                assistant_reply = result["choices"][0]["message"]["content"].strip()
+                return assistant_reply
+
+            except queue.Empty:
+                return "AI分析超时，无法获取分析结果。请稍后再试。"
+            except Exception as e:
+                return f"AI分析过程中发生错误: {str(e)}"
+
+        except Exception as e:
+            self.logger.error(f"AI分析发生错误: {str(e)}")
+            return f"AI分析过程中发生错误，请稍后再试。错误信息: {str(e)}"
+
+    def _calculate_bb_position(self, df):
+        """计算价格在布林带中的位置"""
+        latest = df.iloc[-1]
+        bb_width = latest['BB_upper'] - latest['BB_lower']
+        if bb_width == 0:
+            return "middle"
+
+        position = (latest['close'] - latest['BB_lower']) / bb_width
+
+        if position < 0.2:
+            return "near lower band (potential oversold)"
+        elif position < 0.4:
+            return "below middle band"
+        elif position < 0.6:
+            return "near middle band"
+        elif position < 0.8:
+            return "above middle band"
+        else:
+            return "near upper band (potential overbought)"
+
+    def _format_news_for_prompt(self, news_list):
+        """格式化新闻列表为prompt字符串"""
+        if not news_list:
+            return "   无最新相关新闻"
+
+        formatted = ""
+        for i, news in enumerate(news_list[:3]):  # 最多显示3条
+            date = news.get('date', '')
+            title = news.get('title', '')
+            source = news.get('source', '')
+            formatted += f"   {i + 1}. [{date}] {title} (来源: {source})\n"
+
+        return formatted
+
+    def _format_announcements_for_prompt(self, announcements):
+        """格式化公告列表为prompt字符串"""
+        if not announcements:
+            return "   无最新公告"
+
+        formatted = ""
+        for i, ann in enumerate(announcements[:3]):  # 最多显示3条
+            date = ann.get('date', '')
+            title = ann.get('title', '')
+            type_ = ann.get('type', '')
+            formatted += f"   {i + 1}. [{date}] {title} (类型: {type_})\n"
+
+        return formatted
+
+    # 原有API：保持接口不变
+    def analyze_stock(self, stock_code, market_type='A'):
+        """分析单个股票"""
+        try:
+            # self.clear_cache(stock_code, market_type)
+            # 获取股票数据
+            df = self.get_stock_data(stock_code, market_type)
+            self.logger.info(f"获取股票数据完成")
+            # 计算技术指标
+            df = self.calculate_indicators(df)
+            self.logger.info(f"计算技术指标完成")
+            # 评分系统
+            score = self.calculate_score(df)
+            self.logger.info(f"评分系统完成")
+            # 获取最新数据
+            latest = df.iloc[-1]
+            prev = df.iloc[-2]
+
+            # 获取基本信息
+            stock_info = self.get_stock_info(stock_code)
+            stock_name = stock_info.get('股票名称', '未知')
+            industry = stock_info.get('行业', '未知')
+
+            # 生成报告（保持原有格式）
+            report = {
+                'stock_code': stock_code,
+                'stock_name': stock_name,
+                'industry': industry,
+                'analysis_date': datetime.now().strftime('%Y-%m-%d'),
+                'score': score,
+                'price': latest['close'],
+                'price_change': (latest['close'] - prev['close']) / prev['close'] * 100,
+                'ma_trend': 'UP' if latest['MA5'] > latest['MA20'] else 'DOWN',
+                'rsi': latest['RSI'],
+                'macd_signal': 'BUY' if latest['MACD'] > latest['Signal'] else 'SELL',
+                'volume_status': '放量' if latest['Volume_Ratio'] > 1.5 else '平量',
+                'recommendation': self.get_recommendation(score),
+                'ai_analysis': self.get_ai_analysis(df, stock_code)
+            }
+
+            return report
+
+        except Exception as e:
+            self.logger.error(f"分析股票时出错: {str(e)}")
+            raise
+
+    # 原有API：保持接口不变
+    def scan_market(self, stock_list, min_score=60, market_type='A'):
+        """扫描市场，寻找符合条件的股票"""
+        recommendations = []
+        total_stocks = len(stock_list)
+
+        self.logger.info(f"开始市场扫描，共 {total_stocks} 只股票")
+        start_time = time.time()
+        processed = 0
+
+        # 批量处理，减少日志输出
+        batch_size = 10
+        for i in range(0, total_stocks, batch_size):
+            batch = stock_list[i:i + batch_size]
+            batch_results = []
+
+            for stock_code in batch:
+                try:
+                    # 使用简化版分析以加快速度
+                    report = self.quick_analyze_stock(stock_code, market_type)
+                    if report['score'] >= min_score:
+                        batch_results.append(report)
+                except Exception as e:
+                    self.logger.error(f"分析股票 {stock_code} 时出错: {str(e)}")
+                    continue
+
+            # 添加批处理结果
+            recommendations.extend(batch_results)
+
+            # 更新处理进度
+            processed += len(batch)
+            elapsed = time.time() - start_time
+            remaining = (elapsed / processed) * (total_stocks - processed) if processed > 0 else 0
+
+            self.logger.info(
+                f"已处理 {processed}/{total_stocks} 只股票，耗时 {elapsed:.1f}秒，预计剩余 {remaining:.1f}秒")
+
+        # 按得分排序
+        recommendations.sort(key=lambda x: x['score'], reverse=True)
+
+        total_time = time.time() - start_time
+        self.logger.info(
+            f"市场扫描完成，共分析 {total_stocks} 只股票，找到 {len(recommendations)} 只符合条件的股票，总耗时 {total_time:.1f}秒")
+
+        return recommendations
+
+    # def quick_analyze_stock(self, stock_code, market_type='A'):
+    #     """快速分析股票，用于市场扫描"""
+    #     try:
+    #         # 获取股票数据
+    #         df = self.get_stock_data(stock_code, market_type)
+
+    #         # 计算技术指标
+    #         df = self.calculate_indicators(df)
+
+    #         # 简化评分计算
+    #         score = self.calculate_score(df)
+
+    #         # 获取最新数据
+    #         latest = df.iloc[-1]
+    #         prev = df.iloc[-2] if len(df) > 1 else latest
+
+    #         # 尝试获取股票名称和行业
+    #         try:
+    #             stock_info = self.get_stock_info(stock_code)
+    #             stock_name = stock_info.get('股票名称', '未知')
+    #             industry = stock_info.get('行业', '未知')
+    #         except:
+    #             stock_name = '未知'
+    #             industry = '未知'
+
+    #         # 生成简化报告
+    #         report = {
+    #             'stock_code': stock_code,
+    #             'stock_name': stock_name,
+    #             'industry': industry,
+    #             'analysis_date': datetime.now().strftime('%Y-%m-%d'),
+    #             'score': score,
+    #             'price': float(latest['close']),
+    #             'price_change': float((latest['close'] - prev['close']) / prev['close'] * 100),
+    #             'ma_trend': 'UP' if latest['MA5'] > latest['MA20'] else 'DOWN',
+    #             'rsi': float(latest['RSI']),
+    #             'macd_signal': 'BUY' if latest['MACD'] > latest['Signal'] else 'SELL',
+    #             'volume_status': '放量' if latest['Volume_Ratio'] > 1.5 else '平量',
+    #             'recommendation': self.get_recommendation(score)
+    #         }
+
+    #         return report
+    #     except Exception as e:
+    #         self.logger.error(f"快速分析股票 {stock_code} 时出错: {str(e)}")
+    #         raise
+
+    def quick_analyze_stock(self, stock_code, market_type='A'):
+        """快速分析股票，用于市场扫描"""
+        try:
+            # 获取股票数据
+            df = self.get_stock_data(stock_code, market_type)
+
+            # 计算技术指标
+            df = self.calculate_indicators(df)
+
+            # 简化评分计算
+            score = self.calculate_score(df)
+
+            # 获取最新数据
+            latest = df.iloc[-1]
+            prev = df.iloc[-2] if len(df) > 1 else latest
+
+            # 先获取股票信息再生成报告
+            try:
+                stock_info = self.get_stock_info(stock_code)
+                stock_name = stock_info.get('股票名称', '未知')
+                industry = stock_info.get('行业', '未知')
+
+                # 添加日志
+                self.logger.info(f"股票 {stock_code} 信息: 名称={stock_name}, 行业={industry}")
+            except Exception as e:
+                self.logger.error(f"获取股票 {stock_code} 信息时出错: {str(e)}")
+                stock_name = '未知'
+                industry = '未知'
+
+            # 生成简化报告
+            report = {
+                'stock_code': stock_code,
+                'stock_name': stock_name,
+                'industry': industry,
+                'analysis_date': datetime.now().strftime('%Y-%m-%d'),
+                'score': score,
+                'price': float(latest['close']),
+                'price_change': float((latest['close'] - prev['close']) / prev['close'] * 100),
+                'ma_trend': 'UP' if latest['MA5'] > latest['MA20'] else 'DOWN',
+                'rsi': float(latest['RSI']),
+                'macd_signal': 'BUY' if latest['MACD'] > latest['Signal'] else 'SELL',
+                'volume_status': 'HIGH' if latest['Volume_Ratio'] > 1.5 else 'NORMAL',
+                'recommendation': self.get_recommendation(score)
+            }
+
+            return report
+        except Exception as e:
+            self.logger.error(f"快速分析股票 {stock_code} 时出错: {str(e)}")
+            raise
+
+    # ======================== 新增功能 ========================#
+
+    def get_stock_info(self, stock_code):
+        """获取股票基本信息"""
+        import akshare as ak
+
+        cache_key = f"{stock_code}_info"
+        if cache_key in self.data_cache:
+            return self.data_cache[cache_key]
+
+        try:
+            # 获取A股股票基本信息
+            stock_info = ak.stock_individual_info_em(symbol=stock_code)
+
+            # 修改：使用列名而不是索引访问数据
+            info_dict = {}
+            for _, row in stock_info.iterrows():
+                # 使用iloc安全地获取数据
+                if len(row) >= 2:  # 确保有至少两列
+                    info_dict[row.iloc[0]] = row.iloc[1]
+
+            # 获取股票名称
+            try:
+                stock_name = ak.stock_info_a_code_name()
+
+                # 检查数据框是否包含预期的列
+                if '代码' in stock_name.columns and '名称' in stock_name.columns:
+                    # 尝试找到匹配的股票代码
+                    matched_stocks = stock_name[stock_name['代码'] == stock_code]
+                    if not matched_stocks.empty:
+                        name = matched_stocks['名称'].values[0]
+                    else:
+                        self.logger.warning(f"未找到股票代码 {stock_code} 的名称信息")
+                        name = "未知"
+                else:
+                    # 尝试使用不同的列名
+                    possible_code_columns = ['代码', 'code', 'symbol', '股票代码', 'stock_code']
+                    possible_name_columns = ['名称', 'name', '股票名称', 'stock_name']
+
+                    code_col = next((col for col in possible_code_columns if col in stock_name.columns), None)
+                    name_col = next((col for col in possible_name_columns if col in stock_name.columns), None)
+
+                    if code_col and name_col:
+                        matched_stocks = stock_name[stock_name[code_col] == stock_code]
+                        if not matched_stocks.empty:
+                            name = matched_stocks[name_col].values[0]
+                        else:
+                            name = "未知"
+                    else:
+                        self.logger.warning(f"股票信息DataFrame结构不符合预期: {stock_name.columns.tolist()}")
+                        name = "未知"
+            except Exception as e:
+                self.logger.error(f"获取股票名称时出错: {str(e)}")
+                name = "未知"
+
+            info_dict['股票名称'] = name
+
+            # 确保基本字段存在
+            if '行业' not in info_dict:
+                info_dict['行业'] = "未知"
+            if '地区' not in info_dict:
+                info_dict['地区'] = "未知"
+
+            # 增加更多日志来调试问题
+            self.logger.info(f"获取到股票信息: 名称={name}, 行业={info_dict.get('行业', '未知')}")
+
+            self.data_cache[cache_key] = info_dict
+            return info_dict
+        except Exception as e:
+            self.logger.error(f"获取股票信息失败: {str(e)}")
+            return {"股票名称": "未知", "行业": "未知", "地区": "未知"}
+
+    def identify_support_resistance(self, df):
+        """识别支撑位和压力位"""
+        latest_price = df['close'].iloc[-1]
+
+        # 使用布林带作为支撑压力参考
+        support_levels = [df['BB_lower'].iloc[-1]]
+        resistance_levels = [df['BB_upper'].iloc[-1]]
+
+        # 添加主要均线作为支撑压力
+        if latest_price < df['MA5'].iloc[-1]:
+            resistance_levels.append(df['MA5'].iloc[-1])
+        else:
+            support_levels.append(df['MA5'].iloc[-1])
+
+        if latest_price < df['MA20'].iloc[-1]:
+            resistance_levels.append(df['MA20'].iloc[-1])
+        else:
+            support_levels.append(df['MA20'].iloc[-1])
+
+        # 添加整数关口
+        price_digits = len(str(int(latest_price)))
+        base = 10 ** (price_digits - 1)
+
+        lower_integer = math.floor(latest_price / base) * base
+        upper_integer = math.ceil(latest_price / base) * base
+
+        if lower_integer < latest_price:
+            support_levels.append(lower_integer)
+        if upper_integer > latest_price:
+            resistance_levels.append(upper_integer)
+
+        # 排序并格式化
+        support_levels = sorted(set([round(x, 2) for x in support_levels if x < latest_price]), reverse=True)
+        resistance_levels = sorted(set([round(x, 2) for x in resistance_levels if x > latest_price]))
+
+        # 分类为短期和中期
+        short_term_support = support_levels[:1] if support_levels else []
+        medium_term_support = support_levels[1:2] if len(support_levels) > 1 else []
+        short_term_resistance = resistance_levels[:1] if resistance_levels else []
+        medium_term_resistance = resistance_levels[1:2] if len(resistance_levels) > 1 else []
+
+        return {
+            'support_levels': {
+                'short_term': short_term_support,
+                'medium_term': medium_term_support
+            },
+            'resistance_levels': {
+                'short_term': short_term_resistance,
+                'medium_term': medium_term_resistance
+            }
+        }
+
+    def calculate_technical_score(self, df):
+        """计算技术面评分 (0-40分)"""
+        try:
+            score = 0
+            # 确保有足够的数据
+            if len(df) < 2:
+                self.logger.warning("数据不足，无法计算技术面评分")
+                return {'total': 0, 'trend': 0, 'indicators': 0, 'support_resistance': 0, 'volatility_volume': 0}
+
+            latest = df.iloc[-1]
+            prev = df.iloc[-2]  # 获取前一个时间点的数据
+            prev_close = prev['close']
+
+            # 1. 趋势分析 (0-10分)
+            trend_score = 0
+
+            # 均线排列情况
+            if latest['MA5'] > latest['MA20'] > latest['MA60']:  # 多头排列
+                trend_score += 5
+            elif latest['MA5'] < latest['MA20'] < latest['MA60']:  # 空头排列
+                trend_score = 0
+            else:  # 交叉状态
+                if latest['MA5'] > latest['MA20']:
+                    trend_score += 3
+                if latest['MA20'] > latest['MA60']:
+                    trend_score += 2
+
+            # 价格与均线关系
+            if latest['close'] > latest['MA5']:
+                trend_score += 3
+            elif latest['close'] > latest['MA20']:
+                trend_score += 2
+
+            # 限制最大值
+            trend_score = min(trend_score, 10)
+            score += trend_score
+
+            # 2. 技术指标分析 (0-10分)
+            indicator_score = 0
+
+            # RSI
+            if 40 <= latest['RSI'] <= 60:  # 中性
+                indicator_score += 2
+            elif 30 <= latest['RSI'] < 40 or 60 < latest['RSI'] <= 70:  # 边缘区域
+                indicator_score += 4
+            elif latest['RSI'] < 30:  # 超卖
+                indicator_score += 5
+            elif latest['RSI'] > 70:  # 超买
+                indicator_score += 0
+
+            # MACD
+            if latest['MACD'] > latest['Signal']:  # MACD金叉或在零轴上方
+                indicator_score += 3
+            else:
+                # 修复：比较当前和前一个时间点的MACD柱状图值
+                if latest['MACD_hist'] > prev['MACD_hist']:  # 柱状图上升
+                    indicator_score += 1
+
+            # 限制最大值和最小值
+            indicator_score = max(0, min(indicator_score, 10))
+            score += indicator_score
+
+            # 3. 支撑压力位分析 (0-10分)
+            sr_score = 0
+
+            # 识别支撑位和压力位
+            middle_price = latest['close']
+            upper_band = latest['BB_upper']
+            lower_band = latest['BB_lower']
+
+            # 距离布林带上下轨的距离
+            upper_distance = (upper_band - middle_price) / middle_price * 100
+            lower_distance = (middle_price - lower_band) / middle_price * 100
+
+            if lower_distance < 2:  # 接近下轨
+                sr_score += 5
+            elif lower_distance < 5:
+                sr_score += 3
+
+            if upper_distance > 5:  # 距上轨较远
+                sr_score += 5
+            elif upper_distance > 2:
+                sr_score += 2
+
+            # 限制最大值
+            sr_score = min(sr_score, 10)
+            score += sr_score
+
+            # 4. 波动性和成交量分析 (0-10分)
+            vol_score = 0
+
+            # 波动率分析
+            if latest['Volatility'] < 2:  # 低波动率
+                vol_score += 3
+            elif latest['Volatility'] < 4:  # 中等波动率
+                vol_score += 2
+
+            # 成交量分析
+            if 'Volume_Ratio' in df.columns:
+                if latest['Volume_Ratio'] > 1.5 and latest['close'] > prev_close:  # 放量上涨
+                    vol_score += 4
+                elif latest['Volume_Ratio'] < 0.8 and latest['close'] < prev_close:  # 缩量下跌
+                    vol_score += 3
+                elif latest['Volume_Ratio'] > 1 and latest['close'] > prev_close:  # 普通放量上涨
+                    vol_score += 2
+
+            # 限制最大值
+            vol_score = min(vol_score, 10)
+            score += vol_score
+
+            # 保存各个维度的分数
+            technical_scores = {
+                'total': score,
+                'trend': trend_score,
+                'indicators': indicator_score,
+                'support_resistance': sr_score,
+                'volatility_volume': vol_score
+            }
+
+            return technical_scores
+
+        except Exception as e:
+            self.logger.error(f"计算技术面评分时出错: {str(e)}")
+            self.logger.error(f"错误详情: {traceback.format_exc()}")
+            return {'total': 0, 'trend': 0, 'indicators': 0, 'support_resistance': 0, 'volatility_volume': 0}
+
+    def perform_enhanced_analysis(self, stock_code, market_type='A'):
+        """执行增强版分析"""
+        try:
+            # 记录开始时间，便于性能分析
+            start_time = time.time()
+            self.logger.info(f"开始执行股票 {stock_code} 的增强分析")
+
+            # 获取股票数据
+            df = self.get_stock_data(stock_code, market_type)
+            data_time = time.time()
+            self.logger.info(f"获取股票数据耗时: {data_time - start_time:.2f}秒")
+
+            # 计算技术指标
+            df = self.calculate_indicators(df)
+            indicator_time = time.time()
+            self.logger.info(f"计算技术指标耗时: {indicator_time - data_time:.2f}秒")
+
+            # 获取最新数据
+            latest = df.iloc[-1]
+            prev = df.iloc[-2] if len(df) > 1 else latest
+
+            # 获取支撑压力位
+            sr_levels = self.identify_support_resistance(df)
+
+            # 计算技术面评分
+            technical_score = self.calculate_technical_score(df)
+
+            # 获取股票信息
+            stock_info = self.get_stock_info(stock_code)
+
+            # 确保technical_score包含必要的字段
+            if 'total' not in technical_score:
+                technical_score['total'] = 0
+
+            # 生成增强版报告
+            enhanced_report = {
+                'basic_info': {
+                    'stock_code': stock_code,
+                    'stock_name': stock_info.get('股票名称', '未知'),
+                    'industry': stock_info.get('行业', '未知'),
+                    'analysis_date': datetime.now().strftime('%Y-%m-%d')
+                },
+                'price_data': {
+                    'current_price': float(latest['close']),  # 确保是Python原生类型
+                    'price_change': float((latest['close'] - prev['close']) / prev['close'] * 100),
+                    'price_change_value': float(latest['close'] - prev['close'])
+                },
+                'technical_analysis': {
+                    'trend': {
+                        'ma_trend': 'UP' if latest['MA5'] > latest['MA20'] else 'DOWN',
+                        'ma_status': "多头排列" if latest['MA5'] > latest['MA20'] > latest['MA60'] else
+                        "空头排列" if latest['MA5'] < latest['MA20'] < latest['MA60'] else
+                        "交叉状态",
+                        'ma_values': {
+                            'ma5': float(latest['MA5']),
+                            'ma20': float(latest['MA20']),
+                            'ma60': float(latest['MA60'])
+                        }
+                    },
+                    'indicators': {
+                        # 确保所有指标都存在并是原生类型
+                        'rsi': float(latest['RSI']) if 'RSI' in latest else 50.0,
+                        'macd': float(latest['MACD']) if 'MACD' in latest else 0.0,
+                        'macd_signal': float(latest['Signal']) if 'Signal' in latest else 0.0,
+                        'macd_histogram': float(latest['MACD_hist']) if 'MACD_hist' in latest else 0.0,
+                        'volatility': float(latest['Volatility']) if 'Volatility' in latest else 0.0
+                    },
+                    'volume': {
+                        'current_volume': float(latest['volume']) if 'volume' in latest else 0.0,
+                        'volume_ratio': float(latest['Volume_Ratio']) if 'Volume_Ratio' in latest else 1.0,
+                        'volume_status': '放量' if 'Volume_Ratio' in latest and latest['Volume_Ratio'] > 1.5 else '平量'
+                    },
+                    'support_resistance': sr_levels
+                },
+                'scores': technical_score,
+                'recommendation': {
+                    'action': self.get_recommendation(technical_score['total']),
+                    'key_points': []
+                },
+                'ai_analysis': self.get_ai_analysis(df, stock_code)
+            }
+
+            # 最后检查并修复报告结构
+            self._validate_and_fix_report(enhanced_report)
+
+            # 在函数结束时记录总耗时
+            end_time = time.time()
+            self.logger.info(f"执行增强分析总耗时: {end_time - start_time:.2f}秒")
+
+            return enhanced_report
+
+        except Exception as e:
+            self.logger.error(f"执行增强版分析时出错: {str(e)}")
+            self.logger.error(traceback.format_exc())
+
+            # 返回基础错误报告
+            return {
+                'basic_info': {
+                    'stock_code': stock_code,
+                    'stock_name': '分析失败',
+                    'industry': '未知',
+                    'analysis_date': datetime.now().strftime('%Y-%m-%d')
+                },
+                'price_data': {
+                    'current_price': 0.0,
+                    'price_change': 0.0,
+                    'price_change_value': 0.0
+                },
+                'technical_analysis': {
+                    'trend': {
+                        'ma_trend': 'UNKNOWN',
+                        'ma_status': '未知',
+                        'ma_values': {'ma5': 0.0, 'ma20': 0.0, 'ma60': 0.0}
+                    },
+                    'indicators': {
+                        'rsi': 50.0,
+                        'macd': 0.0,
+                        'macd_signal': 0.0,
+                        'macd_histogram': 0.0,
+                        'volatility': 0.0
+                    },
+                    'volume': {
+                        'current_volume': 0.0,
+                        'volume_ratio': 0.0,
+                        'volume_status': 'NORMAL'
+                    },
+                    'support_resistance': {
+                        'support_levels': {'short_term': [], 'medium_term': []},
+                        'resistance_levels': {'short_term': [], 'medium_term': []}
+                    }
+                },
+                'scores': {'total': 0},
+                'recommendation': {'action': '分析出错，无法提供建议'},
+                'ai_analysis': f"分析过程中出错: {str(e)}"
+            }
+
+            return error_report
+
+    # 添加一个辅助方法确保报告结构完整
+    def _validate_and_fix_report(self, report):
+        """确保分析报告结构完整"""
+        # 检查必要的顶级字段
+        required_sections = ['basic_info', 'price_data', 'technical_analysis', 'scores', 'recommendation',
+                             'ai_analysis']
+        for section in required_sections:
+            if section not in report:
+                self.logger.warning(f"报告缺少 {section} 部分，添加空对象")
+                report[section] = {}
+
+        # 检查technical_analysis的结构
+        if 'technical_analysis' in report:
+            tech = report['technical_analysis']
+            if not isinstance(tech, dict):
+                report['technical_analysis'] = {}
+                tech = report['technical_analysis']
+
+            # 检查indicators部分
+            if 'indicators' not in tech or not isinstance(tech['indicators'], dict):
+                tech['indicators'] = {
+                    'rsi': 50.0,
+                    'macd': 0.0,
+                    'macd_signal': 0.0,
+                    'macd_histogram': 0.0,
+                    'volatility': 0.0
+                }
+
+            # 转换所有指标为原生Python类型
+            for key, value in tech['indicators'].items():
+                try:
+                    tech['indicators'][key] = float(value)
+                except (TypeError, ValueError):
+                    tech['indicators'][key] = 0.0

stock_qa.py

@@ -0,0 +1,125 @@
+# -*- coding: utf-8 -*-
+"""
+智能分析系统（股票） - 股票市场数据分析系统
+开发者：熊猫大侠
+版本：v2.1.0
+许可证：MIT License
+"""
+# stock_qa.py
+import os
+import openai
+
+class StockQA:
+    def __init__(self, analyzer, openai_api_key=None, openai_model=None):
+        self.analyzer = analyzer
+        self.openai_api_key = os.getenv('OPENAI_API_KEY', os.getenv('OPENAI_API_KEY'))
+        self.openai_api_url = os.getenv('OPENAI_API_URL', 'https://api.openai.com/v1')
+        self.openai_model = os.getenv('OPENAI_API_MODEL', 'gemini-2.0-pro-exp-02-05')
+
+    def answer_question(self, stock_code, question, market_type='A'):
+        """回答关于股票的问题"""
+        try:
+            if not self.openai_api_key:
+                return {"error": "未配置API密钥，无法使用智能问答功能"}
+
+            # 获取股票信息
+            stock_info = self.analyzer.get_stock_info(stock_code)
+
+            # 获取技术指标数据
+            df = self.analyzer.get_stock_data(stock_code, market_type)
+            df = self.analyzer.calculate_indicators(df)
+
+            # 提取最新数据
+            latest = df.iloc[-1]
+
+            # 计算评分
+            score = self.analyzer.calculate_score(df)
+
+            # 获取支撑压力位
+            sr_levels = self.analyzer.identify_support_resistance(df)
+
+            # 构建上下文
+            context = f"""股票信息:
+- 代码: {stock_code}
+- 名称: {stock_info.get('股票名称', '未知')}
+- 行业: {stock_info.get('行业', '未知')}
+
+技术指标(最新数据):
+- 价格: {latest['close']}
+- 5日均线: {latest['MA5']}
+- 20日均线: {latest['MA20']}
+- 60日均线: {latest['MA60']}
+- RSI: {latest['RSI']}
+- MACD: {latest['MACD']}
+- MACD信号线: {latest['Signal']}
+- 布林带上轨: {latest['BB_upper']}
+- 布林带中轨: {latest['BB_middle']}
+- 布林带下轨: {latest['BB_lower']}
+- 波动率: {latest['Volatility']}%
+
+技术评分: {score}分
+
+支撑位:
+- 短期: {', '.join([str(level) for level in sr_levels['support_levels']['short_term']])}
+- 中期: {', '.join([str(level) for level in sr_levels['support_levels']['medium_term']])}
+
+压力位:
+- 短期: {', '.join([str(level) for level in sr_levels['resistance_levels']['short_term']])}
+- 中期: {', '.join([str(level) for level in sr_levels['resistance_levels']['medium_term']])}"""
+
+            # 特定问题类型的补充信息
+            if '基本面' in question or '财务' in question or '估值' in question:
+                try:
+                    # 导入基本面分析器
+                    from fundamental_analyzer import FundamentalAnalyzer
+                    fundamental = FundamentalAnalyzer()
+
+                    # 获取基本面数据
+                    indicators = fundamental.get_financial_indicators(stock_code)
+
+                    # 添加到上下文
+                    context += f"""
+
+基本面指标:
+- PE(TTM): {indicators.get('pe_ttm', '未知')}
+- PB: {indicators.get('pb', '未知')}
+- ROE: {indicators.get('roe', '未知')}%
+- 毛利率: {indicators.get('gross_margin', '未知')}%
+- 净利率: {indicators.get('net_profit_margin', '未知')}%"""
+                except:
+                    context += "\n\n注意：未能获取基本面数据"
+
+            # 调用AI API回答问题
+            openai.api_key = self.openai_api_key
+            openai.api_base = self.openai_api_url
+
+            system_content = """你是专业的股票分析师助手，基于'时空共振交易体系'提供分析。
+            请基于技术指标和市场数据进行客观分析。
+            """
+
+            response = openai.ChatCompletion.create(
+                model=self.openai_model,
+                messages=[
+                    {"role": "system", "content": system_content},
+                    {"role": "user",
+                     "content": f"请回答关于股票的问题，并参考以下股票数据：\n\n{context}\n\n问题：{question}"}
+                ],
+                temperature=0.7
+            )
+
+            answer = response.choices[0].message.content
+
+            return {
+                "question": question,
+                "answer": answer,
+                "stock_code": stock_code,
+                "stock_name": stock_info.get('股票名称', '未知')
+            }
+
+        except Exception as e:
+            print(f"智能问答出错: {str(e)}")
+            return {
+                "question": question,
+                "answer": f"抱歉，回答问题时出错: {str(e)}",
+                "stock_code": stock_code
+            }

templates/capital_flow.html

@@ -0,0 +1,775 @@
+{% extends "layout.html" %}
+
+{% block title %}资金流向 - 智能分析系统{% endblock %}
+
+{% block content %}
+<div class="container-fluid py-3">
+    <div id="alerts-container"></div>
+
+    <div class="row mb-3">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header py-2">
+                    <h5 class="mb-0">资金流向分析</h5>
+                </div>
+                <div class="card-body py-2">
+                    <form id="capital-flow-form" class="row g-2">
+                        <div class="col-md-3">
+                            <div class="input-group input-group-sm">
+                                <span class="input-group-text">数据类型</span>
+                                <select class="form-select" id="data-type">
+                                    <option value="concept" selected>概念资金流</option>
+                                    <option value="individual" >个股资金流</option>
+                                </select>
+                            </div>
+                        </div>
+                        <div class="col-md-3">
+                            <div class="input-group input-group-sm">
+                                <span class="input-group-text">周期</span>
+                                <select class="form-select" id="period-select">
+                                    <option value="10日排行" selected>10日排行</option>
+                                    <option value="5日排行">5日排行</option>
+                                    <option value="3日排行">3日排行</option>
+                                </select>
+                            </div>
+                        </div>
+                        <div class="col-md-4 stock-input" style="display: none;">
+                            <div class="input-group input-group-sm">
+                                <span class="input-group-text">股票代码</span>
+                                <input type="text" class="form-control" id="stock-code" placeholder="例如: 600519">
+                            </div>
+                        </div>
+                        <div class="col-md-2">
+                            <button type="submit" class="btn btn-primary btn-sm w-100">
+                                <i class="fas fa-search"></i> 查询
+                            </button>
+                        </div>
+                    </form>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <!-- Loading Panel -->
+    <div id="loading-panel" class="text-center py-5" style="display: none;">
+        <div class="spinner-border text-primary" role="status">
+            <span class="visually-hidden">Loading...</span>
+        </div>
+        <p class="mt-3 mb-0">正在获取资金流向数据...</p>
+    </div>
+
+    <!-- Concept Fund Flow Panel -->
+    <div id="concept-flow-panel" class="row g-3 mb-3" style="display: none;">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header py-2 d-flex justify-content-between align-items-center">
+                    <h5 class="mb-0">概念资金流向</h5>
+                    <span id="concept-period-badge" class="badge bg-primary">10日排行</span>
+                </div>
+                <div class="card-body">
+                    <div class="table-responsive">
+                        <table class="table table-sm table-striped table-hover">
+                            <thead>
+                                <tr>
+                                    <th>序号</th>
+                                    <th>概念/行业</th>
+                                    <th>行业指数</th>
+                                    <th>涨跌幅</th>
+                                    <th>流入资金(亿)</th>
+                                    <th>流出资金(亿)</th>
+                                    <th>净额(亿)</th>
+                                    <th>公司家数</th>
+                                    <th>操作</th>
+                                </tr>
+                            </thead>
+                            <tbody id="concept-flow-table">
+                                <!-- 资金流向数据将在JS中填充 -->
+                            </tbody>
+                        </table>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <!-- Concept Stocks Panel -->
+    <div id="concept-stocks-panel" class="row g-3 mb-3" style="display: none;">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header py-2">
+                    <h5 id="concept-stocks-title" class="mb-0">概念成分股</h5>
+                </div>
+                <div class="card-body">
+                    <div class="table-responsive">
+                        <table class="table table-sm table-striped table-hover">
+                            <thead>
+                                <tr>
+                                    <th>代码</th>
+                                    <th>名称</th>
+                                    <th>最新价</th>
+                                    <th>涨跌幅</th>
+                                    <th>主力净流入</th>
+                                    <th>主力净流入占比</th>
+                                    <th>操作</th>
+                                </tr>
+                            </thead>
+                            <tbody id="concept-stocks-table">
+                                <!-- 概念成分股数据将在JS中填充 -->
+                            </tbody>
+                        </table>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <!-- Individual Fund Flow Panel -->
+    <div id="individual-flow-panel" class="row g-3 mb-3" style="display: none;">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header py-2 d-flex justify-content-between align-items-center">
+                    <h5 id="individual-flow-title" class="mb-0">个股资金流向</h5>
+                    <span id="individual-period-badge" class="badge bg-primary">10日排行</span>
+                </div>
+                <div class="card-body">
+                    <div class="row">
+                        <div class="col-md-6">
+                            <h6>资金流向概览</h6>
+                            <table class="table table-sm">
+                                <tbody id="individual-flow-summary">
+                                    <!-- 个股资金流向概览将在JS中填充 -->
+                                </tbody>
+                            </table>
+                        </div>
+                        <div class="col-md-6">
+                            <h6>资金流入占比</h6>
+                            <div id="fund-flow-pie-chart" style="height: 200px;"></div>
+                        </div>
+                    </div>
+                    <div class="row mt-3">
+                        <div class="col-12">
+                            <h6>资金流向历史</h6>
+                            <div id="fund-flow-history-chart" style="height: 300px;"></div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <!-- Individual Fund Flow Rank Panel -->
+    <div id="individual-rank-panel" class="row g-3 mb-3" style="display: none;">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header py-2 d-flex justify-content-between align-items-center">
+                    <h5 class="mb-0">个股资金流向排名</h5>
+                    <span id="individual-rank-period-badge" class="badge bg-primary">10日排行</span>
+                </div>
+                <div class="card-body">
+                    <div class="table-responsive">
+                        <table class="table table-sm table-striped table-hover">
+                            <thead>
+                                <tr>
+                                    <th>序号</th>
+                                    <th>代码</th>
+                                    <th>名称</th>
+                                    <th>最新价</th>
+                                    <th>涨跌幅</th>
+                                    <th>主力净流入</th>
+                                    <th>主力净流入占比</th>
+                                    <th>超大单净流入</th>
+                                    <th>大单净流入</th>
+                                    <th>中单净流入</th>
+                                    <th>小单净流入</th>
+                                    <th>操作</th>
+                                </tr>
+                            </thead>
+                            <tbody id="individual-rank-table">
+                                <!-- 个股资金流向排名数据将在JS中填充 -->
+                            </tbody>
+                        </table>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+</div>
+{% endblock %}
+
+{% block scripts %}
+<script>
+    $(document).ready(function() {
+        // 默认加载概念资金流向
+        loadConceptFundFlow('90日排行');
+
+        // 表单提交事件
+        $('#capital-flow-form').submit(function(e) {
+            e.preventDefault();
+            const dataType = $('#data-type').val();
+            const period = $('#period-select').val();
+            const stockCode = $('#stock-code').val().trim();
+
+            if (dataType === 'concept') {
+                loadConceptFundFlow(period);
+            } else if (dataType === 'individual') {
+                if (stockCode) {
+                    loadIndividualFundFlow(stockCode);
+                } else {
+                    loadIndividualFundFlowRank(period);
+                }
+            }
+        });
+
+        // 数据类型切换事件
+        $('#data-type').change(function() {
+            const dataType = $(this).val();
+            if (dataType === 'individual') {
+                $('.stock-input').show();
+            } else {
+                $('.stock-input').hide();
+            }
+        });
+    });
+
+    // 加载概念资金流向
+    function loadConceptFundFlow(period) {
+        $('#loading-panel').show();
+        $('#concept-flow-panel, #concept-stocks-panel, #individual-flow-panel, #individual-rank-panel').hide();
+
+        $.ajax({
+            url: `/api/concept_fund_flow?period=${period}`,
+            type: 'GET',
+            success: function(response) {
+                renderConceptFundFlow(response, period);
+                $('#loading-panel').hide();
+                $('#concept-flow-panel').show();
+            },
+            error: function(xhr, status, error) {
+                $('#loading-panel').hide();
+                showError('获取概念资金流向数据失败: ' + error);
+            }
+        });
+    }
+
+    // 加载概念成分股
+    function loadConceptStocks(sector) {
+        $('#loading-panel').show();
+        $('#concept-stocks-panel').hide();
+
+        $.ajax({
+            url: `/api/sector_stocks?sector=${encodeURIComponent(sector)}`,
+            type: 'GET',
+            success: function(response) {
+                renderConceptStocks(response, sector);
+                $('#loading-panel').hide();
+                $('#concept-stocks-panel').show();
+            },
+            error: function(xhr, status, error) {
+                $('#loading-panel').hide();
+                showError('获取概念成分股数据失败: ' + error);
+            }
+        });
+    }
+
+    // 加载个股资金流向
+    function loadIndividualFundFlow(stockCode) {
+        $('#loading-panel').show();
+        $('#concept-flow-panel, #concept-stocks-panel, #individual-flow-panel, #individual-rank-panel').hide();
+
+        $.ajax({
+            url: `/api/individual_fund_flow?stock_code=${stockCode}`,
+            type: 'GET',
+            success: function(response) {
+                renderIndividualFundFlow(response);
+                $('#loading-panel').hide();
+                $('#individual-flow-panel').show();
+            },
+            error: function(xhr, status, error) {
+                $('#loading-panel').hide();
+                showError('获取个股资金流向数据失败: ' + error);
+            }
+        });
+    }
+
+    // 加载个股资金流向排名
+    function loadIndividualFundFlowRank(period) {
+        $('#loading-panel').show();
+        $('#concept-flow-panel, #concept-stocks-panel, #individual-flow-panel, #individual-rank-panel').hide();
+
+        $.ajax({
+            url: `/api/individual_fund_flow_rank?period=${period}`,
+            type: 'GET',
+            success: function(response) {
+                renderIndividualFundFlowRank(response, period);
+                $('#loading-panel').hide();
+                $('#individual-rank-panel').show();
+            },
+            error: function(xhr, status, error) {
+                $('#loading-panel').hide();
+                showError('获取个股资金流向排名数据失败: ' + error);
+            }
+        });
+    }
+
+    // 渲染概念资金流向
+    function renderConceptFundFlow(data, period) {
+        $('#concept-period-badge').text(period);
+
+        let html = '';
+        if (!data || data.length === 0) {
+            html = '<tr><td colspan="9" class="text-center">暂无数据</td></tr>';
+        } else {
+            data.forEach((item, index) => {
+                const changeClass = parseFloat(item.change_percent) >= 0 ? 'trend-up' : 'trend-down';
+                const changeIcon = parseFloat(item.change_percent) >= 0 ? '<i class="fas fa-caret-up"></i>' : '<i class="fas fa-caret-down"></i>';
+
+                const netFlowClass = parseFloat(item.net_flow) >= 0 ? 'trend-up' : 'trend-down';
+                const netFlowIcon = parseFloat(item.net_flow) >= 0 ? '<i class="fas fa-caret-up"></i>' : '<i class="fas fa-caret-down"></i>';
+
+                html += `
+                    <tr>
+                        <td>${item.rank}</td>
+                        <td><a href="javascript:void(0)" onclick="loadConceptStocks('${item.sector}')">${item.sector}</a></td>
+                        <td>${formatNumber(item.sector_index, 2)}</td>
+                        <td class="${changeClass}">${changeIcon} ${formatPercent(item.change_percent)}</td>
+                        <td>${formatNumber(item.inflow, 2)}</td>
+                        <td>${formatNumber(item.outflow, 2)}</td>
+                        <td class="${netFlowClass}">${netFlowIcon} ${formatNumber(item.net_flow, 2)}</td>
+                        <td>${item.company_count}</td>
+                        <td>
+                            <button class="btn btn-sm btn-outline-primary" onclick="loadConceptStocks('${item.sector}')">
+                                <i class="fas fa-search"></i>
+                            </button>
+                        </td>
+                    </tr>
+                `;
+            });
+        }
+
+        $('#concept-flow-table').html(html);
+    }
+
+    // 渲染概念成分股
+    function renderConceptStocks(data, sector) {
+        $('#concept-stocks-title').text(`${sector} 成分股`);
+
+        let html = '';
+        if (!data || data.length === 0) {
+            html = '<tr><td colspan="7" class="text-center">暂无数据</td></tr>';
+        } else {
+            data.forEach((item) => {
+                const changeClass = parseFloat(item.change_percent) >= 0 ? 'trend-up' : 'trend-down';
+                const changeIcon = parseFloat(item.change_percent) >= 0 ? '<i class="fas fa-caret-up"></i>' : '<i class="fas fa-caret-down"></i>';
+
+                const netFlowClass = parseFloat(item.main_net_inflow) >= 0 ? 'trend-up' : 'trend-down';
+                const netFlowIcon = parseFloat(item.main_net_inflow) >= 0 ? '<i class="fas fa-caret-up"></i>' : '<i class="fas fa-caret-down"></i>';
+
+                html += `
+                    <tr>
+                        <td>${item.code}</td>
+                        <td>${item.name}</td>
+                        <td>${formatNumber(item.price, 2)}</td>
+                        <td class="${changeClass}">${changeIcon} ${formatPercent(item.change_percent)}</td>
+                        <td class="${netFlowClass}">${netFlowIcon} ${formatMoney(item.main_net_inflow)}</td>
+                        <td class="${netFlowClass}">${formatPercent(item.main_net_inflow_percent)}</td>
+                        <td>
+                            <a href="/stock_detail/${item.code}" class="btn btn-sm btn-outline-primary">
+                                <i class="fas fa-chart-line"></i>
+                            </a>
+                            <button class="btn btn-sm btn-outline-info" onclick="loadIndividualFundFlow('${item.code}')">
+                                <i class="fas fa-money-bill-wave"></i>
+                            </button>
+                        </td>
+                    </tr>
+                `;
+            });
+        }
+
+        $('#concept-stocks-table').html(html);
+    }
+
+    // 渲染个股资金流向
+    function renderIndividualFundFlow(data) {
+        if (!data || !data.data || data.data.length === 0) {
+            showError('未获取到有效的个股资金流向数据');
+            return;
+        }
+
+        // Sort data by date (descending - newest first)
+        data.data.sort((a, b) => {
+            // Parse dates to ensure proper comparison
+            let dateA = new Date(a.date);
+            let dateB = new Date(b.date);
+            return dateB - dateA;
+        });
+
+        // Re-calculate summary for 90 days instead of relying on backend calculation
+        recalculateSummary(data, 90);
+
+        // 设置标题
+        $('#individual-flow-title').text(`${data.stock_code} 资金流向`);
+
+        // 渲染概览
+        renderIndividualFlowSummary(data);
+
+        // 渲染资金流入占比饼图
+        renderFundFlowPieChart(data);
+
+        // 渲染资金流向历史图表
+        renderFundFlowHistoryChart(data);
+    }
+
+    function recalculateSummary(data, days) {
+        // Get recent data (up to the specified number of days)
+        const recent_data = data.data.slice(0, Math.min(days, data.data.length));
+
+        // Calculate summary statistics
+        const total_main_net_inflow = recent_data.reduce((sum, item) => sum + item.main_net_inflow, 0);
+        const avg_main_net_inflow_percent = recent_data.reduce((sum, item) => sum + item.main_net_inflow_percent, 0) / recent_data.length;
+        const positive_days = recent_data.filter(item => item.main_net_inflow > 0).length;
+        const negative_days = recent_data.length - positive_days;
+
+        // Create or update summary object
+        data.summary = {
+            recent_days: recent_data.length,
+            total_main_net_inflow: total_main_net_inflow,
+            avg_main_net_inflow_percent: avg_main_net_inflow_percent,
+            positive_days: positive_days,
+            negative_days: negative_days
+        };
+    }
+
+    // 渲染个股资金流向概览
+    function renderIndividualFlowSummary(data) {
+        if (!data.summary) return;
+
+        const summary = data.summary;
+        // Now using the first item after sorting
+        const recent = data.data[0]; // 最近一天的数据
+
+        let html = `
+            <tr>
+                <td>最新日期：</td>
+                <td>${recent.date}</td>
+                <td>最新价：</td>
+                <td>${formatNumber(recent.price, 2)}</td>
+            </tr>
+            <tr>
+                <td>涨跌幅：</td>
+                <td class="${recent.change_percent >= 0 ? 'trend-up' : 'trend-down'}">
+                    ${recent.change_percent >= 0 ? '↑' : '↓'} ${formatPercent(recent.change_percent)}
+                </td>
+                <td>分析周期：</td>
+                <td>${summary.recent_days}天</td>
+            </tr>
+            <tr>
+                <td>主力净流入：</td>
+                <td class="${summary.total_main_net_inflow >= 0 ? 'trend-up' : 'trend-down'}">
+                    ${summary.total_main_net_inflow >= 0 ? '↑' : '↓'} ${formatMoney(summary.total_main_net_inflow)}
+                </td>
+                <td>净流入占比：</td>
+                <td class="${summary.avg_main_net_inflow_percent >= 0 ? 'trend-up' : 'trend-down'}">
+                    ${summary.avg_main_net_inflow_percent >= 0 ? '↑' : '↓'} ${formatPercent(summary.avg_main_net_inflow_percent)}
+                </td>
+            </tr>
+            <tr>
+                <td>资金流入天数：</td>
+                <td>${summary.positive_days}天</td>
+                <td>资金流出天数：</td>
+                <td>${summary.negative_days}天</td>
+            </tr>
+        `;
+
+        $('#individual-flow-summary').html(html);
+    }
+
+    // 渲染资金流入占比饼图
+    function renderFundFlowPieChart(data) {
+        if (!data.data || data.data.length === 0) return;
+
+        // Using the first item after sorting
+        const recent = data.data[0]; // 最近一天的数据
+
+        // 计算资金流入总额（绝对值）
+        const totalInflow = Math.abs(recent.super_large_net_inflow) +
+                            Math.abs(recent.large_net_inflow) +
+                            Math.abs(recent.medium_net_inflow) +
+                            Math.abs(recent.small_net_inflow);
+
+        // 计算各类型占比
+        const superLargePct = Math.abs(recent.super_large_net_inflow) / totalInflow * 100;
+        const largePct = Math.abs(recent.large_net_inflow) / totalInflow * 100;
+        const mediumPct = Math.abs(recent.medium_net_inflow) / totalInflow * 100;
+        const smallPct = Math.abs(recent.small_net_inflow) / totalInflow * 100;
+
+        const options = {
+            series: [superLargePct, largePct, mediumPct, smallPct],
+            chart: {
+                type: 'pie',
+                height: 200
+            },
+            labels: ['超大单', '大单', '中单', '小单'],
+            colors: ['#0d6efd', '#198754', '#ffc107', '#dc3545'],
+            legend: {
+                position: 'bottom'
+            },
+            tooltip: {
+                y: {
+                    formatter: function(value) {
+                        return value.toFixed(2) + '%';
+                    }
+                }
+            }
+        };
+
+        // 清除旧图表
+        $('#fund-flow-pie-chart').empty();
+
+        const chart = new ApexCharts(document.querySelector("#fund-flow-pie-chart"), options);
+        chart.render();
+    }
+
+    // 渲染资金流向历史图表
+    function renderFundFlowHistoryChart(data) {
+        if (!data.data || data.data.length === 0) return;
+
+        // 最近90天的数据
+        // Since we've already sorted the data, just get the first 90 and reverse for chronological display
+        const historyData = data.data.slice(0, 90).reverse();
+
+        const dates = historyData.map(item => item.date);
+        const mainNetInflow = historyData.map(item => item.main_net_inflow);
+        const superLargeInflow = historyData.map(item => item.super_large_net_inflow);
+        const largeInflow = historyData.map(item => item.large_net_inflow);
+        const mediumInflow = historyData.map(item => item.medium_net_inflow);
+        const smallInflow = historyData.map(item => item.small_net_inflow);
+        const priceChanges = historyData.map(item => item.change_percent);
+
+        const options = {
+            series: [
+                {
+                    name: '主力净流入',
+                    type: 'column',
+                    data: mainNetInflow
+                },
+                {
+                    name: '超大单',
+                    type: 'line',
+                    data: superLargeInflow
+                },
+                {
+                    name: '大单',
+                    type: 'line',
+                    data: largeInflow
+                },
+                {
+                    name: '价格涨跌幅',
+                    type: 'line',
+                    data: priceChanges
+                }
+            ],
+            chart: {
+                height: 300,
+                type: 'line',
+                toolbar: {
+                    show: false
+                }
+            },
+            stroke: {
+                width: [0, 2, 2, 2],
+                curve: 'smooth'
+            },
+            plotOptions: {
+                bar: {
+                    columnWidth: '50%'
+                }
+            },
+            colors: ['#0d6efd', '#198754', '#ffc107', '#dc3545'],
+            dataLabels: {
+                enabled: false
+            },
+            labels: dates,
+            xaxis: {
+                type: 'category'
+            },
+            yaxis: [
+                {
+                    title: {
+                        text: '资金流入(亿)',
+                        style: {
+                            fontSize: '12px'
+                        }
+                    },
+                    labels: {
+                        formatter: function(val) {
+                            // Convert to 亿 for display (divide by 100 million)
+                            return (val / 100000000).toFixed(2);
+                        }
+                    }
+                },
+                {
+                    opposite: true,
+                    title: {
+                        text: '价格涨跌幅(%)',
+                        style: {
+                            fontSize: '12px'
+                        }
+                    },
+                    labels: {
+                        formatter: function(val) {
+                            return val.toFixed(2);
+                        }
+                    },
+                    min: -10,
+                    max: 10,
+                    tickAmount: 5
+                }
+            ],
+            tooltip: {
+                shared: true,
+                intersect: false,
+                y: {
+                    formatter: function(y, { seriesIndex }) {
+                        if (seriesIndex === 3) {
+                            return y.toFixed(2) + '%';
+                        }
+                        // Display money values in 亿 (hundred million) units
+                        return (y / 100000000).toFixed(2) + ' 亿';
+                    }
+                }
+            },
+            legend: {
+                position: 'top'
+            }
+        };
+
+        // 清除旧图表
+        $('#fund-flow-history-chart').empty();
+
+        const chart = new ApexCharts(document.querySelector("#fund-flow-history-chart"), options);
+        chart.render();
+    }
+
+    // 渲染个股资金流向排名
+    function renderIndividualFundFlowRank(data, period) {
+        $('#individual-rank-period-badge').text(period);
+
+        let html = '';
+        if (!data || data.length === 0) {
+            html = '<tr><td colspan="12" class="text-center">暂无数据</td></tr>';
+        } else {
+            data.forEach((item) => {
+                const changeClass = parseFloat(item.change_percent) >= 0 ? 'trend-up' : 'trend-down';
+                const changeIcon = parseFloat(item.change_percent) >= 0 ? '<i class="fas fa-caret-up"></i>' : '<i class="fas fa-caret-down"></i>';
+
+                const mainNetClass = parseFloat(item.main_net_inflow) >= 0 ? 'trend-up' : 'trend-down';
+                const mainNetIcon = parseFloat(item.main_net_inflow) >= 0 ? '<i class="fas fa-caret-up"></i>' : '<i class="fas fa-caret-down"></i>';
+
+                html += `
+                    <tr>
+                        <td>${item.rank}</td>
+                        <td>${item.code}</td>
+                        <td>${item.name}</td>
+                        <td>${formatNumber(item.price, 2)}</td>
+                        <td class="${changeClass}">${changeIcon} ${formatPercent(item.change_percent)}</td>
+                        <td class="${mainNetClass}">${mainNetIcon} ${formatMoney(item.main_net_inflow)}</td>
+                        <td class="${mainNetClass}">${formatPercent(item.main_net_inflow_percent)}</td>
+                        <td>${formatMoney(item.super_large_net_inflow)}</td>
+                        <td>${formatMoney(item.large_net_inflow)}</td>
+                        <td>${formatMoney(item.medium_net_inflow)}</td>
+                        <td>${formatMoney(item.small_net_inflow)}</td>
+                        <td>
+                            <a href="/stock_detail/${item.code}" class="btn btn-sm btn-outline-primary">
+                                <i class="fas fa-chart-line"></i>
+                            </a>
+                            <button class="btn btn-sm btn-outline-info" onclick="loadIndividualFundFlow('${item.code}')">
+                                <i class="fas fa-money-bill-wave"></i>
+                            </button>
+                        </td>
+                    </tr>
+                `;
+            });
+        }
+
+        $('#individual-rank-table').html(html);
+    }
+
+    // 格式化资金数字（支持大数字缩写）
+    function formatCompactNumber(num) {
+        if (Math.abs(num) >= 1.0e9) {
+            return (num / 1.0e9).toFixed(2) + "B";
+        } else if (Math.abs(num) >= 1.0e6) {
+            return (num / 1.0e6).toFixed(2) + "M";
+        } else if (Math.abs(num) >= 1.0e3) {
+            return (num / 1.0e3).toFixed(2) + "K";
+        } else {
+            return num.toFixed(2);
+        }
+    }
+
+    // 格式化资金
+    function formatMoney(value) {
+        if (value === null || value === undefined) {
+            return '--';
+        }
+
+        value = parseFloat(value);
+        if (isNaN(value)) {
+            return '--';
+        }
+
+        if (Math.abs(value) >= 1e8) {
+            return (value / 1e8).toFixed(2) + ' 亿';
+        } else if (Math.abs(value) >= 1e4) {
+            return (value / 1e4).toFixed(2) + ' 万';
+        } else {
+            return value.toFixed(2) + ' 元';
+        }
+    }
+
+    // 格式化百分比
+    function formatPercent(value) {
+        if (value === null || value === undefined) {
+            return '--';
+        }
+
+        value = parseFloat(value);
+        if (isNaN(value)) {
+            return '--';
+        }
+
+        return value.toFixed(2) + '%';
+    }
+
+    document.addEventListener('DOMContentLoaded', function() {
+    const dataType = document.getElementById('data-type');
+    const periodSelect = document.getElementById('period-select');
+    const stockInput = document.querySelector('.stock-input');
+
+    // 初始加载时检查默认值
+    toggleOptions();
+
+    dataType.addEventListener('change', toggleOptions);
+
+    function toggleOptions() {
+        if (dataType.value === 'individual') {
+            // 个股资金流选项
+            periodSelect.innerHTML = `
+                <option value="3日">3日</option>
+                <option value="5日">5日</option>
+                <option value="10日">10日</option>
+            `;
+            stockInput.style.display = 'block';
+        } else {
+            // 概念资金流选项
+            periodSelect.innerHTML = `
+                <option value="10日排行" selected>10日排行</option>
+                <option value="5日排行">5日排行</option>
+                <option value="3日排行">3日排行</option>
+            `;
+            stockInput.style.display = 'none';
+        }
+    }
+});
+</script>
+{% endblock %}

templates/dashboard.html

@@ -0,0 +1,604 @@
+{% extends "layout.html" %}
+
+{% block title %}智能仪表盘 - 智能分析系统{% endblock %}
+
+{% block content %}
+<div class="container-fluid py-3">
+    <div id="alerts-container"></div>
+    <div class="row g-3 mb-3">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header py-1">  <!-- 减少padding-top和padding-bottom -->
+                    <h5 class="mb-0">智能股票分析</h5>
+                </div>
+                <div class="card-body py-2">  <!-- 减少padding-top和padding-bottom -->
+                    <form id="analysis-form" class="row g-2">  <!-- 减少间距g-3到g-2 -->
+                        <div class="col-md-4">
+                            <div class="input-group input-group-sm">  <!-- 添加input-group-sm使输入框更小 -->
+                                <span class="input-group-text">股票代码</span>
+                                <input type="text" class="form-control" id="stock-code" placeholder="例如: 600519" required>
+                            </div>
+                        </div>
+                        <div class="col-md-3">
+                            <div class="input-group input-group-sm">  <!-- 添加input-group-sm使下拉框更小 -->
+                                <span class="input-group-text">市场</span>
+                                <select class="form-select" id="market-type">
+                                    <option value="A" selected>A股</option>
+                                    <option value="HK">港股</option>
+                                    <option value="US">美股</option>
+                                </select>
+                            </div>
+                        </div>
+                        <div class="col-md-3">
+                            <div class="input-group input-group-sm">  <!-- 添加input-group-sm使下拉框更小 -->
+                                <span class="input-group-text">周期</span>
+                                <select class="form-select" id="analysis-period">
+                                    <option value="1m">1个月</option>
+                                    <option value="3m">3个月</option>
+                                    <option value="6m">6个月</option>
+                                    <option value="1y" selected>1年</option>
+                                </select>
+                            </div>
+                        </div>
+                        <div class="col-md-2">
+                            <button type="submit" class="btn btn-primary btn-sm w-100">  <!-- 使用btn-sm减小按钮尺寸 -->
+                                <i class="fas fa-chart-line"></i> 分析
+                            </button>
+                        </div>
+                    </form>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <div id="analysis-result" style="display: none;">
+        <div class="row g-3 mb-3">
+            <div class="col-md-6">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">股票概要</h5>
+                    </div>
+                    <div class="card-body">
+                        <div class="row mb-3">
+                            <div class="col-md-7">
+                                <h2 id="stock-name" class="mb-0 fs-4"></h2>
+                                <p id="stock-info" class="text-muted mb-0 small"></p>
+                            </div>
+                            <div class="col-md-5 text-end">
+                                <h3 id="stock-price" class="mb-0 fs-4"></h3>
+                                <p id="price-change" class="mb-0"></p>
+                            </div>
+                        </div>
+                        <div class="row">
+                            <div class="col-md-6">
+                                <div class="mb-2">
+                                    <span class="text-muted small">综合评分:</span>
+                                    <div class="mt-1">
+                                        <span id="total-score" class="badge rounded-pill score-pill"></span>
+                                    </div>
+                                </div>
+                                <div class="mb-2">
+                                    <span class="text-muted small">投资建议:</span>
+                                    <p id="recommendation" class="mb-0 text-strong"></p>
+                                </div>
+                            </div>
+                            <div class="col-md-6">
+                                <div class="mb-2">
+                                    <span class="text-muted small">技术面指标:</span>
+                                    <ul class="list-unstyled mt-1 mb-0 small">
+                                        <li><span class="text-muted">RSI:</span> <span id="rsi-value"></span></li>
+                                        <li><span class="text-muted">MA趋势:</span> <span id="ma-trend"></span></li>
+                                        <li><span class="text-muted">MACD信号:</span> <span id="macd-signal"></span></li>
+                                        <li><span class="text-muted">波动率:</span> <span id="volatility"></span></li>
+                                    </ul>
+                                </div>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+            <div class="col-md-6">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">多维度评分</h5>
+                    </div>
+                    <div class="card-body">
+                        <div id="radar-chart" style="height: 200px;"></div>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <div class="row g-3 mb-3">
+            <div class="col-12">
+                <div class="card">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">价格与技术指标</h5>
+                    </div>
+                    <div class="card-body p-0">
+                        <div id="price-chart" style="height: 400px;"></div>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <div class="row g-3 mb-3">
+            <div class="col-md-4">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">支撑与压力位</h5>
+                    </div>
+                    <div class="card-body">
+                        <table class="table table-sm">
+                            <thead>
+                                <tr>
+                                    <th>类型</th>
+                                    <th>价格</th>
+                                    <th>距离</th>
+                                </tr>
+                            </thead>
+                            <tbody id="support-resistance-table">
+                                <!-- 支撑压力位数据将在JS中动态填充 -->
+                            </tbody>
+                        </table>
+                    </div>
+                </div>
+            </div>
+            <div class="col-md-8">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">AI分析建议</h5>
+                    </div>
+                    <div class="card-body">
+                        <div id="ai-analysis" class="analysis-section">
+                            <!-- AI分析结果将在JS中动态填充 -->
+                            <div class="loading">
+                                <div class="spinner-border text-primary" role="status">
+                                    <span class="visually-hidden">Loading...</span>
+                                </div>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+</div>
+{% endblock %}
+
+{% block scripts %}
+<script>
+    let stockData = [];
+    let analysisResult = null;
+
+    // 提交表单进行分析
+    $('#analysis-form').submit(function(e) {
+        e.preventDefault();
+        const stockCode = $('#stock-code').val().trim();
+        const marketType = $('#market-type').val();
+        const period = $('#analysis-period').val();
+
+        if (!stockCode) {
+            showError('请输入股票代码！');
+            return;
+        }
+
+        // 重定向到股票详情页
+        window.location.href = `/stock_detail/${stockCode}?market_type=${marketType}&period=${period}`;
+    });
+
+    // Format AI analysis text
+    function formatAIAnalysis(text) {
+        if (!text) return '';
+
+        // First, make the text safe for HTML
+        const safeText = text
+            .replace(/&/g, '&amp;')
+            .replace(/</g, '&lt;')
+            .replace(/>/g, '&gt;');
+
+        // Replace basic Markdown elements
+        let formatted = safeText
+            // Bold text with ** or __
+            .replace(/\*\*(.*?)\*\*/g, '<strong class="keyword">$1</strong>')
+            .replace(/__(.*?)__/g, '<strong>$1</strong>')
+
+            // Italic text with * or _
+            .replace(/\*(.*?)\*/g, '<em>$1</em>')
+            .replace(/_(.*?)_/g, '<em>$1</em>')
+
+            // Headers
+            .replace(/^# (.*?)$/gm, '<h4 class="mt-3 mb-2">$1</h4>')
+            .replace(/^## (.*?)$/gm, '<h5 class="mt-2 mb-2">$1</h5>')
+
+            // Apply special styling to financial terms
+            .replace(/支撑位/g, '<span class="keyword">支撑位</span>')
+            .replace(/压力位/g, '<span class="keyword">压力位</span>')
+            .replace(/趋势/g, '<span class="keyword">趋势</span>')
+            .replace(/均线/g, '<span class="keyword">均线</span>')
+            .replace(/MACD/g, '<span class="term">MACD</span>')
+            .replace(/RSI/g, '<span class="term">RSI</span>')
+            .replace(/KDJ/g, '<span class="term">KDJ</span>')
+
+            // Highlight price patterns and movements
+            .replace(/([上涨升])/g, '<span class="trend-up">$1</span>')
+            .replace(/([下跌降])/g, '<span class="trend-down">$1</span>')
+            .replace(/(买入|做多|多头|突破)/g, '<span class="trend-up">$1</span>')
+            .replace(/(卖出|做空|空头|跌破)/g, '<span class="trend-down">$1</span>')
+
+            // Highlight price values (matches patterns like 31.25, 120.50)
+            .replace(/(\d+\.\d{2})/g, '<span class="price">$1</span>')
+
+            // Convert line breaks to paragraph tags
+            .replace(/\n\n+/g, '</p><p class="analysis-para">')
+            .replace(/\n/g, '<br>');
+
+        // Wrap in paragraph tags for consistent styling
+        return '<p class="analysis-para">' + formatted + '</p>';
+    }
+
+    // 获取股票数据
+    function fetchStockData(stockCode, marketType, period) {
+        showLoading();
+
+        $.ajax({
+            url: `/api/stock_data?stock_code=${stockCode}&market_type=${marketType}&period=${period}`,
+            type: 'GET',
+            dataType: 'json',
+            success: function(response) {
+
+                // 检查response是否有data属性
+                if (!response.data) {
+                    hideLoading();
+                    showError('响应格式不正确: 缺少data字段');
+                    return;
+                }
+
+                if (response.data.length === 0) {
+                    hideLoading();
+                    showError('未找到股票数据');
+                    return;
+                }
+
+                stockData = response.data;
+
+                // 获取增强分析数据
+                fetchEnhancedAnalysis(stockCode, marketType);
+            },
+            error: function(xhr, status, error) {
+                hideLoading();
+
+                let errorMsg = '获取股票数据失败';
+                if (xhr.responseJSON && xhr.responseJSON.error) {
+                    errorMsg += ': ' + xhr.responseJSON.error;
+                } else if (error) {
+                    errorMsg += ': ' + error;
+                }
+                showError(errorMsg);
+            }
+        });
+    }
+
+    // 获取增强分析数据
+    function fetchEnhancedAnalysis(stockCode, marketType) {
+
+        $.ajax({
+            url: '/api/enhanced_analysis?_=' + new Date().getTime(),
+            type: 'POST',
+            contentType: 'application/json',
+            data: JSON.stringify({
+                stock_code: stockCode,
+                market_type: marketType
+            }),
+            success: function(response) {
+
+                if (!response.result) {
+                    hideLoading();
+                    showError('增强分析响应格式不正确');
+                    return;
+                }
+
+                analysisResult = response.result;
+                renderAnalysisResult();
+                hideLoading();
+                $('#analysis-result').show();
+            },
+            error: function(xhr, status, error) {
+                hideLoading();
+
+                let errorMsg = '获取分析数据失败';
+                if (xhr.responseJSON && xhr.responseJSON.error) {
+                    errorMsg += ': ' + xhr.responseJSON.error;
+                } else if (error) {
+                    errorMsg += ': ' + error;
+                }
+                showError(errorMsg);
+            }
+        });
+    }
+
+    // 渲染分析结果
+    function renderAnalysisResult() {
+        if (!analysisResult) return;
+
+        // 渲染股票基本信息
+        $('#stock-name').text(analysisResult.basic_info.stock_name + ' (' + analysisResult.basic_info.stock_code + ')');
+        $('#stock-info').text(analysisResult.basic_info.industry + ' | ' + analysisResult.basic_info.analysis_date);
+
+        // 渲染价格信息
+        $('#stock-price').text('¥' + formatNumber(analysisResult.price_data.current_price, 2));
+        const priceChangeClass = analysisResult.price_data.price_change >= 0 ? 'trend-up' : 'trend-down';
+        const priceChangeIcon = analysisResult.price_data.price_change >= 0 ? '<i class="fas fa-caret-up"></i>' : '<i class="fas fa-caret-down"></i>';
+        $('#price-change').html(`<span class="${priceChangeClass}">${priceChangeIcon} ${formatNumber(analysisResult.price_data.price_change_value, 2)} (${formatPercent(analysisResult.price_data.price_change, 2)})</span>`);
+
+        // 渲染评分和建议
+        const scoreClass = getScoreColorClass(analysisResult.scores.total_score);
+        $('#total-score').text(analysisResult.scores.total_score).addClass(scoreClass);
+        $('#recommendation').text(analysisResult.recommendation.action);
+
+        // 渲染技术指标
+        $('#rsi-value').text(formatNumber(analysisResult.technical_analysis.indicators.rsi, 2));
+
+        const maTrendClass = getTrendColorClass(analysisResult.technical_analysis.trend.ma_trend);
+        const maTrendIcon = getTrendIcon(analysisResult.technical_analysis.trend.ma_trend);
+        $('#ma-trend').html(`<span class="${maTrendClass}">${maTrendIcon} ${analysisResult.technical_analysis.trend.ma_status}</span>`);
+
+        const macdSignal = analysisResult.technical_analysis.indicators.macd > analysisResult.technical_analysis.indicators.macd_signal ? 'BUY' : 'SELL';
+        const macdClass = macdSignal === 'BUY' ? 'trend-up' : 'trend-down';
+        const macdIcon = macdSignal === 'BUY' ? '<i class="fas fa-arrow-up"></i>' : '<i class="fas fa-arrow-down"></i>';
+        $('#macd-signal').html(`<span class="${macdClass}">${macdIcon} ${macdSignal}</span>`);
+
+        $('#volatility').text(formatPercent(analysisResult.technical_analysis.indicators.volatility, 2));
+
+        // 渲染支撑压力位
+        let supportResistanceHtml = '';
+
+        // 渲染压力位
+        if (analysisResult.technical_analysis.support_resistance.resistance &&
+            analysisResult.technical_analysis.support_resistance.resistance.short_term &&
+            analysisResult.technical_analysis.support_resistance.resistance.short_term.length > 0) {
+            const resistance = analysisResult.technical_analysis.support_resistance.resistance.short_term[0];
+            const distance = ((resistance - analysisResult.price_data.current_price) / analysisResult.price_data.current_price * 100).toFixed(2);
+            supportResistanceHtml += `
+                <tr>
+                    <td><span class="badge bg-danger">短期压力</span></td>
+                    <td>${formatNumber(resistance, 2)}</td>
+                    <td>+${distance}%</td>
+                </tr>
+            `;
+        }
+
+        if (analysisResult.technical_analysis.support_resistance.resistance &&
+            analysisResult.technical_analysis.support_resistance.resistance.medium_term &&
+            analysisResult.technical_analysis.support_resistance.resistance.medium_term.length > 0) {
+            const resistance = analysisResult.technical_analysis.support_resistance.resistance.medium_term[0];
+            const distance = ((resistance - analysisResult.price_data.current_price) / analysisResult.price_data.current_price * 100).toFixed(2);
+            supportResistanceHtml += `
+                <tr>
+                    <td><span class="badge bg-warning text-dark">中期压力</span></td>
+                    <td>${formatNumber(resistance, 2)}</td>
+                    <td>+${distance}%</td>
+                </tr>
+            `;
+        }
+
+        // 渲染支撑位
+        if (analysisResult.technical_analysis.support_resistance.support &&
+            analysisResult.technical_analysis.support_resistance.support.short_term &&
+            analysisResult.technical_analysis.support_resistance.support.short_term.length > 0) {
+            const support = analysisResult.technical_analysis.support_resistance.support.short_term[0];
+            const distance = ((support - analysisResult.price_data.current_price) / analysisResult.price_data.current_price * 100).toFixed(2);
+            supportResistanceHtml += `
+                <tr>
+                    <td><span class="badge bg-success">短期支撑</span></td>
+                    <td>${formatNumber(support, 2)}</td>
+                    <td>${distance}%</td>
+                </tr>
+            `;
+        }
+
+        if (analysisResult.technical_analysis.support_resistance.support &&
+            analysisResult.technical_analysis.support_resistance.support.medium_term &&
+            analysisResult.technical_analysis.support_resistance.support.medium_term.length > 0) {
+            const support = analysisResult.technical_analysis.support_resistance.support.medium_term[0];
+            const distance = ((support - analysisResult.price_data.current_price) / analysisResult.price_data.current_price * 100).toFixed(2);
+            supportResistanceHtml += `
+                <tr>
+                    <td><span class="badge bg-info">中期支撑</span></td>
+                    <td>${formatNumber(support, 2)}</td>
+                    <td>${distance}%</td>
+                </tr>
+            `;
+        }
+
+        $('#support-resistance-table').html(supportResistanceHtml);
+
+        // 渲染AI分析
+        $('#ai-analysis').html(formatAIAnalysis(analysisResult.ai_analysis));
+
+        // 绘制雷达图
+        renderRadarChart();
+
+        // 绘制价格图表
+        renderPriceChart();
+    }
+
+    // 绘制雷达图
+    function renderRadarChart() {
+        if (!analysisResult) return;
+
+        const options = {
+            series: [{
+                name: '评分',
+                data: [
+                    analysisResult.scores.trend_score || 0,
+                    analysisResult.scores.indicators_score || 0,
+                    analysisResult.scores.support_resistance_score || 0,
+                    analysisResult.scores.volatility_volume_score || 0
+                ]
+            }],
+            chart: {
+                height: 200,
+                type: 'radar',
+                toolbar: {
+                    show: false
+                }
+            },
+            title: {
+                text: '多维度技术分析评分',
+                style: {
+                    fontSize: '14px'
+                }
+            },
+            xaxis: {
+                categories: ['趋势分析', '技术指标', '支撑压力位', '波动与成交量']
+            },
+            yaxis: {
+                max: 10,
+                min: 0
+            },
+            fill: {
+                opacity: 0.5,
+                colors: ['#4e73df']
+            },
+            markers: {
+                size: 4
+            }
+        };
+
+        // 清除旧图表
+        $('#radar-chart').empty();
+
+        const chart = new ApexCharts(document.querySelector("#radar-chart"), options);
+        chart.render();
+    }
+
+    // 绘制价格图表
+    function renderPriceChart() {
+        if (!stockData || stockData.length === 0) return;
+
+        // 准备价格数据
+        const seriesData = [];
+
+        // 添加蜡烛图数据
+        const candleData = stockData.map(item => ({
+            x: new Date(item.date),
+            y: [item.open, item.high, item.low, item.close]
+        }));
+        seriesData.push({
+            name: '价格',
+            type: 'candlestick',
+            data: candleData
+        });
+
+        // 添加均线数据
+        const ma5Data = stockData.map(item => ({
+            x: new Date(item.date),
+            y: item.MA5
+        }));
+        seriesData.push({
+            name: 'MA5',
+            type: 'line',
+            data: ma5Data
+        });
+
+        const ma20Data = stockData.map(item => ({
+            x: new Date(item.date),
+            y: item.MA20
+        }));
+        seriesData.push({
+            name: 'MA20',
+            type: 'line',
+            data: ma20Data
+        });
+
+        const ma60Data = stockData.map(item => ({
+            x: new Date(item.date),
+            y: item.MA60
+        }));
+        seriesData.push({
+            name: 'MA60',
+            type: 'line',
+            data: ma60Data
+        });
+
+        // 创建图表
+        const options = {
+            series: seriesData,
+            chart: {
+                height: 400,
+                type: 'candlestick',
+                toolbar: {
+                    show: true,
+                    tools: {
+                        download: true,
+                        selection: true,
+                        zoom: true,
+                        zoomin: true,
+                        zoomout: true,
+                        pan: true,
+                        reset: true
+                    }
+                }
+            },
+            title: {
+                text: `${analysisResult.basic_info.stock_name} (${analysisResult.basic_info.stock_code}) 价格走势`,
+                align: 'left',
+                style: {
+                    fontSize: '14px'
+                }
+            },
+            xaxis: {
+                type: 'datetime'
+            },
+            yaxis: {
+                tooltip: {
+                    enabled: true
+                },
+                labels: {
+                    formatter: function(value) {
+                        return formatNumber(value, 2);  // 统一使用2位小数
+                    }
+                }
+            },
+            tooltip: {
+                shared: true,
+                custom: [
+                    function({ seriesIndex, dataPointIndex, w }) {
+                        if (seriesIndex === 0) {
+                            const o = w.globals.seriesCandleO[seriesIndex][dataPointIndex];
+                            const h = w.globals.seriesCandleH[seriesIndex][dataPointIndex];
+                            const l = w.globals.seriesCandleL[seriesIndex][dataPointIndex];
+                            const c = w.globals.seriesCandleC[seriesIndex][dataPointIndex];
+
+                            return `
+                                <div class="apexcharts-tooltip-candlestick">
+                                    <div>开盘: <span>${formatNumber(o, 2)}</span></div>
+                                    <div>最高: <span>${formatNumber(h, 2)}</span></div>
+                                    <div>最低: <span>${formatNumber(l, 2)}</span></div>
+                                    <div>收盘: <span>${formatNumber(c, 2)}</span></div>
+                                </div>
+                            `;
+                        }
+                        return '';
+                    }
+                ]
+            },
+            plotOptions: {
+                candlestick: {
+                    colors: {
+                        upward: '#3C90EB',
+                        downward: '#DF7D46'
+                    }
+                }
+            }
+        };
+
+        // 清除旧图表
+        $('#price-chart').empty();
+
+        const chart = new ApexCharts(document.querySelector("#price-chart"), options);
+        chart.render();
+    }
+</script>
+{% endblock %}

templates/error.html

@@ -0,0 +1,30 @@
+{% extends "layout.html" %}
+
+{% block title %}错误 {{ error_code }} - 股票智能分析系统{% endblock %}
+
+{% block content %}
+<div class="container py-5">
+    <div class="row justify-content-center">
+        <div class="col-md-8">
+            <div class="card">
+                <div class="card-header bg-danger text-white">
+                    <h4 class="mb-0">错误 {{ error_code }}</h4>
+                </div>
+                <div class="card-body text-center py-5">
+                    <i class="fas fa-exclamation-triangle fa-5x text-danger mb-4"></i>
+                    <h2>出现错误</h2>
+                    <p class="lead">{{ message }}</p>
+                    <div class="mt-4">
+                        <a href="/" class="btn btn-primary me-2">
+                            <i class="fas fa-home"></i> 返回首页
+                        </a>
+                        <button class="btn btn-outline-secondary" onclick="history.back()">
+                            <i class="fas fa-arrow-left"></i> 返回上一页
+                        </button>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+</div>
+{% endblock %}

templates/fundamental.html

@@ -0,0 +1,405 @@
+{% extends "layout.html" %}
+
+{% block title %}基本面分析 - {{ stock_code }} - 智能分析系统{% endblock %}
+
+{% block content %}
+<div class="container-fluid py-3">
+    <div id="alerts-container"></div>
+
+    <div class="row mb-3">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header py-2">
+                    <h5 class="mb-0">基本面分析</h5>
+                </div>
+                <div class="card-body py-2">
+                    <form id="fundamental-form" class="row g-2">
+                        <div class="col-md-4">
+                            <div class="input-group input-group-sm">
+                                <span class="input-group-text">股票代码</span>
+                                <input type="text" class="form-control" id="stock-code" placeholder="例如: 600519" required>
+                            </div>
+                        </div>
+                        <div class="col-md-3">
+                            <div class="input-group input-group-sm">
+                                <span class="input-group-text">市场</span>
+                                <select class="form-select" id="market-type">
+                                    <option value="A" selected>A股</option>
+                                    <option value="HK">港股</option>
+                                    <option value="US">美股</option>
+                                </select>
+                            </div>
+                        </div>
+                        <div class="col-md-3">
+                            <button type="submit" class="btn btn-primary btn-sm w-100">
+                                <i class="fas fa-search"></i> 分析
+                            </button>
+                        </div>
+                    </form>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <div id="fundamental-result" style="display: none;">
+        <div class="row g-3 mb-3">
+            <div class="col-md-6">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">财务概况</h5>
+                    </div>
+                    <div class="card-body">
+                        <div class="row mb-3">
+                            <div class="col-md-7">
+                                <h3 id="stock-name" class="mb-0 fs-4"></h3>
+                                <p id="stock-info" class="text-muted mb-0 small"></p>
+                            </div>
+                            <div class="col-md-5 text-end">
+                                <span id="fundamental-score" class="badge rounded-pill score-pill"></span>
+                            </div>
+                        </div>
+                        <div class="row">
+                            <div class="col-md-6">
+                                <h6>估值指标</h6>
+                                <ul class="list-unstyled mt-1 mb-0 small">
+                                    <li><span class="text-muted">PE(TTM):</span> <span id="pe-ttm"></span></li>
+                                    <li><span class="text-muted">PB:</span> <span id="pb"></span></li>
+                                    <li><span class="text-muted">PS(TTM):</span> <span id="ps-ttm"></span></li>
+                                </ul>
+                            </div>
+                            <div class="col-md-6">
+                                <h6>盈利能力</h6>
+                                <ul class="list-unstyled mt-1 mb-0 small">
+                                    <li><span class="text-muted">ROE:</span> <span id="roe"></span></li>
+                                    <li><span class="text-muted">毛利率:</span> <span id="gross-margin"></span></li>
+                                    <li><span class="text-muted">净利润率:</span> <span id="net-profit-margin"></span></li>
+                                </ul>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+            <div class="col-md-6">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">成长性分析</h5>
+                    </div>
+                    <div class="card-body">
+                        <div class="row">
+                            <div class="col-md-6">
+                                <h6>营收增长</h6>
+                                <ul class="list-unstyled mt-1 mb-0 small">
+                                    <li><span class="text-muted">3年CAGR:</span> <span id="revenue-growth-3y"></span></li>
+                                    <li><span class="text-muted">5年CAGR:</span> <span id="revenue-growth-5y"></span></li>
+                                </ul>
+                            </div>
+                            <div class="col-md-6">
+                                <h6>利润增长</h6>
+                                <ul class="list-unstyled mt-1 mb-0 small">
+                                    <li><span class="text-muted">3年CAGR:</span> <span id="profit-growth-3y"></span></li>
+                                    <li><span class="text-muted">5年CAGR:</span> <span id="profit-growth-5y"></span></li>
+                                </ul>
+                            </div>
+                        </div>
+                        <div class="mt-3">
+                            <div id="growth-chart" style="height: 150px;"></div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <div class="row g-3 mb-3">
+            <div class="col-md-4">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">估值评分</h5>
+                    </div>
+                    <div class="card-body">
+                        <div id="valuation-chart" style="height: 200px;"></div>
+                        <p id="valuation-comment" class="small text-muted mt-2"></p>
+                    </div>
+                </div>
+            </div>
+            <div class="col-md-4">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">财务健康评分</h5>
+                    </div>
+                    <div class="card-body">
+                        <div id="financial-chart" style="height: 200px;"></div>
+                        <p id="financial-comment" class="small text-muted mt-2"></p>
+                    </div>
+                </div>
+            </div>
+            <div class="col-md-4">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">成长性评分</h5>
+                    </div>
+                    <div class="card-body">
+                        <div id="growth-score-chart" style="height: 200px;"></div>
+                        <p id="growth-comment" class="small text-muted mt-2"></p>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+</div>
+{% endblock %}
+
+{% block scripts %}
+<script>
+    $(document).ready(function() {
+        $('#fundamental-form').submit(function(e) {
+            e.preventDefault();
+            const stockCode = $('#stock-code').val().trim();
+            const marketType = $('#market-type').val();
+
+            if (!stockCode) {
+                showError('请输入股票代码！');
+                return;
+            }
+
+            fetchFundamentalAnalysis(stockCode);
+        });
+    });
+
+    function fetchFundamentalAnalysis(stockCode) {
+        showLoading();
+
+        $.ajax({
+            url: '/api/fundamental_analysis',
+            type: 'POST',
+            contentType: 'application/json',
+            data: JSON.stringify({
+                stock_code: stockCode
+            }),
+            success: function(response) {
+                hideLoading();
+                renderFundamentalAnalysis(response, stockCode);
+                $('#fundamental-result').show();
+            },
+            error: function(xhr, status, error) {
+                hideLoading();
+                let errorMsg = '获取基本面分析失败';
+                if (xhr.responseJSON && xhr.responseJSON.error) {
+                    errorMsg += ': ' + xhr.responseJSON.error;
+                } else if (error) {
+                    errorMsg += ': ' + error;
+                }
+                showError(errorMsg);
+            }
+        });
+    }
+
+    function renderFundamentalAnalysis(data, stockCode) {
+        // 设置基本信息
+        $('#stock-name').text(data.details.indicators.stock_name || stockCode);
+        $('#stock-info').text(data.details.indicators.industry || '未知行业');
+
+        // 设置评分
+        const scoreClass = getScoreColorClass(data.total);
+        $('#fundamental-score').text(data.total).addClass(scoreClass);
+
+        // 设置估值指标
+        $('#pe-ttm').text(formatNumber(data.details.indicators.pe_ttm, 2));
+        $('#pb').text(formatNumber(data.details.indicators.pb, 2));
+        $('#ps-ttm').text(formatNumber(data.details.indicators.ps_ttm, 2));
+
+        // 设置盈利能力
+        $('#roe').text(formatPercent(data.details.indicators.roe, 2));
+        $('#gross-margin').text(formatPercent(data.details.indicators.gross_margin, 2));
+        $('#net-profit-margin').text(formatPercent(data.details.indicators.net_profit_margin, 2));
+
+        // 设置成长率
+        $('#revenue-growth-3y').text(formatPercent(data.details.growth.revenue_growth_3y, 2));
+        $('#revenue-growth-5y').text(formatPercent(data.details.growth.revenue_growth_5y, 2));
+        $('#profit-growth-3y').text(formatPercent(data.details.growth.profit_growth_3y, 2));
+        $('#profit-growth-5y').text(formatPercent(data.details.growth.profit_growth_5y, 2));
+
+        // 评论
+        $('#valuation-comment').text("估值处于行业" + (data.valuation > 20 ? "合理水平" : "偏高水平"));
+        $('#financial-comment').text("财务状况" + (data.financial_health > 30 ? "良好" : "一般"));
+        $('#growth-comment').text("成长性" + (data.growth > 20 ? "较好" : "一般"));
+
+        // 渲染图表
+        renderValuationChart(data.valuation);
+        renderFinancialChart(data.financial_health);
+        renderGrowthScoreChart(data.growth);
+        renderGrowthChart(data.details.growth);
+    }
+
+    function renderValuationChart(score) {
+        const options = {
+            series: [score],
+            chart: {
+                height: 200,
+                type: 'radialBar',
+            },
+            plotOptions: {
+                radialBar: {
+                    hollow: {
+                        size: '70%',
+                    },
+                    dataLabels: {
+                        name: {
+                            fontSize: '22px',
+                        },
+                        value: {
+                            fontSize: '16px',
+                        },
+                        total: {
+                            show: true,
+                            label: '估值',
+                            formatter: function() {
+                                return score;
+                            }
+                        }
+                    }
+                }
+            },
+            colors: ['#1ab7ea'],
+            labels: ['估值'],
+        };
+
+        const chart = new ApexCharts(document.querySelector("#valuation-chart"), options);
+        chart.render();
+    }
+
+    function renderFinancialChart(score) {
+        const options = {
+            series: [score],
+            chart: {
+                height: 200,
+                type: 'radialBar',
+            },
+            plotOptions: {
+                radialBar: {
+                    hollow: {
+                        size: '70%',
+                    },
+                    dataLabels: {
+                        name: {
+                            fontSize: '22px',
+                        },
+                        value: {
+                            fontSize: '16px',
+                        },
+                        total: {
+                            show: true,
+                            label: '财务',
+                            formatter: function() {
+                                return score;
+                            }
+                        }
+                    }
+                }
+            },
+            colors: ['#20E647'],
+            labels: ['财务'],
+        };
+
+        const chart = new ApexCharts(document.querySelector("#financial-chart"), options);
+        chart.render();
+    }
+
+    function renderGrowthScoreChart(score) {
+        const options = {
+            series: [score],
+            chart: {
+                height: 200,
+                type: 'radialBar',
+            },
+            plotOptions: {
+                radialBar: {
+                    hollow: {
+                        size: '70%',
+                    },
+                    dataLabels: {
+                        name: {
+                            fontSize: '22px',
+                        },
+                        value: {
+                            fontSize: '16px',
+                        },
+                        total: {
+                            show: true,
+                            label: '成长',
+                            formatter: function() {
+                                return score;
+                            }
+                        }
+                    }
+                }
+            },
+            colors: ['#F9CE1D'],
+            labels: ['成长'],
+        };
+
+        const chart = new ApexCharts(document.querySelector("#growth-score-chart"), options);
+        chart.render();
+    }
+
+    function renderGrowthChart(growthData) {
+        const options = {
+            series: [{
+                name: '营收增长率',
+                data: [
+                    growthData.revenue_growth_3y || 0,
+                    growthData.revenue_growth_5y || 0
+                ]
+            }, {
+                name: '利润增长率',
+                data: [
+                    growthData.profit_growth_3y || 0,
+                    growthData.profit_growth_5y || 0
+                ]
+            }],
+            chart: {
+                type: 'bar',
+                height: 150,
+                toolbar: {
+                    show: false
+                }
+            },
+            plotOptions: {
+                bar: {
+                    horizontal: false,
+                    columnWidth: '55%',
+                    endingShape: 'rounded'
+                },
+            },
+            dataLabels: {
+                enabled: false
+            },
+            stroke: {
+                show: true,
+                width: 2,
+                colors: ['transparent']
+            },
+            xaxis: {
+                categories: ['3年CAGR', '5年CAGR'],
+            },
+            yaxis: {
+                title: {
+                    text: '百分比 (%)'
+                }
+            },
+            fill: {
+                opacity: 1
+            },
+            tooltip: {
+                y: {
+                    formatter: function(val) {
+                        return val + "%"
+                    }
+                }
+            }
+        };
+
+        const chart = new ApexCharts(document.querySelector("#growth-chart"), options);
+        chart.render();
+    }
+</script>
+{% endblock %}

templates/index.html

@@ -0,0 +1,337 @@
+{% extends "layout.html" %}
+
+{% block title %}首页 - 智能分析系统{% endblock %}
+
+{% block content %}
+<div class="container py-5">
+    <div class="row mb-5">
+        <div class="col-md-8 mx-auto text-center">
+            <h1 class="display-4 mb-4">智能分析系统</h1>
+            <p class="lead mb-4">基于人工智能的多维度股票分析平台，为您提供专业的投资决策支持</p>
+            <div class="d-grid gap-2 d-md-flex justify-content-md-center">
+                <a href="/dashboard" class="btn btn-primary btn-lg px-4 me-md-2">
+                    <i class="fas fa-chart-line"></i> 开始分析
+                </a>
+                <button type="button" class="btn btn-outline-secondary btn-lg px-4" data-bs-toggle="modal" data-bs-target="#quickAnalysisModal">
+                    <i class="fas fa-search"></i> 快速分析
+                </button>
+            </div>
+        </div>
+    </div>
+
+    <div class="row mb-5">
+        <div class="col-md-4 mb-4">
+            <div class="card h-100">
+                <div class="card-body text-center py-5">
+                    <i class="fas fa-chart-pie fa-3x text-primary mb-3"></i>
+                    <h4>多维度分析</h4>
+                    <p class="text-muted">技术面、基本面、资金面多角度评估，全方位解读股票价值</p>
+                </div>
+            </div>
+        </div>
+        <div class="col-md-4 mb-4">
+            <div class="card h-100">
+                <div class="card-body text-center py-5">
+                    <i class="fas fa-robot fa-3x text-primary mb-3"></i>
+                    <h4>AI智能辅助</h4>
+                    <p class="text-muted">基于先进算法的智能分析，提供专业级投资建议和趋势预测</p>
+                </div>
+            </div>
+        </div>
+        <div class="col-md-4 mb-4">
+            <div class="card h-100">
+                <div class="card-body text-center py-5">
+                    <i class="fas fa-briefcase fa-3x text-primary mb-3"></i>
+                    <h4>投资组合管理</h4>
+                    <p class="text-muted">智能构建和评估投资组合，优化资产配置，控制风险</p>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <!-- 替换index.html中的功能列表部分 -->
+    <div class="row mb-5">
+        <div class="col-md-10 mx-auto">
+            <div class="card">
+                <div class="card-header">
+                    <h5 class="mb-0">主要功能</h5>
+                </div>
+                <div class="card-body">
+                    <div class="row">
+                        <div class="col-md-6">
+                            <ul class="list-group list-group-flush">
+                                <li class="list-group-item d-flex align-items-center">
+                                    <i class="fas fa-chart-line text-primary me-3"></i>
+                                    <div>
+                                        <strong>单股智能分析</strong>
+                                        <p class="text-muted mb-0">多维度评估股票表现，提供精准分析</p>
+                                    </div>
+                                </li>
+                                <li class="list-group-item d-flex align-items-center">
+                                    <i class="fas fa-search text-primary me-3"></i>
+                                    <div>
+                                        <strong>市场扫描</strong>
+                                        <p class="text-muted mb-0">筛选高评分股票，发现潜在投资机会</p>
+                                    </div>
+                                </li>
+                                <li class="list-group-item d-flex align-items-center">
+                                    <i class="fas fa-robot text-primary me-3"></i>
+                                    <div>
+                                        <strong>AI智能问答</strong>
+                                        <p class="text-muted mb-0">提供智能分析解读，解答投资疑问</p>
+                                    </div>
+                                </li>
+                                <li class="list-group-item d-flex align-items-center">
+                                    <i class="fas fa-file-invoice-dollar text-success me-3"></i>
+                                    <div>
+                                        <strong>基本面分析</strong>
+                                        <p class="text-muted mb-0">透视公司财务健康和增长潜力</p>
+                                    </div>
+                                </li>
+                                <li class="list-group-item d-flex align-items-center">
+                                    <i class="fas fa-money-bill-wave text-info me-3"></i>
+                                    <div>
+                                        <strong>资金流向分析</strong>
+                                        <p class="text-muted mb-0">跟踪主力资金、北向资金和机构持仓</p>
+                                    </div>
+                                </li>
+                            </ul>
+                        </div>
+                        <div class="col-md-6">
+                            <ul class="list-group list-group-flush">
+                                <li class="list-group-item d-flex align-items-center">
+                                    <i class="fas fa-briefcase text-primary me-3"></i>
+                                    <div>
+                                        <strong>投资组合分析</strong>
+                                        <p class="text-muted mb-0">评估组合表现，提供优化建议</p>
+                                    </div>
+                                </li>
+                                <li class="list-group-item d-flex align-items-center">
+                                    <i class="fas fa-exclamation-triangle text-danger me-3"></i>
+                                    <div>
+                                        <strong>风险预警</strong>
+                                        <p class="text-muted mb-0">监控技术指标，及时预警潜在风险</p>
+                                    </div>
+                                </li>
+                                <li class="list-group-item d-flex align-items-center">
+                                    <i class="fas fa-lightbulb text-warning me-3"></i>
+                                    <div>
+                                        <strong>情景预测</strong>
+                                        <p class="text-muted mb-0">分析多种市场情景，提前布局应对</p>
+                                    </div>
+                                </li>
+                                <li class="list-group-item d-flex align-items-center">
+                                    <i class="fas fa-industry text-secondary me-3"></i>
+                                    <div>
+                                        <strong>行业指数分析</strong>
+                                        <p class="text-muted mb-0">洞察行业格局和板块轮动机会</p>
+                                    </div>
+                                </li>
+                                <li class="list-group-item d-flex align-items-center">
+                                    <i class="fas fa-chart-area text-primary me-3"></i>
+                                    <div>
+                                        <strong>可视化图表</strong>
+                                        <p class="text-muted mb-0">直观展示分析结果，辅助决策</p>
+                                    </div>
+                                </li>
+                            </ul>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <!-- 在index.html的功能卡片部分（现有的卡片之后）添加新卡片 -->
+    <div class="row mb-5">
+        <!-- 保留原有的三个卡片 -->
+        <div class="col-md-4 mb-4">
+            <div class="card h-100">
+                <div class="card-body text-center py-5">
+                    <i class="fas fa-chart-pie fa-3x text-primary mb-3"></i>
+                    <h4>多维度分析</h4>
+                    <p class="text-muted">技术面、基本面、资金面多角度评估，全方位解读股票价值</p>
+                </div>
+            </div>
+        </div>
+        <div class="col-md-4 mb-4">
+            <div class="card h-100">
+                <div class="card-body text-center py-5">
+                    <i class="fas fa-robot fa-3x text-primary mb-3"></i>
+                    <h4>AI智能辅助</h4>
+                    <p class="text-muted">基于先进算法的智能分析，提供专业级投资建议和趋势预测</p>
+                </div>
+            </div>
+        </div>
+        <div class="col-md-4 mb-4">
+            <div class="card h-100">
+                <div class="card-body text-center py-5">
+                    <i class="fas fa-briefcase fa-3x text-primary mb-3"></i>
+                    <h4>投资组合管理</h4>
+                    <p class="text-muted">智能构建和评估投资组合，优化资产配置，控制风险</p>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <!-- 添加新的功能卡片行 -->
+    <div class="row mb-5">
+        <div class="col-md-4 mb-4">
+            <div class="card h-100">
+                <div class="card-body text-center py-5">
+                    <i class="fas fa-file-invoice-dollar fa-3x text-success mb-3"></i>
+                    <h4>基本面透视</h4>
+                    <p class="text-muted">财务指标分析、估值评估、成长性预测，揭示公司内在价值</p>
+                    <a href="/fundamental" class="btn btn-outline-success mt-2">开始分析</a>
+                </div>
+            </div>
+        </div>
+        <div class="col-md-4 mb-4">
+            <div class="card h-100">
+                <div class="card-body text-center py-5">
+                    <i class="fas fa-money-bill-wave fa-3x text-info mb-3"></i>
+                    <h4>资金流向分析</h4>
+                    <p class="text-muted">主力资金、北向资金、机构持仓变化，把握资金动向</p>
+                    <a href="/capital_flow" class="btn btn-outline-info mt-2">查看资金</a>
+                </div>
+            </div>
+        </div>
+        <div class="col-md-4 mb-4">
+            <div class="card h-100">
+                <div class="card-body text-center py-5">
+                    <i class="fas fa-crystal-ball fa-3x text-warning mb-3"></i>
+                    <h4>多情景预测</h4>
+                    <p class="text-muted">乐观、中性、悲观三种市场情景，助您提前布局应对不同市场环境</p>
+                    <a href="/scenario_predict" class="btn btn-outline-warning mt-2">查看预测</a>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <div class="row mb-5">
+        <div class="col-md-4 mb-4">
+            <div class="card h-100">
+                <div class="card-body text-center py-5">
+                    <i class="fas fa-exclamation-triangle fa-3x text-danger mb-3"></i>
+                    <h4>风险监控</h4>
+                    <p class="text-muted">多维度风险预警，帮助您及时发现潜在风险，保护投资安全</p>
+                    <a href="/risk_monitor" class="btn btn-outline-danger mt-2">查看风险</a>
+                </div>
+            </div>
+        </div>
+        <div class="col-md-4 mb-4">
+            <div class="card h-100">
+                <div class="card-body text-center py-5">
+                    <i class="fas fa-question-circle fa-3x text-purple mb-3"></i>
+                    <h4>智能问答</h4>
+                    <p class="text-muted">AI助手随时回答您关于股票的各种问题，专业知识触手可及</p>
+                    <a href="/qa" class="btn btn-outline-primary mt-2">开始提问</a>
+                </div>
+            </div>
+        </div>
+        <div class="col-md-4 mb-4">
+            <div class="card h-100">
+                <div class="card-body text-center py-5">
+                    <i class="fas fa-industry fa-3x text-secondary mb-3"></i>
+                    <h4>行业指数分析</h4>
+                    <p class="text-muted">行业整体分析与板块轮动把握，发现行业机会，洞察市场格局</p>
+                    <a href="/industry_analysis" class="btn btn-outline-secondary mt-2">查看行业</a>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <!-- 替换index.html底部的导航按钮区域 -->
+    <div class="row">
+        <div class="col-md-10 mx-auto text-center">
+            <h4 class="mb-4">开始使用智能分析系统</h4>
+            <div class="d-grid gap-2 d-md-flex justify-content-md-center flex-wrap">
+                <a href="/dashboard" class="btn btn-primary btn-lg px-4 me-md-2 mb-2">
+                    <i class="fas fa-chart-line"></i> 智能仪表盘
+                </a>
+                <a href="/fundamental" class="btn btn-success btn-lg px-4 me-md-2 mb-2">
+                    <i class="fas fa-file-invoice-dollar"></i> 基本面分析
+                </a>
+                <a href="/capital_flow" class="btn btn-info btn-lg px-4 me-md-2 mb-2">
+                    <i class="fas fa-money-bill-wave"></i> 资金流向
+                </a>
+                <a href="/market_scan" class="btn btn-secondary btn-lg px-4 me-md-2 mb-2">
+                    <i class="fas fa-search"></i> 市场扫描
+                </a>
+                <a href="/scenario_predict" class="btn btn-warning btn-lg px-4 me-md-2 mb-2">
+                    <i class="fas fa-crystal-ball"></i> 情景预测
+                </a>
+                <a href="/portfolio" class="btn btn-dark btn-lg px-4 me-md-2 mb-2">
+                    <i class="fas fa-briefcase"></i> 投资组合
+                </a>
+                <a href="/qa" class="btn btn-primary btn-lg px-4 me-md-2 mb-2">
+                    <i class="fas fa-question-circle"></i> 智能问答
+                </a>
+                <a href="/risk_monitor" class="btn btn-danger btn-lg px-4 me-md-2 mb-2">
+                    <i class="fas fa-exclamation-triangle"></i> 风险监控
+                </a>
+            </div>
+        </div>
+    </div>
+</div>
+
+<!-- 快速分析模态框 -->
+<div class="modal fade" id="quickAnalysisModal" tabindex="-1" aria-labelledby="quickAnalysisModalLabel" aria-hidden="true">
+    <div class="modal-dialog">
+        <div class="modal-content">
+            <div class="modal-header">
+                <h5 class="modal-title" id="quickAnalysisModalLabel">快速股票分析</h5>
+                <button type="button" class="btn-close" data-bs-dismiss="modal" aria-label="Close"></button>
+            </div>
+            <div class="modal-body">
+                <form id="quick-analysis-form">
+                    <div class="mb-3">
+                        <label for="quick-stock-code" class="form-label">股票代码</label>
+                        <input type="text" class="form-control" id="quick-stock-code" placeholder="例如: 600519" required>
+                    </div>
+                    <div class="mb-3">
+                        <label for="quick-market-type" class="form-label">市场类型</label>
+                        <select class="form-select" id="quick-market-type">
+                            <option value="A" selected>A股</option>
+                            <option value="HK">港股</option>
+                            <option value="US">美股</option>
+                        </select>
+                    </div>
+                </form>
+            </div>
+            <div class="modal-footer">
+                <button type="button" class="btn btn-secondary" data-bs-dismiss="modal">取消</button>
+                <button type="button" class="btn btn-primary" id="quick-analysis-btn">开始分析</button>
+            </div>
+        </div>
+    </div>
+</div>
+{% endblock %}
+
+{% block scripts %}
+<script>
+    $(document).ready(function() {
+        // 快速分析按钮点击事件
+        $('#quick-analysis-btn').click(function() {
+            const stockCode = $('#quick-stock-code').val().trim();
+            const marketType = $('#quick-market-type').val();
+
+            if (!stockCode) {
+                alert('请输入股票代码');
+                return;
+            }
+
+            // 跳转到股票详情页
+            window.location.href = `/stock_detail/${stockCode}?market_type=${marketType}`;
+        });
+
+        // 回车键提交表单
+        $('#quick-stock-code').keypress(function(e) {
+            if (e.which === 13) {
+                $('#quick-analysis-btn').click();
+                return false;
+            }
+        });
+    });
+</script>
+{% endblock %}

templates/industry_analysis.html

@@ -0,0 +1,1135 @@
+{% extends "layout.html" %}
+
+{% block title %}行业分析 - 智能分析系统{% endblock %}
+
+{% block content %}
+<div class="container-fluid py-3">
+    <div id="alerts-container"></div>
+
+    <div class="row mb-3">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header py-2">
+                    <h5 class="mb-0">行业资金流向分析</h5>
+                </div>
+                <div class="card-body py-2">
+                    <form id="industry-form" class="row g-2">
+                        <div class="col-md-3">
+                            <div class="input-group input-group-sm">
+                                <span class="input-group-text">周期</span>
+                                <select class="form-select" id="fund-flow-period">
+                                    <option value="即时" selected>即时</option>
+                                    <option value="3日排行">3日排行</option>
+                                    <option value="5日排行">5日排行</option>
+                                    <option value="10日排行">10日排行</option>
+                                    <option value="20日排行">20日排行</option>
+                                </select>
+                            </div>
+                        </div>
+                        <div class="col-md-3">
+                            <div class="input-group input-group-sm">
+                                <span class="input-group-text">行业</span>
+                                <select class="form-select" id="industry-selector">
+                                    <option value="">-- 选择行业 --</option>
+                                    <!-- 行业选项将通过JS动态填充 -->
+                                </select>
+                            </div>
+                        </div>
+                        <div class="col-md-2">
+                            <button type="submit" class="btn btn-primary btn-sm w-100">
+                                <i class="fas fa-search"></i> 分析
+                            </button>
+                        </div>
+                    </form>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <div id="loading-panel" class="text-center py-5" style="display: none;">
+        <div class="spinner-border text-primary" role="status">
+            <span class="visually-hidden">Loading...</span>
+        </div>
+        <p class="mt-3 mb-0">正在获取行业数据...</p>
+    </div>
+
+    <!-- 行业资金流向概览 -->
+    <div id="industry-overview" class="row g-3 mb-3" style="display: none;">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header py-2 d-flex justify-content-between align-items-center">
+                    <h5 class="mb-0">行业资金流向概览</h5>
+                    <div class="d-flex">
+                        <span id="period-badge" class="badge bg-primary ms-2">即时</span>
+                        <button class="btn btn-sm btn-outline-primary ms-2" id="export-btn">
+                            <i class="fas fa-download"></i> 导出数据
+                        </button>
+                    </div>
+                </div>
+                <div class="card-body p-0">
+                    <div class="table-responsive">
+                        <table class="table table-sm table-striped table-hover mb-0">
+                            <thead>
+                                <tr>
+                                    <th>序号</th>
+                                    <th>行业</th>
+                                    <th>行业指数</th>
+                                    <th>涨跌幅</th>
+                                    <th>流入资金(亿)</th>
+                                    <th>流出资金(亿)</th>
+                                    <th>净额(亿)</th>
+                                    <th>公司家数</th>
+                                    <th>领涨股</th>
+                                    <th>操作</th>
+                                </tr>
+                            </thead>
+                            <tbody id="industry-table">
+                                <!-- 行业资金流向数据将在JS中动态填充 -->
+                            </tbody>
+                        </table>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <!-- 行业详细分析 -->
+    <div id="industry-detail" class="row g-3 mb-3" style="display: none;">
+        <div class="col-md-6">
+            <div class="card h-100">
+                <div class="card-header py-2">
+                    <h5 id="industry-name" class="mb-0">行业详情</h5>
+                </div>
+                <div class="card-body">
+                    <div class="row">
+                        <div class="col-md-6">
+                            <h6>行业概况</h6>
+                            <p><span class="text-muted">行业指数:</span> <span id="industry-index" class="fw-bold"></span></p>
+                            <p><span class="text-muted">涨跌幅:</span> <span id="industry-change" class="fw-bold"></span></p>
+                            <p><span class="text-muted">公司家数:</span> <span id="industry-company-count" class="fw-bold"></span></p>
+                        </div>
+                        <div class="col-md-6">
+                            <h6>资金流向</h6>
+                            <p><span class="text-muted">流入资金:</span> <span id="industry-inflow" class="fw-bold"></span></p>
+                            <p><span class="text-muted">流出资金:</span> <span id="industry-outflow" class="fw-bold"></span></p>
+                            <p><span class="text-muted">净额:</span> <span id="industry-net-flow" class="fw-bold"></span></p>
+                        </div>
+                    </div>
+                    <div class="mt-3">
+                        <div id="industry-flow-chart" style="height: 200px;"></div>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <div class="col-md-6">
+            <div class="card h-100">
+                <div class="card-header py-2">
+                    <h5 class="mb-0">行业评分</h5>
+                </div>
+                <div class="card-body">
+                    <div class="row">
+                        <div class="col-md-5">
+                            <div id="industry-score-chart" style="height: 150px;"></div>
+                            <h4 id="industry-score" class="text-center mt-2">--</h4>
+                            <p class="text-muted text-center">综合评分</p>
+                        </div>
+                        <div class="col-md-7">
+                            <div class="mb-3">
+                                <div class="d-flex justify-content-between mb-1">
+                                    <span>技术面</span>
+                                    <span id="technical-score">--/40</span>
+                                </div>
+                                <div class="progress">
+                                    <div id="technical-progress" class="progress-bar bg-info" role="progressbar" style="width: 0%"></div>
+                                </div>
+                            </div>
+                            <div class="mb-3">
+                                <div class="d-flex justify-content-between mb-1">
+                                    <span>基本面</span>
+                                    <span id="fundamental-score">--/40</span>
+                                </div>
+                                <div class="progress">
+                                    <div id="fundamental-progress" class="progress-bar bg-success" role="progressbar" style="width: 0%"></div>
+                                </div>
+                            </div>
+                            <div class="mb-3">
+                                <div class="d-flex justify-content-between mb-1">
+                                    <span>资金面</span>
+                                    <span id="capital-flow-score">--/20</span>
+                                </div>
+                                <div class="progress">
+                                    <div id="capital-flow-progress" class="progress-bar bg-warning" role="progressbar" style="width: 0%"></div>
+                                </div>
+                            </div>
+                        </div>
+                    </div>
+                    <div class="mt-3">
+                        <h6>投资建议</h6>
+                        <p id="industry-recommendation" class="mb-0"></p>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <!-- 行业成分股表现 -->
+    <div id="industry-stocks" class="row g-3 mb-3" style="display: none;">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header py-2">
+                    <h5 class="mb-0">行业成分股表现</h5>
+                </div>
+                <div class="card-body p-0">
+                    <div class="table-responsive">
+                        <table class="table table-sm table-striped table-hover mb-0">
+                            <thead>
+                                <tr>
+                                    <th>代码</th>
+                                    <th>名称</th>
+                                    <th>最新价</th>
+                                    <th>涨跌幅</th>
+                                    <th>成交量</th>
+                                    <th>成交额(万)</th>
+                                    <th>换手率</th>
+                                    <th>评分</th>
+                                    <th>操作</th>
+                                </tr>
+                            </thead>
+                            <tbody id="industry-stocks-table">
+                                <!-- 行业成分股数据将在JS中动态填充 -->
+                            </tbody>
+                        </table>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <!-- 行业对比分析 -->
+    <div class="row g-3 mb-3">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header py-2">
+                    <h5 class="mb-0">行业对比分析</h5>
+                </div>
+                <div class="card-body">
+                    <ul class="nav nav-tabs" id="industry-compare-tabs" role="tablist">
+                        <li class="nav-item" role="presentation">
+                            <button class="nav-link active" id="fund-flow-tab" data-bs-toggle="tab" data-bs-target="#fund-flow" type="button" role="tab" aria-controls="fund-flow" aria-selected="true">资金流向</button>
+                        </li>
+                        <li class="nav-item" role="presentation">
+                            <button class="nav-link" id="performance-tab" data-bs-toggle="tab" data-bs-target="#performance" type="button" role="tab" aria-controls="performance" aria-selected="false">行业涨跌幅</button>
+                        </li>
+                    </ul>
+                    <div class="tab-content mt-3" id="industry-compare-tabs-content">
+                        <div class="tab-pane fade show active" id="fund-flow" role="tabpanel" aria-labelledby="fund-flow-tab">
+                            <div class="row">
+                                <div class="col-md-6">
+                                    <h6>资金净流入前10行业</h6>
+                                    <div id="top-inflow-chart" style="height: 300px;"></div>
+                                </div>
+                                <div class="col-md-6">
+                                    <h6>资金净流出前10行业</h6>
+                                    <div id="top-outflow-chart" style="height: 300px;"></div>
+                                </div>
+                            </div>
+                        </div>
+                        <div class="tab-pane fade" id="performance" role="tabpanel" aria-labelledby="performance-tab">
+                            <div class="row">
+                                <div class="col-md-6">
+                                    <h6>涨幅前10行业</h6>
+                                    <div id="top-gainers-chart" style="height: 300px;"></div>
+                                </div>
+                                <div class="col-md-6">
+                                    <h6>跌幅前10行业</h6>
+                                    <div id="top-losers-chart" style="height: 300px;"></div>
+                                </div>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+</div>
+{% endblock %}
+
+{% block scripts %}
+<script>
+
+    $(document).ready(function() {
+    // 加载行业资金流向数据
+    loadIndustryFundFlow();
+
+    $('#industry-form').submit(function(e) {
+        e.preventDefault();
+
+        // 获取选择的行业
+        const industry = $('#industry-selector').val();
+
+        if (!industry) {
+            showError('请选择行业名称');
+            return;
+        }
+
+        // 分析行业
+        loadIndustryDetail(industry);
+    });
+
+    // 资金流向周期切换
+    $('#fund-flow-period').change(function() {
+        const period = $(this).val();
+        loadIndustryFundFlow(period);
+    });
+
+    // 导出按钮点击事件
+    $('#export-btn').click(function() {
+        exportToCSV();
+    });
+    });
+
+    function analyzeIndustry(industry) {
+        $('#loading-panel').show();
+        $('#industry-result').hide();
+
+        // 1. 获取行业详情
+        $.ajax({
+            url: `/api/industry_detail?industry=${encodeURIComponent(industry)}`,
+            type: 'GET',
+            success: function(industryDetail) {
+                console.log("Industry detail loaded successfully:", industryDetail);
+
+                // 2. 获取行业成分股
+                $.ajax({
+                    url: `/api/industry_stocks?industry=${encodeURIComponent(industry)}`,
+                    type: 'GET',
+                    success: function(stocksResponse) {
+                        console.log("Industry stocks loaded successfully:", stocksResponse);
+
+                        $('#loading-panel').hide();
+
+                        // 渲染行业详情和成分股
+                        renderIndustryDetail(industryDetail);
+                        renderIndustryStocks(stocksResponse);
+
+                        $('#industry-detail').show();
+                        $('#industry-stocks').show();
+                    },
+                    error: function(xhr, status, error) {
+                        $('#loading-panel').hide();
+                        console.error("Error loading industry stocks:", error);
+                        showError('获取行业成分股失败: ' + (xhr.responseJSON?.error || error));
+                    }
+                });
+            },
+            error: function(xhr, status, error) {
+                $('#loading-panel').hide();
+                console.error("Error loading industry detail:", error);
+                showError('获取行业详情失败: ' + (xhr.responseJSON?.error || error));
+            }
+        });
+    }
+
+    // 加载行业资金流向数据
+    function loadIndustryFundFlow(period = '即时') {
+        $('#loading-panel').show();
+        $('#industry-overview').hide();
+        $('#industry-detail').hide();
+        $('#industry-stocks').hide();
+
+        $.ajax({
+            url: `/api/industry_fund_flow?symbol=${encodeURIComponent(period)}`,
+            type: 'GET',
+            dataType: 'json',
+            success: function(response) {
+                if (Array.isArray(response) && response.length > 0) {
+                    renderIndustryFundFlow(response, period);
+                    populateIndustrySelector(response);
+
+                    // 加载行业对比数据
+                    loadIndustryCompare();
+
+                    $('#loading-panel').hide();
+                    $('#industry-overview').show();
+                } else {
+                    showError('获取行业资金流向数据失败：返回数据为空');
+                    $('#loading-panel').hide();
+                }
+            },
+            error: function(xhr, status, error) {
+                $('#loading-panel').hide();
+                let errorMsg = '获取行业资金流向数据失败';
+                if (xhr.responseJSON && xhr.responseJSON.error) {
+                    errorMsg += ': ' + xhr.responseJSON.error;
+                } else if (error) {
+                    errorMsg += ': ' + error;
+                }
+                showError(errorMsg);
+            }
+        });
+    }
+
+
+    // 统一的行业详情加载函数
+    function loadIndustryDetail(industry) {
+        console.log(`Loading industry detail for: ${industry}`);
+        $('#loading-panel').show();
+        $('#industry-overview').hide();
+        $('#industry-detail').hide();
+        $('#industry-stocks').hide();
+
+        // 并行加载行业详情和行业成分股
+        $.when(
+            // 获取行业详情
+            $.ajax({
+                url: `/api/industry_detail?industry=${encodeURIComponent(industry)}`,
+                type: 'GET',
+                dataType: 'json'
+            }),
+            // 获取行业成分股
+            $.ajax({
+                url: `/api/industry_stocks?industry=${encodeURIComponent(industry)}`,
+                type: 'GET',
+                dataType: 'json'
+            })
+        ).done(function(detailResponse, stocksResponse) {
+            // 处理行业详情数据
+            const industryData = detailResponse[0];
+
+            // 处理行业成分股数据
+            const stocksData = stocksResponse[0];
+
+            console.log("Industry detail loaded:", industryData);
+            console.log("Industry stocks loaded:", stocksData);
+
+            renderIndustryDetail(industryData);
+            renderIndustryStocks(stocksData);
+
+            $('#loading-panel').hide();
+            $('#industry-detail').show();
+            $('#industry-stocks').show();
+        }).fail(function(jqXHR, textStatus, errorThrown) {
+            $('#loading-panel').hide();
+            console.error("Error loading industry data:", textStatus, errorThrown);
+            let errorMsg = '获取行业数据失败';
+            try {
+                if (jqXHR.responseJSON && jqXHR.responseJSON.error) {
+                    errorMsg += ': ' + jqXHR.responseJSON.error;
+                } else if (errorThrown) {
+                    errorMsg += ': ' + errorThrown;
+                }
+            } catch (e) {
+                console.error("Error parsing error response:", e);
+            }
+            showError(errorMsg);
+        });
+    }
+
+    // 加载行业对比数据
+    function loadIndustryCompare() {
+        $.ajax({
+            url: '/api/industry_compare',
+            type: 'GET',
+            dataType: 'json',
+            success: function(response) {
+                try {
+                    if (response && response.results) {
+                        // 按资金净流入排序
+                        const sortedByNetFlow = [...response.results]
+                            .filter(item => item.netFlow !== undefined)
+                            .sort((a, b) => parseFloat(b.netFlow || 0) - parseFloat(a.netFlow || 0));
+
+                        // 按涨跌幅排序
+                        const sortedByChange = [...response.results]
+                            .filter(item => item.change !== undefined)
+                            .sort((a, b) => parseFloat(b.change || 0) - parseFloat(a.change || 0));
+
+                        // 资金净流入前10行业
+                        const topInflow = sortedByNetFlow.slice(0, 10);
+                        renderBarChart('top-inflow-chart',
+                                    topInflow.map(item => item.industry),
+                                    topInflow.map(item => parseFloat(item.netFlow || 0)),
+                                    '资金净流入(亿)',
+                                    '#00E396');
+
+                        // 资金净流出前10行业
+                        const bottomInflow = [...sortedByNetFlow].reverse().slice(0, 10);
+                        renderBarChart('top-outflow-chart',
+                                    bottomInflow.map(item => item.industry),
+                                    bottomInflow.map(item => Math.abs(parseFloat(item.netFlow || 0))),
+                                    '资金净流出(亿)',
+                                    '#FF4560');
+
+                        // 涨幅前10行业
+                        const topGainers = sortedByChange.slice(0, 10);
+                        renderBarChart('top-gainers-chart',
+                                    topGainers.map(item => item.industry),
+                                    topGainers.map(item => parseFloat(item.change || 0)),
+                                    '涨幅(%)',
+                                    '#00E396');
+
+                        // 跌幅前10行业
+                        const topLosers = [...sortedByChange].reverse().slice(0, 10);
+                        renderBarChart('top-losers-chart',
+                                    topLosers.map(item => item.industry),
+                                    topLosers.map(item => Math.abs(parseFloat(item.change || 0))),
+                                    '跌幅(%)',
+                                    '#FF4560');
+                    }
+                } catch (e) {
+                    console.error("Error processing industry comparison data:", e);
+                }
+            },
+            error: function(xhr, status, error) {
+                console.error('获取行业对比数据失败:', error);
+            }
+        });
+    }
+
+    // 渲染行业资金流向表格
+    function renderIndustryFundFlow(data, period) {
+        $('#period-badge').text(period);
+
+        let html = '';
+        if (data.length === 0) {
+            html = '<tr><td colspan="10" class="text-center">暂无数据</td></tr>';
+        } else {
+            data.forEach((item, index) => {
+                const changeClass = parseFloat(item.change) >= 0 ? 'trend-up' : 'trend-down';
+                const changeIcon = parseFloat(item.change) >= 0 ? '<i class="fas fa-caret-up"></i>' : '<i class="fas fa-caret-down"></i>';
+
+                const netFlowClass = parseFloat(item.netFlow) >= 0 ? 'trend-up' : 'trend-down';
+                const netFlowIcon = parseFloat(item.netFlow) >= 0 ? '<i class="fas fa-caret-up"></i>' : '<i class="fas fa-caret-down"></i>';
+
+                html += `
+                    <tr>
+                        <td>${item.rank}</td>
+                        <td>
+                            <a href="javascript:void(0)" onclick="loadIndustryDetail('${item.industry}')" class="industry-link">
+                                ${item.industry}
+                            </a>
+                        </td>
+                        <td>${formatNumber(item.index, 2)}</td>
+                        <td class="${changeClass}">${changeIcon} ${item.change}%</td>
+                        <td>${formatNumber(item.inflow, 2)}</td>
+                        <td>${formatNumber(item.outflow, 2)}</td>
+                        <td class="${netFlowClass}">${netFlowIcon} ${formatNumber(item.netFlow, 2)}</td>
+                        <td>${item.companyCount}</td>
+                        <td>${item.leadingStock || '-'}</td>
+                        <td>
+                            <button class="btn btn-sm btn-outline-primary" onclick="loadIndustryDetail('${item.industry}')">
+                                <i class="fas fa-search"></i>
+                            </button>
+                        </td>
+                    </tr>
+                `;
+            });
+        }
+
+        $('#industry-table').html(html);
+    }
+
+    // 渲染行业详情
+    function renderIndustryDetail(data) {
+        if (!data) {
+            console.error("renderIndustryDetail: No data provided");
+            return;
+        }
+
+        console.log("Rendering industry detail:", data);
+
+        // 设置基本信息
+        $('#industry-name').text(data.industry);
+
+        // 设置行业评分
+        const scoreClass = getScoreColorClass(data.score);
+        $('#industry-score').text(data.score).removeClass().addClass(scoreClass);
+
+        // 设置技术面、基本面、资金面分数 (模拟分数)
+        const technicalScore = Math.round(data.score * 0.4);
+        const fundamentalScore = Math.round(data.score * 0.4);
+        const capitalFlowScore = Math.round(data.score * 0.2);
+
+        $('#technical-score').text(`${technicalScore}/40`);
+        $('#fundamental-score').text(`${fundamentalScore}/40`);
+        $('#capital-flow-score').text(`${capitalFlowScore}/20`);
+
+        $('#technical-progress').css('width', `${technicalScore / 40 * 100}%`);
+        $('#fundamental-progress').css('width', `${fundamentalScore / 40 * 100}%`);
+        $('#capital-flow-progress').css('width', `${capitalFlowScore / 20 * 100}%`);
+
+        // 设置行业基本信息
+        $('#industry-index').text(formatNumber(data.index, 2));
+        $('#industry-company-count').text(data.companyCount);
+
+        // 设置涨跌幅
+        const changeClass = parseFloat(data.change) >= 0 ? 'trend-up' : 'trend-down';
+        const changeIcon = parseFloat(data.change) >= 0 ? '<i class="fas fa-caret-up"></i>' : '<i class="fas fa-caret-down"></i>';
+        $('#industry-change').html(`<span class="${changeClass}">${changeIcon} ${data.change}%</span>`);
+
+        // 设置资金流向
+        $('#industry-inflow').text(formatNumber(data.inflow, 2) + ' 亿');
+        $('#industry-outflow').text(formatNumber(data.outflow, 2) + ' 亿');
+
+        const netFlowClass = parseFloat(data.netFlow) >= 0 ? 'trend-up' : 'trend-down';
+        const netFlowIcon = parseFloat(data.netFlow) >= 0 ? '<i class="fas fa-arrow-up"></i>' : '<i class="fas fa-arrow-down"></i>';
+        $('#industry-net-flow').html(`<span class="${netFlowClass}">${netFlowIcon} ${formatNumber(data.netFlow, 2)} 亿</span>`);
+
+        // 设置投资建议
+        $('#industry-recommendation').text(data.recommendation);
+
+        // 绘制行业评分图表
+        renderIndustryScoreChart(data.score);
+
+        // 绘制资金流向图表
+        renderIndustryFlowChart(data.flowHistory);
+    }
+
+
+    // 渲染行业成分股表格
+    function renderIndustryStocks(data) {
+        if (!data) {
+            console.error("renderIndustryStocks: No data provided");
+            return;
+        }
+
+        console.log("Rendering industry stocks:", data);
+
+        let html = '';
+
+        if (!Array.isArray(data) || data.length === 0) {
+            html = '<tr><td colspan="9" class="text-center">暂无成分股数据</td></tr>';
+        } else {
+            data.forEach(stock => {
+                const changeClass = parseFloat(stock.change) >= 0 ? 'trend-up' : 'trend-down';
+                const changeIcon = parseFloat(stock.change) >= 0 ? '<i class="fas fa-caret-up"></i>' : '<i class="fas fa-caret-down"></i>';
+
+                html += `
+                    <tr>
+                        <td>${stock.code}</td>
+                        <td>${stock.name}</td>
+                        <td>${formatNumber(stock.price, 2)}</td>
+                        <td class="${changeClass}">${changeIcon} ${formatNumber(stock.change, 2)}%</td>
+                        <td>${formatNumber(stock.volume, 0)}</td>
+                        <td>${formatMoney(stock.turnover)}</td>
+                        <td>${formatNumber(stock.turnover_rate || stock.turnoverRate, 2)}%</td>
+                        <td>${stock.score ? formatNumber(stock.score, 0) : '-'}</td>
+                        <td>
+                            <a href="/stock_detail/${stock.code}" class="btn btn-sm btn-outline-primary">
+                                <i class="fas fa-chart-line"></i>
+                            </a>
+                        </td>
+                    </tr>
+                `;
+            });
+        }
+
+        $('#industry-stocks-table').html(html);
+    }
+
+    function renderCapitalFlowChart(flowHistory) {
+
+        // 添加数据检查
+        if (!flowHistory || !Array.isArray(flowHistory) || flowHistory.length === 0) {
+            // 如果没有历史数据，显示提示信息
+            document.querySelector("#industry-flow-chart").innerHTML =
+                '<div class="text-center text-muted py-5">暂无资金流向历史数据</div>';
+            return;
+        }
+
+        const dates = flowHistory.map(item => item.date);
+        const netFlows = flowHistory.map(item => parseFloat(item.netFlow));
+        const changes = flowHistory.map(item => parseFloat(item.change));
+
+        // 确保所有数组都有值
+        if (dates.length === 0 || netFlows.length === 0 || changes.length === 0) {
+            document.querySelector("#industry-flow-chart").innerHTML =
+                '<div class="text-center text-muted py-5">资金流向数据格式不正确</div>';
+            return;
+        }
+
+        const options = {
+            series: [
+                {
+                    name: '净流入(亿)',
+                    type: 'column',
+                    data: netFlows
+                },
+                {
+                    name: '涨跌幅(%)',
+                    type: 'line',
+                    data: changes
+                }
+            ],
+            chart: {
+                height: 265,
+                type: 'line',
+                toolbar: {
+                    show: false
+                }
+            },
+            plotOptions: {
+                bar: {
+                    borderRadius: 2,
+                    dataLabels: {
+                        position: 'top'
+                    }
+                }
+            },
+            dataLabels: {
+                enabled: false
+            },
+            stroke: {
+                width: [0, 3]
+            },
+            colors: ['#0d6efd', '#dc3545'],
+            xaxis: {
+                categories: dates,
+                labels: {
+                    formatter: function(value) {
+                        return value.slice(5); // 只显示月-日
+                    }
+                }
+            },
+            yaxis: [
+                {
+                    title: {
+                        text: '净流入(亿)',
+                        style: {
+                            fontSize: '12px'
+                        }
+                    },
+                    labels: {
+                        formatter: function(val) {
+                            return val.toFixed(2);
+                        }
+                    }
+                },
+                {
+                    opposite: true,
+                    title: {
+                        text: '涨跌幅(%)',
+                        style: {
+                            fontSize: '12px'
+                        }
+                    },
+                    labels: {
+                        formatter: function(val) {
+                            return val.toFixed(2);
+                        }
+                    }
+                }
+            ],
+            tooltip: {
+                shared: true,
+                intersect: false,
+                y: {
+                    formatter: function(value, { seriesIndex }) {
+                        if (seriesIndex === 0) {
+                            return value.toFixed(2) + ' 亿';
+                        }
+                        return value.toFixed(2) + '%';
+                    }
+                }
+            },
+            legend: {
+                position: 'top'
+            }
+        };
+
+        // 清除任何现有图表
+        document.querySelector("#industry-flow-chart").innerHTML = '';
+        const chart = new ApexCharts(document.querySelector("#industry-flow-chart"), options);
+        chart.render();
+    }
+
+    // 填充行业选择器
+    function populateIndustrySelector(data) {
+        let options = '<option value="">-- 选择行业 --</option>';
+        const industries = data.map(item => item.industry);
+
+        industries.forEach(industry => {
+            options += `<option value="${industry}">${industry}</option>`;
+        });
+
+        $('#industry-selector').html(options);
+    }
+
+    // 评分颜色类
+    function getScoreColorClass(score) {
+        if (score >= 80) return 'badge rounded-pill bg-success';
+        if (score >= 60) return 'badge rounded-pill bg-primary';
+        if (score >= 40) return 'badge rounded-pill bg-warning text-dark';
+        return 'badge rounded-pill bg-danger';
+    }
+
+    // 获取评分颜色
+    function getScoreColor(score) {
+        if (score >= 80) return '#28a745'; // 绿色
+        if (score >= 60) return '#007bff'; // 蓝色
+        if (score >= 40) return '#ffc107'; // 黄色
+        return '#dc3545'; // 红色
+    }
+
+    // 格式化金额（单位：万元）
+    function formatMoney(value) {
+        if (value === undefined || value === null) {
+            return '--';
+        }
+
+        value = parseFloat(value);
+        if (isNaN(value)) {
+            return '--';
+        }
+
+        if (value >= 100000000) {
+            return (value / 100000000).toFixed(2) + ' 亿';
+        } else if (value >= 10000) {
+            return (value / 10000).toFixed(2) + ' 万';
+        } else {
+            return value.toFixed(2);
+        }
+    }
+
+    // 渲染行业资金流向图表
+    function renderIndustryFlowChart(data) {
+        const options = {
+            series: [
+                {
+                    name: '流入资金',
+                    data: data.flowHistory.map(item => item.inflow)
+                },
+                {
+                    name: '流出资金',
+                    data: data.flowHistory.map(item => item.outflow)
+                },
+                {
+                    name: '净流入',
+                    data: data.flowHistory.map(item => item.netFlow)
+                }
+            ],
+            chart: {
+                type: 'bar',
+                height: 200,
+                toolbar: {
+                    show: false
+                }
+            },
+            plotOptions: {
+                bar: {
+                    horizontal: false,
+                    columnWidth: '55%',
+                    endingShape: 'rounded'
+                },
+            },
+            dataLabels: {
+                enabled: false
+            },
+            stroke: {
+                show: true,
+                width: 2,
+                colors: ['transparent']
+            },
+            xaxis: {
+                categories: data.flowHistory.map(item => item.date)
+            },
+            yaxis: {
+                title: {
+                    text: '亿元'
+                }
+            },
+            fill: {
+                opacity: 1
+            },
+            tooltip: {
+                y: {
+                    formatter: function(val) {
+                        return val + " 亿元";
+                    }
+                }
+            },
+            colors: ['#00E396', '#FF4560', '#008FFB']
+        };
+
+        const chart = new ApexCharts(document.querySelector("#industry-flow-chart"), options);
+        chart.render();
+    }
+
+    // 绘制行业评分图表
+    function renderIndustryScoreChart(score) {
+        const options = {
+            series: [score],
+            chart: {
+                height: 150,
+                type: 'radialBar',
+            },
+            plotOptions: {
+                radialBar: {
+                    hollow: {
+                        size: '70%',
+                    },
+                    dataLabels: {
+                        show: false
+                    }
+                }
+            },
+            colors: [getScoreColor(score)],
+            stroke: {
+                lineCap: 'round'
+            }
+        };
+
+        // 清除旧图表并创建新图表
+        $('#industry-score-chart').empty();
+        const chart = new ApexCharts(document.querySelector("#industry-score-chart"), options);
+        chart.render();
+    }
+
+    // 绘制行业资金流向图表
+    function renderIndustryFlowChart(flowHistory) {
+        if (!flowHistory || !Array.isArray(flowHistory) || flowHistory.length === 0) {
+            console.error("renderIndustryFlowChart: Invalid flow history data");
+            return;
+        }
+
+        console.log("Rendering flow chart with data:", flowHistory);
+
+        const dates = flowHistory.map(item => item.date);
+        const netFlows = flowHistory.map(item => parseFloat(item.netFlow));
+        const changes = flowHistory.map(item => parseFloat(item.change));
+
+        const options = {
+            series: [
+                {
+                    name: '净流入(亿)',
+                    type: 'column',
+                    data: netFlows
+                },
+                {
+                    name: '涨跌幅(%)',
+                    type: 'line',
+                    data: changes
+                }
+            ],
+            chart: {
+                height: 200,
+                type: 'line',
+                toolbar: {
+                    show: false
+                }
+            },
+            plotOptions: {
+                bar: {
+                    borderRadius: 2,
+                    dataLabels: {
+                        position: 'top'
+                    }
+                }
+            },
+            dataLabels: {
+                enabled: false
+            },
+            stroke: {
+                width: [0, 3]
+            },
+            colors: ['#0d6efd', '#dc3545'],
+            xaxis: {
+                categories: dates,
+                labels: {
+                    formatter: function(value) {
+                        // Only show month-day if it's a date string
+                        if (typeof value === 'string' && value.includes('-')) {
+                            return value.slice(5); // 只显示月-日
+                        }
+                        return value;
+                    }
+                }
+            },
+            yaxis: [
+                {
+                    title: {
+                        text: '净流入(亿)',
+                        style: {
+                            fontSize: '12px'
+                        }
+                    },
+                    labels: {
+                        formatter: function(val) {
+                            return val.toFixed(2);
+                        }
+                    }
+                },
+                {
+                    opposite: true,
+                    title: {
+                        text: '涨跌幅(%)',
+                        style: {
+                            fontSize: '12px'
+                        }
+                    },
+                    labels: {
+                        formatter: function(val) {
+                            return val.toFixed(2);
+                        }
+                    }
+                }
+            ],
+            tooltip: {
+                shared: true,
+                intersect: false,
+                y: {
+                    formatter: function(value, { seriesIndex }) {
+                        if (seriesIndex === 0) {
+                            return value.toFixed(2) + ' 亿';
+                        }
+                        return value.toFixed(2) + '%';
+                    }
+                }
+            },
+            legend: {
+                position: 'top'
+            }
+        };
+
+        // 清除旧图表并创建新图表
+        $('#industry-flow-chart').empty();
+        try {
+            const chart = new ApexCharts(document.querySelector("#industry-flow-chart"), options);
+            chart.render();
+        } catch (e) {
+            console.error("Error rendering flow chart:", e);
+        }
+    }
+
+    // 渲染行业对比图表
+    function renderIndustryCompareCharts(data) {
+        // 按资金净流入排序
+        const sortedByNetFlow = [...data].sort((a, b) => b.netFlow - a.netFlow);
+
+        // 资金净流入前10
+        const topInflow = sortedByNetFlow.slice(0, 10);
+        renderBarChart('top-inflow-chart', topInflow.map(item => item.industry), topInflow.map(item => item.netFlow), '资金净流入(亿元)', '#00E396');
+
+        // 资金净流出前10
+        const bottomInflow = [...sortedByNetFlow].reverse().slice(0, 10);
+        renderBarChart('top-outflow-chart', bottomInflow.map(item => item.industry), bottomInflow.map(item => Math.abs(item.netFlow)), '资金净流出(亿元)', '#FF4560');
+
+        // 按涨跌幅排序
+        const sortedByChange = [...data].sort((a, b) => parseFloat(b.change) - parseFloat(a.change));
+
+        // 涨幅前10
+        const topGainers = sortedByChange.slice(0, 10);
+        renderBarChart('top-gainers-chart', topGainers.map(item => item.industry), topGainers.map(item => parseFloat(item.change)), '涨幅(%)', '#00E396');
+
+        // 跌幅前10
+        const topLosers = [...sortedByChange].reverse().slice(0, 10);
+        renderBarChart('top-losers-chart', topLosers.map(item => item.industry), topLosers.map(item => Math.abs(parseFloat(item.change))), '跌幅(%)', '#FF4560');
+    }
+
+    // 通用水平条形图
+    function renderBarChart(elementId, categories, data, title, color) {
+        if (!categories || !data || categories.length === 0 || data.length === 0) {
+            console.error(`renderBarChart: Invalid data for ${elementId}`);
+            return;
+        }
+
+        const options = {
+            series: [{
+                name: title,
+                data: data
+            }],
+            chart: {
+                type: 'bar',
+                height: 300,
+                toolbar: {
+                    show: false
+                }
+            },
+            plotOptions: {
+                bar: {
+                    horizontal: true,
+                    dataLabels: {
+                        position: 'top',
+                    },
+                }
+            },
+            dataLabels: {
+                enabled: true,
+                offsetX: -6,
+                style: {
+                    fontSize: '12px',
+                    colors: ['#fff']
+                },
+                formatter: function(val) {
+                    return val.toFixed(2);
+                }
+            },
+            stroke: {
+                show: true,
+                width: 1,
+                colors: ['#fff']
+            },
+            xaxis: {
+                categories: categories
+            },
+            yaxis: {
+                title: {
+                    text: title
+                }
+            },
+            fill: {
+                opacity: 1
+            },
+            colors: [color]
+        };
+
+        // 清除旧图表并创建新图表
+        $(`#${elementId}`).empty();
+        try {
+            const chart = new ApexCharts(document.querySelector(`#${elementId}`), options);
+            chart.render();
+        } catch (e) {
+            console.error(`Error rendering chart ${elementId}:`, e);
+        }
+    }
+
+    // 导出CSV
+    function exportToCSV() {
+        // 获取表格数据
+        const table = document.querySelector('#industry-overview table');
+        let csv = [];
+        let rows = table.querySelectorAll('tr');
+
+        for (let i = 0; i < rows.length; i++) {
+            let row = [], cols = rows[i].querySelectorAll('td, th');
+
+            for (let j = 0; j < cols.length - 1; j++) { // 跳过最后一列（操作列）
+                // 获取单元格的文本内容，去除HTML标签
+                let text = cols[j].innerText.replace(/(\r\n|\n|\r)/gm, '').replace(/,/g, '，');
+                row.push(text);
+            }
+
+            csv.push(row.join(','));
+        }
+
+        // 下载CSV文件
+        const period = $('#period-badge').text();
+        const csvString = csv.join('\n');
+        const filename = `行业资金流向_${period}_${new Date().toISOString().slice(0, 10)}.csv`;
+
+        const blob = new Blob(['\uFEFF' + csvString], { type: 'text/csv;charset=utf-8;' });
+        const link = document.createElement('a');
+        link.href = URL.createObjectURL(blob);
+        link.download = filename;
+
+        link.style.display = 'none';
+        document.body.appendChild(link);
+
+        link.click();
+
+        document.body.removeChild(link);
+    }
+
+</script>
+{% endblock %}

templates/layout.html

@@ -0,0 +1,495 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+    <meta charset="UTF-8">
+    <link rel="icon" href="/static/favicon.ico" type="image/x-icon">
+    <link rel="shortcut icon" href="/static/favicon.ico" type="image/x-icon">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>{% block title %}智能分析系统{% endblock %}</title>
+    <!-- Bootstrap CSS -->
+    <link href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css" rel="stylesheet">
+    <!-- Font Awesome -->
+    <link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/css/all.min.css" rel="stylesheet">
+    <!-- ApexCharts -->
+    <link href="https://cdn.jsdelivr.net/npm/[email protected]/dist/apexcharts.min.css" rel="stylesheet">
+    <!-- Custom CSS -->
+    <style>
+        body {
+            font-family: 'Helvetica Neue', Arial, sans-serif;
+            background-color: #f8f9fa;
+        }
+        .navbar-brand {
+            font-weight: bold;
+        }
+        .nav-item {
+            margin-left: 10px;
+        }
+        .sidebar {
+            background-color: #343a40;
+            color: white;
+            min-height: calc(100vh - 56px);
+        }
+        .sidebar .nav-link {
+            color: #ced4da;
+            padding: 0.75rem 1rem;
+        }
+        .sidebar .nav-link:hover {
+            color: #fff;
+            background-color: rgba(255, 255, 255, 0.1);
+        }
+        .sidebar .nav-link.active {
+            color: #fff;
+            background-color: rgba(255, 255, 255, 0.2);
+        }
+        .sidebar .nav-link i {
+            margin-right: 10px;
+            width: 20px;
+            text-align: center;
+        }
+        .main-content {
+            padding: 20px;
+        }
+        .card {
+            margin-bottom: 20px;
+            box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);
+            overflow: hidden; /* Prevent content from stretching container */
+        }
+
+        .card-header {
+            padding: 0.5rem 1rem;
+            height: auto !important;
+            max-height: 50px;
+        }
+
+        .form-control, .form-select, .input-group-text {
+            font-size: 0.875rem;
+        }
+
+        .input-group-sm .input-group-text {
+            padding: 0.25rem 0.5rem;
+        }
+
+        .card-body.py-2 {
+            padding-top: 0.5rem;
+            padding-bottom: 0.5rem;
+        }
+
+
+        .card-body {
+            padding: 1.25rem;
+            overflow: hidden; /* Prevent content from stretching container */
+        }
+        .loading {
+            display: flex;
+            justify-content: center;
+            align-items: center;
+            height: 200px;
+        }
+        .spinner-border {
+            width: 3rem;
+            height: 3rem;
+        }
+        .badge-success {
+            background-color: #28a745;
+        }
+        .badge-danger {
+            background-color: #dc3545;
+        }
+        .badge-warning {
+            background-color: #ffc107;
+            color: #212529;
+        }
+        .score-pill {
+            font-size: 1.2rem;
+            padding: 0.5rem 1rem;
+        }
+        #loading-overlay {
+            position: fixed;
+            top: 0;
+            left: 0;
+            width: 100%;
+            height: 100%;
+            background-color: rgba(255, 255, 255, 0.8);
+            display: none;
+            justify-content: center;
+            align-items: center;
+            z-index: 9999;
+        }
+        .text-strong {
+            font-weight: bold;
+        }
+        .text-larger {
+            font-size: 1.1em;
+        }
+        .trend-up {
+            color: #28a745;
+        }
+        .trend-down {
+            color: #dc3545;
+        }
+        .analysis-section {
+            margin-bottom: 1.5rem;
+        }
+
+        /* Fix for chart container heights */
+        #price-chart {
+            height: 400px !important;
+            max-height: 400px;
+        }
+
+        /* Fix for indicators chart container */
+        #indicators-chart {
+            height: 350px !important;
+            max-height: 350px;
+        }
+
+        /* Fix chart containers */
+        .apexcharts-canvas {
+            overflow: visible !important;
+        }
+
+        /* Fix for radar chart */
+        #radar-chart {
+            height: 200px !important;
+            max-height: 200px;
+        }
+
+        /* Fix for score chart */
+        #score-chart {
+            height: 200px !important;
+            max-height: 200px;
+        }
+
+        /* Fix header alignment */
+        .card-header h5 {
+            margin-bottom: 0;
+            display: flex;
+            align-items: center;
+        }
+
+        .apexcharts-tooltip {
+            background: #fff !important;
+            border: 1px solid #e3e3e3 !important;
+            box-shadow: 0 2px 5px rgba(0,0,0,0.1) !important;
+            border-radius: 4px !important;
+            padding: 8px !important;
+            font-size: 13px !important;
+        }
+
+        .apexcharts-tooltip-title {
+            background: #f8f9fa !important;
+            border-bottom: 1px solid #e3e3e3 !important;
+            padding: 6px 8px !important;
+            margin-bottom: 4px !important;
+            font-weight: 600 !important;
+        }
+
+        .apexcharts-tooltip-y-group {
+            padding: 3px 0 !important;
+        }
+
+        .apexcharts-tooltip-candlestick {
+            padding: 5px 8px !important;
+        }
+
+        .apexcharts-tooltip-candlestick div {
+            margin: 3px 0 !important;
+        }
+
+        .apexcharts-tooltip-candlestick span {
+            font-weight: 600 !important;
+        }
+
+        .apexcharts-crosshairs {
+            stroke-width: 1px !important;
+            stroke: #90A4AE !important;
+            stroke-dasharray: 0 !important;
+            opacity: 0.8 !important;
+        }
+
+        .apexcharts-tooltip-marker {
+            width: 10px !important;
+            height: 10px !important;
+            display: inline-block !important;
+            margin-right: 5px !important;
+            border-radius: 50% !important;
+        }
+
+        .apexcharts-tooltip-series-group {
+            padding: 4px 8px !important;
+            border-bottom: 1px solid #eee !important;
+        }
+
+        .apexcharts-tooltip-series-group:last-child {
+            border-bottom: none !important;
+        }
+
+        .apexcharts-tooltip-text-y-value {
+            font-weight: 600 !important;
+        }
+
+        .apexcharts-xaxistooltip {
+            background: #fff !important;
+            border: 1px solid #e3e3e3 !important;
+            border-radius: 2px !important;
+            box-shadow: 0 2px 5px rgba(0,0,0,0.1) !important;
+            padding: 4px 8px !important;
+            font-size: 12px !important;
+            color: #333 !important;
+        }
+
+        .apexcharts-yaxistooltip {
+            background: #fff !important;
+            border: 1px solid #e3e3e3 !important;
+            border-radius: 2px !important;
+            box-shadow: 0 2px 5px rgba(0,0,0,0.1) !important;
+            padding: 4px 8px !important;
+            font-size: 12px !important;
+            color: #333 !important;
+        }
+
+        /* AI分析样式 */
+        .analysis-para {
+          line-height: 1.8;
+          margin-bottom: 1.2rem;
+          color: #333;
+        }
+
+        .keyword {
+          color: #2c7be5;
+          font-weight: 600;
+        }
+
+        .term {
+          color: #d6336c;
+          font-weight: 500;
+          padding: 0 2px;
+        }
+
+        .price {
+          color: #00a47c;
+          font-family: 'Roboto Mono', monospace;
+          background: #f3faf8;
+          padding: 2px 4px;
+          border-radius: 3px;
+        }
+
+        .date {
+          color: #6c757d;
+          font-family: 'Roboto Mono', monospace;
+        }
+
+        strong.keyword {
+          border-bottom: 2px solid #2c7be5;
+        }
+
+        .table-info {
+            position: relative;
+        }
+
+        .table-info:after {
+            content: '';
+            position: absolute;
+            top: 0;
+            left: 0;
+            right: 0;
+            bottom: 0;
+            background: rgba(0, 123, 255, 0.1);
+            animation: pulse 1.5s infinite;
+        }
+
+        @keyframes pulse {
+            0% { opacity: 0.5; }
+            50% { opacity: 0.3; }
+            100% { opacity: 0.5; }
+        }
+
+    </style>
+    {% block head %}{% endblock %}
+</head>
+<body>
+    <!-- Loading Overlay -->
+    <div id="loading-overlay">
+        <div class="spinner-border text-primary" role="status">
+            <span class="visually-hidden">Loading...</span>
+        </div>
+    </div>
+
+    <!-- Top Navigation -->
+    <nav class="navbar navbar-expand-lg navbar-dark bg-primary">
+        <div class="container-fluid">
+            <a class="navbar-brand" href="/">智能分析系统</a>
+            <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarNav" aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation">
+                <span class="navbar-toggler-icon"></span>
+            </button>
+            <!-- 在layout.html的导航栏部分修改 -->
+            <div class="collapse navbar-collapse" id="navbarNav">
+                <ul class="navbar-nav me-auto">
+                    <li class="nav-item">
+                        <a class="nav-link {% if request.path == '/' %}active{% endif %}" href="/"><i class="fas fa-home"></i> 主页</a>
+                    </li>
+                    <li class="nav-item">
+                        <a class="nav-link {% if request.path == '/dashboard' %}active{% endif %}" href="/dashboard"><i class="fas fa-chart-line"></i> 智能仪表盘</a>
+                    </li>
+                    <!-- 新增菜单项 - 基本面分析 -->
+                    <li class="nav-item">
+                        <a class="nav-link {% if request.path.startswith('/fundamental') %}active{% endif %}" href="/fundamental"><i class="fas fa-file-invoice-dollar"></i> 基本面分析</a>
+                    </li>
+                    <!-- 新增菜单项 - 资金流向 -->
+                    <li class="nav-item">
+                        <a class="nav-link {% if request.path.startswith('/capital_flow') %}active{% endif %}" href="/capital_flow"><i class="fas fa-money-bill-wave"></i> 资金流向</a>
+                    </li>
+                    <!-- 新增菜单项 - 情景预测 -->
+                    <li class="nav-item">
+                        <a class="nav-link {% if request.path.startswith('/scenario') %}active{% endif %}" href="/scenario_predict"><i class="fas fa-lightbulb"></i> 情景预测</a>
+                    </li>
+                    <li class="nav-item">
+                        <a class="nav-link {% if request.path == '/market_scan' %}active{% endif %}" href="/market_scan"><i class="fas fa-search"></i> 市场扫描</a>
+                    </li>
+                    <li class="nav-item">
+                        <a class="nav-link {% if request.path == '/portfolio' %}active{% endif %}" href="/portfolio"><i class="fas fa-briefcase"></i> 投资组合</a>
+                    </li>
+                    <!-- 新增菜单项 - 风险监控 -->
+                    <li class="nav-item">
+                        <a class="nav-link {% if request.path.startswith('/risk') %}active{% endif %}" href="/risk_monitor"><i class="fas fa-exclamation-triangle"></i> 风险监控</a>
+                    </li>
+                    <!-- 新增菜单项 - 智能问答 -->
+                    <li class="nav-item">
+                        <a class="nav-link {% if request.path == '/qa' %}active{% endif %}" href="/qa"><i class="fas fa-question-circle"></i> 智能问答</a>
+                    </li>
+                </ul>
+                <div class="d-flex">
+                    <div class="input-group">
+                        <input type="text" id="search-stock" class="form-control" placeholder="搜索股票代码/名称" aria-label="搜索股票">
+                        <button class="btn btn-light" type="button" id="search-button">
+                            <i class="fas fa-search"></i>
+                        </button>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </nav>
+
+    <div class="container-fluid">
+        <div class="row">
+            {% block sidebar %}{% endblock %}
+
+            <main class="{% if self.sidebar()|trim %}col-md-9 ms-sm-auto col-lg-10 px-md-4{% else %}col-12{% endif %} main-content">
+                {% block content %}{% endblock %}
+            </main>
+        </div>
+    </div>
+
+    <!-- Bootstrap JS with Popper -->
+    <script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/js/bootstrap.bundle.min.js"></script>
+    <!-- jQuery -->
+    <script src="https://code.jquery.com/jquery-3.6.0.min.js"></script>
+    <!-- ApexCharts -->
+    <script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/apexcharts.min.js"></script>
+    <!-- Common JS -->
+    <script>
+        // 显示加载中覆盖层
+        function showLoading() {
+            $('#loading-overlay').css('display', 'flex');
+        }
+
+        // 隐藏加载中覆盖层
+        function hideLoading() {
+            $('#loading-overlay').css('display', 'none');
+        }
+
+        // 显示错误提示
+        function showError(message) {
+            const alertHtml = `
+                <div class="alert alert-danger alert-dismissible fade show" role="alert">
+                    ${message}
+                    <button type="button" class="btn-close" data-bs-dismiss="alert" aria-label="Close"></button>
+                </div>
+            `;
+            $('#alerts-container').html(alertHtml);
+        }
+
+    // 显示信息提示
+    function showInfo(message) {
+        const alertHtml = `
+            <div class="alert alert-info alert-dismissible fade show" role="alert">
+                <i class="fas fa-info-circle me-2"></i>${message}
+                <button type="button" class="btn-close" data-bs-dismiss="alert" aria-label="Close"></button>
+            </div>
+        `;
+        $('#alerts-container').html(alertHtml);
+    }
+
+        // 显示成功提示
+        function showSuccess(message) {
+            const alertHtml = `
+                <div class="alert alert-success alert-dismissible fade show" role="alert">
+                    ${message}
+                    <button type="button" class="btn-close" data-bs-dismiss="alert" aria-label="Close"></button>
+                </div>
+            `;
+            $('#alerts-container').html(alertHtml);
+        }
+
+        // 搜索股票并跳转到详情页
+        $('#search-button').click(function() {
+            const stockCode = $('#search-stock').val().trim();
+            if (stockCode) {
+                window.location.href = `/stock_detail/${stockCode}`;
+            }
+        });
+
+        // 回车键搜索
+        $('#search-stock').keypress(function(e) {
+            if (e.which === 13) {
+                $('#search-button').click();
+            }
+        });
+
+        // 格式化数字 - 增强版
+        function formatNumber(num, digits = 2) {
+            if (num === null || num === undefined) return '-';
+            return parseFloat(num).toFixed(digits);
+        }
+
+        // 格式化技术指标 - 新增函数
+        function formatIndicator(value, indicatorType) {
+            if (value === null || value === undefined) return '-';
+
+            // 根据指标类型使用不同的小数位数
+            if (indicatorType === 'MACD' || indicatorType === 'Signal' || indicatorType === 'Histogram') {
+                return parseFloat(value).toFixed(3);  // MACD相关指标使用3位小数
+            } else if (indicatorType === 'RSI') {
+                return parseFloat(value).toFixed(2);  // RSI使用2位小数
+            } else {
+                return parseFloat(value).toFixed(2);  // 默认使用2位小数
+            }
+        }
+
+        // 格式化百分比
+        function formatPercent(num, digits = 2) {
+            if (num === null || num === undefined) return '-';
+            return parseFloat(num).toFixed(digits) + '%';
+        }
+
+        // 根据评分获取颜色类
+        function getScoreColorClass(score) {
+            if (score >= 80) return 'bg-success';
+            if (score >= 60) return 'bg-primary';
+            if (score >= 40) return 'bg-warning';
+            return 'bg-danger';
+        }
+
+        // 根据趋势获取颜色类
+        function getTrendColorClass(trend) {
+            return trend === 'UP' ? 'trend-up' : 'trend-down';
+        }
+
+        // 根据趋势获取图标
+        function getTrendIcon(trend) {
+            return trend === 'UP' ? '<i class="fas fa-arrow-up"></i>' : '<i class="fas fa-arrow-down"></i>';
+        }
+    </script>
+    {% block scripts %}{% endblock %}
+</body>
+</html>

templates/market_scan.html

@@ -0,0 +1,591 @@
+{% extends "layout.html" %}
+
+{% block title %}市场扫描 - 智能分析系统{% endblock %}
+
+{% block content %}
+<div class="container-fluid py-4">
+    <div id="alerts-container"></div>
+
+    <div class="row mb-4">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header d-flex justify-content-between">
+                    <h5 class="mb-0">市场扫描</h5>
+                </div>
+                <div class="card-body">
+                    <form id="scan-form" class="row g-3">
+                        <div class="col-md-3">
+                            <div class="input-group">
+                                <span class="input-group-text">选择指数</span>
+                                <select class="form-select" id="index-selector">
+                                    <option value="">-- 选择指数 --</option>
+                                    <option value="000300">沪深300</option>
+                                    <option value="000905">中证500</option>
+                                    <option value="000852">中证1000</option>
+                                    <option value="000001">上证指数</option>
+                                </select>
+                            </div>
+                        </div>
+                        <div class="col-md-3">
+                            <div class="input-group">
+                                <span class="input-group-text">选择行业</span>
+                                <select class="form-select" id="industry-selector">
+                                    <option value="">-- 选择行业 --</option>
+                                    <option value="保险">保险</option>
+                                    <option value="食品饮料">食品饮料</option>
+                                    <option value="多元金融">多元金融</option>
+                                    <option value="游戏">游戏</option>
+                                    <option value="酿酒行业">酿酒行业</option>
+                                    <option value="商业百货">商业百货</option>
+                                    <option value="证券">证券</option>
+                                    <option value="船舶制造">船舶制造</option>
+                                    <option value="家用轻工">家用轻工</option>
+                                    <option value="旅游酒店">旅游酒店</option>
+                                    <option value="美容护理">美容护理</option>
+                                    <option value="医疗服务">医疗服务</option>
+                                    <option value="软件开发">软件开发</option>
+                                    <option value="化学制药">化学制药</option>
+                                    <option value="医疗器械">医疗器械</option>
+                                    <option value="家电行业">家电行业</option>
+                                    <option value="汽车服务">汽车服务</option>
+                                    <option value="造纸印刷">造纸印刷</option>
+                                    <option value="纺织服装">纺织服装</option>
+                                    <option value="光伏设备">光伏设备</option>
+                                    <option value="房地产服务">房地产服务</option>
+                                    <option value="文化传媒">文化传媒</option>
+                                    <option value="医药商业">医药商业</option>
+                                    <option value="中药">中药</option>
+                                    <option value="专业服务">专业服务</option>
+                                    <option value="生物制品">生物制品</option>
+                                    <option value="仪器仪表">仪器仪表</option>
+                                    <option value="房地产开发">房地产开发</option>
+                                    <option value="教育">教育</option>
+                                    <option value="半导体">半导体</option>
+                                    <option value="玻璃玻纤">玻璃玻纤</option>
+                                    <option value="汽车整车">汽车整车</option>
+                                    <option value="消费电子">消费电子</option>
+                                    <option value="贸易行业">贸易行业</option>
+                                    <option value="包装材料">包装材料</option>
+                                    <option value="汽车零部件">汽车零部件</option>
+                                    <option value="电子化学品">电子化学品</option>
+                                    <option value="电子元件">电子元件</option>
+                                    <option value="装修建材">装修建材</option>
+                                    <option value="交运设备">交运设备</option>
+                                    <option value="农牧饲渔">农牧饲渔</option>
+                                    <option value="塑料制品">塑料制品</option>
+                                    <option value="珠宝首饰">珠宝首饰</option>
+                                    <option value="贵金属">贵金属</option>
+                                    <option value="非金属材料">非金属材料</option>
+                                    <option value="装修装饰">装修装饰</option>
+                                    <option value="风电设备">风电设备</option>
+                                    <option value="工程咨询服务">工程咨询服务</option>
+                                    <option value="专用设备">专用设备</option>
+                                    <option value="光学光电子">光学光电子</option>
+                                    <option value="航空机场">航空机场</option>
+                                    <option value="小金属">小金属</option>
+                                    <option value="物流行业">物流行业</option>
+                                    <option value="通用设备">通用设备</option>
+                                    <option value="计算机设备">计算机设备</option>
+                                    <option value="环保行业">环保行业</option>
+                                    <option value="航运港口">航运港口</option>
+                                    <option value="通信设备">通信设备</option>
+                                    <option value="水泥建材">水泥建材</option>
+                                    <option value="电池">电池</option>
+                                    <option value="化肥行业">化肥行业</option>
+                                    <option value="互联网服务">互联网服务</option>
+                                    <option value="工程建设">工程建设</option>
+                                    <option value="橡胶制品">橡胶制品</option>
+                                    <option value="化学原料">化学原料</option>
+                                    <option value="化纤行业">化纤行业</option>
+                                    <option value="农药兽药">农药兽药</option>
+                                    <option value="化学制品">化学制品</option>
+                                    <option value="能源金属">能源金属</option>
+                                    <option value="有色金属">有色金属</option>
+                                    <option value="采掘行业">采掘行业</option>
+                                    <option value="燃气">燃气</option>
+                                    <option value="综合行业">综合行业</option>
+                                    <option value="工程机械">工程机械</option>
+                                    <option value="银行">银行</option>
+                                    <option value="铁路公路">铁路公路</option>
+                                    <option value="石油行业">石油行业</option>
+                                    <option value="公用事业">公用事业</option>
+                                    <option value="电机">电机</option>
+                                    <option value="通信服务">通信服务</option>
+                                    <option value="钢铁行业">钢铁行业</option>
+                                    <option value="电力行业">电力行业</option>
+                                    <option value="电网设备">电网设备</option>
+                                    <option value="煤炭行业">煤炭行业</option>
+                                    <option value="电源设备">电源设备</option>
+                                    <option value="航天航空">航天航空</option>
+                                </select>                                
+                            </div>
+                        </div>
+                        <div class="col-md-3">
+                            <div class="input-group">
+                                <span class="input-group-text">自定义股票</span>
+                                <input type="text" class="form-control" id="custom-stocks" placeholder="多个股票代码用逗号分隔">
+                            </div>
+                        </div>
+                        <div class="col-md-3">
+                            <div class="input-group">
+                                <span class="input-group-text">最低分数</span>
+                                <input type="number" class="form-control" id="min-score" value="60" min="0" max="100">
+                                <button type="submit" class="btn btn-primary">
+                                    <i class="fas fa-search"></i> 扫描
+                                </button>
+                            </div>
+                        </div>
+                    </form>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <div class="row mb-4">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header d-flex justify-content-between">
+                    <h5 class="mb-0">扫描结果</h5>
+                    <div>
+                        <span class="badge bg-primary ms-2" id="result-count">0</span>
+                        <button class="btn btn-sm btn-outline-primary ms-2" id="export-btn" style="display: none;">
+                            <i class="fas fa-download"></i> 导出结果
+                        </button>
+                    </div>
+                </div>
+                <div class="card-body">
+                    <div id="scan-loading" class="text-center py-5" style="display: none;">
+                        <div class="spinner-border text-primary" role="status">
+                            <span class="visually-hidden">Loading...</span>
+                        </div>
+                        <p class="mt-2" id="scan-message">正在扫描市场，请稍候...</p>
+                        <div class="progress mt-3" style="height: 5px;">
+                            <div class="progress-bar progress-bar-striped progress-bar-animated" role="progressbar" style="width: 100%"></div>
+                        </div>
+                        <button id="cancel-scan-btn" class="btn btn-outline-secondary mt-3">
+                            <i class="fas fa-times"></i> 取消扫描
+                        </button>
+                    </div>
+
+                    <!-- 添加错误重试区域 -->
+                    <div id="scan-error-retry" class="text-center mt-3" style="display: none;">
+                        <button id="scan-retry-button" class="btn btn-primary mt-2">
+                            <i class="fas fa-sync-alt"></i> 重试扫描
+                        </button>
+                        <p class="text-muted small mt-2">
+                            已超负载
+                        </p>
+                    </div>
+
+                    <div id="scan-results">
+                        <table class="table table-hover">
+                            <thead>
+                                <tr>
+                                    <th>代码</th>
+                                    <th>名称</th>
+                                    <th>行业</th>
+                                    <th>得分</th>
+                                    <th>价格</th>
+                                    <th>涨跌幅</th>
+                                    <th>RSI</th>
+                                    <th>MA趋势</th>
+                                    <th>成交量</th>
+                                    <th>建议</th>
+                                    <th>操作</th>
+                                </tr>
+                            </thead>
+                            <tbody id="results-table">
+                                <tr>
+                                    <td colspan="11" class="text-center">暂无数据，请开始扫描</td>
+                                </tr>
+                            </tbody>
+                        </table>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+</div>
+{% endblock %}
+
+{% block scripts %}
+<script>
+    $(document).ready(function() {
+        // 表单提交
+        $('#scan-form').submit(function(e) {
+            e.preventDefault();
+
+            // 获取股票列表
+            let stockList = [];
+
+            // 获取指数股票
+            const indexCode = $('#index-selector').val();
+            if (indexCode) {
+                fetchIndexStocks(indexCode);
+                return;
+            }
+
+            // 获取行业股票
+            const industry = $('#industry-selector').val();
+            if (industry) {
+                fetchIndustryStocks(industry);
+                return;
+            }
+
+            // 获取自定义股票
+            const customStocks = $('#custom-stocks').val().trim();
+            if (customStocks) {
+                stockList = customStocks.split(',').map(s => s.trim());
+                scanMarket(stockList);
+            } else {
+                showError('请至少选择一种方式获取股票列表');
+            }
+        });
+
+        // 指数选择变化
+        $('#index-selector').change(function() {
+            if ($(this).val()) {
+                $('#industry-selector').val('');
+            }
+        });
+
+        // 行业选择变化
+        $('#industry-selector').change(function() {
+            if ($(this).val()) {
+                $('#index-selector').val('');
+            }
+        });
+
+        // 导出结果
+        $('#export-btn').click(function() {
+            exportToCSV();
+        });
+
+        // 获取指数成分股
+        function fetchIndexStocks(indexCode) {
+            $('#scan-loading').show();
+            $('#scan-results').hide();
+
+            $.ajax({
+                url: `/api/index_stocks?index_code=${indexCode}`,
+                type: 'GET',
+                dataType: 'json',
+                success: function(response) {
+                    const stockList = response.stock_list;
+                    if (stockList && stockList.length > 0) {
+                        // 保存最近的扫描列表用于重试
+                        window.lastScanList = stockList;
+
+                        scanMarket(stockList);
+                    } else {
+                        $('#scan-loading').hide();
+                        $('#scan-results').show();
+                        showError('获取指数成分股失败，或成分股列表为空');
+                    }
+                },
+                error: function(error) {
+                    $('#scan-loading').hide();
+                    $('#scan-results').show();
+                    showError('获取指数成分股失败: ' + (error.responseJSON ? error.responseJSON.error : error.statusText));
+                }
+            });
+        }
+
+        // 获取行业成分股
+        function fetchIndustryStocks(industry) {
+            $('#scan-loading').show();
+            $('#scan-results').hide();
+
+            $.ajax({
+                url: `/api/industry_stocks?industry=${encodeURIComponent(industry)}`,
+                type: 'GET',
+                dataType: 'json',
+                success: function(response) {
+                    const stockList = response.stock_list;
+                    if (stockList && stockList.length > 0) {
+                        // 保存最近的扫描列表用于重试
+                        window.lastScanList = stockList;
+
+                        scanMarket(stockList);
+                    } else {
+                        $('#scan-loading').hide();
+                        $('#scan-results').show();
+                        showError('获取行业成分股失败，或成分股列表为空');
+                    }
+                },
+                error: function(error) {
+                    $('#scan-loading').hide();
+                    $('#scan-results').show();
+                    showError('获取行业成分股失败: ' + (error.responseJSON ? error.responseJSON.error : error.statusText));
+                }
+            });
+        }
+
+        // 扫描市场
+        function scanMarket(stockList) {
+            $('#scan-loading').show();
+            $('#scan-results').hide();
+            $('#scan-error-retry').hide();
+
+            // 添加处理时间计数器
+            let processingTime = 0;
+            let stockCount = stockList.length;
+
+            // 保存上次扫描列表
+            window.lastScanList = stockList;
+
+            // 更新扫描提示消息
+            $('#scan-message').html(`正在准备扫描${stockCount}只股票，请稍候...`);
+
+            const minScore = parseInt($('#min-score').val() || 60);
+
+            // 第一步：启动扫描任务
+            $.ajax({
+                url: '/api/start_market_scan',
+                type: 'POST',
+                contentType: 'application/json',
+                data: JSON.stringify({
+                    stock_list: stockList,
+                    min_score: minScore,
+                    market_type: 'A'
+                }),
+                success: function(response) {
+                    const taskId = response.task_id;
+
+                    if (!taskId) {
+                        showError('启动扫描任务失败：未获取到任务ID');
+                        $('#scan-loading').hide();
+                        $('#scan-results').show();
+                        $('#scan-error-retry').show();
+                        return;
+                    }
+
+                    // 启动轮询任务状态
+                    pollScanStatus(taskId, processingTime);
+                },
+                error: function(xhr, status, error) {
+                    $('#scan-loading').hide();
+                    $('#scan-results').show();
+
+                    let errorMsg = '启动扫描任务失败';
+                    if (xhr.responseJSON && xhr.responseJSON.error) {
+                        errorMsg += ': ' + xhr.responseJSON.error;
+                    } else if (error) {
+                        errorMsg += ': ' + error;
+                    }
+
+                    showError(errorMsg);
+                    $('#scan-error-retry').show();
+                }
+            });
+        }
+
+        // 轮询扫描任务状态
+        function pollScanStatus(taskId, startTime) {
+            let elapsedTime = startTime || 0;
+            let pollInterval;
+
+            // 立即执行一次，然后设置定时器
+            checkStatus();
+
+            function checkStatus() {
+                $.ajax({
+                    url: `/api/scan_status/${taskId}`,
+                    type: 'GET',
+                    success: function(response) {
+                        // 更新计时和进度
+                        elapsedTime++;
+                        const progress = response.progress || 0;
+
+                        // 更新进度消息
+                        $('#scan-message').html(`正在扫描市场...<br>
+                            进度: ${progress}% 完成<br>
+                            已处理 ${Math.round(response.total * progress / 100)} / ${response.total} 只股票<br>
+                            耗时: ${elapsedTime}秒`);
+
+                        // 检查任务状态
+                        if (response.status === 'completed') {
+                            // 扫描完成，停止轮询
+                            clearInterval(pollInterval);
+
+                            // 显示结果
+                            renderResults(response.result || []);
+                            $('#scan-loading').hide();
+                            $('#scan-results').show();
+
+                            // 如果结果为空，显示提示
+                            if (!response.result || response.result.length === 0) {
+                                $('#results-table').html('<tr><td colspan="11" class="text-center">未找到符合条件的股票</td></tr>');
+                                $('#result-count').text('0');
+                                $('#export-btn').hide();
+                            }
+
+                        } else if (response.status === 'failed') {
+                            // 扫描失败，停止轮询
+                            clearInterval(pollInterval);
+
+                            $('#scan-loading').hide();
+                            $('#scan-results').show();
+
+                            showError('扫描任务失败: ' + (response.error || '未知错误'));
+                            $('#scan-error-retry').show();
+
+                        } else {
+                            // 任务仍在进行中，继续轮询
+                            // 轮询间隔根据进度动态调整
+                            if (!pollInterval) {
+                                pollInterval = setInterval(checkStatus, 2000);
+                            }
+                        }
+                    },
+                    error: function(xhr, status, error) {
+
+                        // 尝试继续轮询
+                        if (!pollInterval) {
+                            pollInterval = setInterval(checkStatus, 3000);
+                        }
+
+                        // 更新进度消息
+                        $('#scan-message').html(`正在扫描市场...<br>
+                            无法获取最新进度<br>
+                            耗时: ${elapsedTime}秒`);
+                    }
+                });
+            }
+        }
+
+        // 取消扫描任务
+        function cancelScan(taskId) {
+            $.ajax({
+                url: `/api/cancel_scan/${taskId}`,
+                type: 'POST',
+                success: function(response) {
+                    $('#scan-loading').hide();
+                    $('#scan-results').show();
+                    showError('扫描任务已取消');
+                    $('#scan-error-retry').show();
+                },
+                error: function(xhr, status, error) {
+                    console.error('取消扫描任务失败:', error);
+                }
+            });
+        }
+
+        // 渲染扫描结果
+        function renderResults(results) {
+            if (!results || results.length === 0) {
+                $('#results-table').html('<tr><td colspan="11" class="text-center">未找到符合条件的股票</td></tr>');
+                $('#result-count').text('0');
+                $('#export-btn').hide();
+                return;
+            }
+
+            let html = '';
+            results.forEach(result => {
+                // 获取股票评分的颜色类
+                const scoreClass = getScoreColorClass(result.score);
+
+                // 获取MA趋势的类和图标
+                const maTrendClass = getTrendColorClass(result.ma_trend);
+                const maTrendIcon = getTrendIcon(result.ma_trend);
+
+                // 获取价格变动的类和图标
+                const priceChangeClass = result.price_change >= 0 ? 'trend-up' : 'trend-down';
+                const priceChangeIcon = result.price_change >= 0 ? '<i class="fas fa-caret-up"></i>' : '<i class="fas fa-caret-down"></i>';
+
+                html += `
+                    <tr>
+                        <td>${result.stock_code}</td>
+                        <td>${result.stock_name || '未知'}</td>
+                        <td>${result.industry || '-'}</td>
+                        <td><span class="badge ${scoreClass}">${result.score}</span></td>
+                        <td>${formatNumber(result.price)}</td>
+                        <td class="${priceChangeClass}">${priceChangeIcon} ${formatPercent(result.price_change)}</td>
+                        <td>${formatNumber(result.rsi)}</td>
+                        <td class="${maTrendClass}">${maTrendIcon} ${result.ma_trend}</td>
+                        <td>${result.volume_status}</td>
+                        <td>${result.recommendation}</td>
+                        <td>
+                            <a href="/stock_detail/${result.stock_code}" class="btn btn-sm btn-primary">
+                                <i class="fas fa-chart-line"></i> 详情
+                            </a>
+                        </td>
+                    </tr>
+                `;
+            });
+
+            $('#results-table').html(html);
+            $('#result-count').text(results.length);
+            $('#export-btn').show();
+        }
+
+        // 导出到CSV
+        function exportToCSV() {
+            // 获取表格数据
+            const table = document.querySelector('#scan-results table');
+            let csv = [];
+            let rows = table.querySelectorAll('tr');
+
+            for (let i = 0; i < rows.length; i++) {
+                let row = [], cols = rows[i].querySelectorAll('td, th');
+
+                for (let j = 0; j < cols.length - 1; j++) { // 跳过最后一列（操作列）
+                    // 获取单元格的文本内容，去除HTML标签
+                    let text = cols[j].innerText.replace(/(\r\n|\n|\r)/gm, '').replace(/,/g, '，');
+                    row.push(text);
+                }
+
+                csv.push(row.join(','));
+            }
+
+            // 下载CSV文件
+            const csvString = csv.join('\n');
+            const filename = '市场扫描结果_' + new Date().toISOString().slice(0, 10) + '.csv';
+
+            const blob = new Blob(['\uFEFF' + csvString], { type: 'text/csv;charset=utf-8;' });
+            const link = document.createElement('a');
+            link.href = URL.createObjectURL(blob);
+            link.download = filename;
+
+            link.style.display = 'none';
+            document.body.appendChild(link);
+
+            link.click();
+
+            document.body.removeChild(link);
+        }
+    });
+
+
+    // 添加到script部分
+    let currentTaskId = null;  // 存储当前任务ID
+
+    // 取消按钮点击事件
+    $('#cancel-scan-btn').click(function() {
+        if (currentTaskId) {
+            cancelScan(currentTaskId);
+        } else {
+            $('#scan-loading').hide();
+            $('#scan-results').show();
+        }
+    });
+
+    // 修改启动成功处理
+    function handleStartSuccess(response) {
+        const taskId = response.task_id;
+        currentTaskId = taskId;  // 保存当前任务ID
+
+        if (!taskId) {
+            showError('启动扫描任务失败：未获取到任务ID');
+            $('#scan-loading').hide();
+            $('#scan-results').show();
+            $('#scan-error-retry').show();
+            return;
+        }
+
+        // 启动轮询任务状态
+        pollScanStatus(taskId, 0);
+    }
+
+</script>
+{% endblock %}

templates/portfolio.html

@@ -0,0 +1,605 @@
+{% extends "layout.html" %}
+
+{% block title %}投资组合 - 智能分析系统{% endblock %}
+
+{% block content %}
+<div class="container-fluid py-4">
+    <div id="alerts-container"></div>
+
+    <div class="row mb-4">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header d-flex justify-content-between">
+                    <h5 class="mb-0">我的投资组合</h5>
+                    <button class="btn btn-sm btn-outline-primary" data-bs-toggle="modal" data-bs-target="#addStockModal">
+                        <i class="fas fa-plus"></i> 添加股票
+                    </button>
+                </div>
+                <div class="card-body">
+                    <div id="portfolio-empty" class="text-center py-4">
+                        <i class="fas fa-folder-open fa-3x text-muted mb-3"></i>
+                        <p>您的投资组合还是空的，请添加股票</p>
+                        <button class="btn btn-primary" data-bs-toggle="modal" data-bs-target="#addStockModal">
+                            <i class="fas fa-plus"></i> 添加股票
+                        </button>
+                    </div>
+
+                    <div id="portfolio-content" style="display: none;">
+                        <div class="table-responsive">
+                            <table class="table table-hover">
+                                <thead>
+                                    <tr>
+                                        <th>代码</th>
+                                        <th>名称</th>
+                                        <th>行业</th>
+                                        <th>持仓比例</th>
+                                        <th>当前价格</th>
+                                        <th>今日涨跌</th>
+                                        <th>综合评分</th>
+                                        <th>建议</th>
+                                        <th>操作</th>
+                                    </tr>
+                                </thead>
+                                <tbody id="portfolio-table">
+                                    <!-- 投资组合数据将在JS中动态填充 -->
+                                </tbody>
+                            </table>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <div id="portfolio-analysis" class="row mb-4" style="display: none;">
+        <div class="col-md-6">
+            <div class="card h-100">
+                <div class="card-header">
+                    <h5 class="mb-0">投资组合评分</h5>
+                </div>
+                <div class="card-body">
+                    <div class="row">
+                        <div class="col-md-4 text-center">
+                            <div id="portfolio-score-chart"></div>
+                            <h4 id="portfolio-score" class="mt-2">--</h4>
+                            <p class="text-muted">综合评分</p>
+                        </div>
+                        <div class="col-md-8">
+                            <h5 class="mb-3">维度评分</h5>
+                            <div class="mb-3">
+                                <div class="d-flex justify-content-between mb-1">
+                                    <span>技术面</span>
+                                    <span id="technical-score">--/40</span>
+                                </div>
+                                <div class="progress">
+                                    <div id="technical-progress" class="progress-bar bg-info" role="progressbar" style="width: 0%"></div>
+                                </div>
+                            </div>
+                            <div class="mb-3">
+                                <div class="d-flex justify-content-between mb-1">
+                                    <span>基本面</span>
+                                    <span id="fundamental-score">--/40</span>
+                                </div>
+                                <div class="progress">
+                                    <div id="fundamental-progress" class="progress-bar bg-success" role="progressbar" style="width: 0%"></div>
+                                </div>
+                            </div>
+                            <div class="mb-3">
+                                <div class="d-flex justify-content-between mb-1">
+                                    <span>资金面</span>
+                                    <span id="capital-flow-score">--/20</span>
+                                </div>
+                                <div class="progress">
+                                    <div id="capital-flow-progress" class="progress-bar bg-warning" role="progressbar" style="width: 0%"></div>
+                                </div>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+        <div class="col-md-6">
+            <div class="card h-100">
+                <div class="card-header">
+                    <h5 class="mb-0">行业分布</h5>
+                </div>
+                <div class="card-body">
+                    <div id="industry-chart"></div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <div id="portfolio-recommendations" class="row mb-4" style="display: none;">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header">
+                    <h5 class="mb-0">投资建议</h5>
+                </div>
+                <div class="card-body">
+                    <ul class="list-group" id="recommendations-list">
+                        <!-- 投资建议将在JS中动态填充 -->
+                    </ul>
+                </div>
+            </div>
+        </div>
+    </div>
+</div>
+
+<!-- 添加股票模态框 -->
+<div class="modal fade" id="addStockModal" tabindex="-1" aria-labelledby="addStockModalLabel" aria-hidden="true">
+    <div class="modal-dialog">
+        <div class="modal-content">
+            <div class="modal-header">
+                <h5 class="modal-title" id="addStockModalLabel">添加股票到投资组合</h5>
+                <button type="button" class="btn-close" data-bs-dismiss="modal" aria-label="Close"></button>
+            </div>
+            <div class="modal-body">
+                <form id="add-stock-form">
+                    <div class="mb-3">
+                        <label for="add-stock-code" class="form-label">股票代码</label>
+                        <input type="text" class="form-control" id="add-stock-code" required>
+                    </div>
+                    <div class="mb-3">
+                        <label for="add-stock-weight" class="form-label">持仓比例 (%)</label>
+                        <input type="number" class="form-control" id="add-stock-weight" min="1" max="100" value="10" required>
+                    </div>
+                </form>
+            </div>
+            <div class="modal-footer">
+                <button type="button" class="btn btn-secondary" data-bs-dismiss="modal">取消</button>
+                <button type="button" class="btn btn-primary" id="add-stock-btn">添加</button>
+            </div>
+        </div>
+    </div>
+</div>
+{% endblock %}
+
+{% block scripts %}
+<script>
+    // 投资组合数据
+    let portfolio = [];
+    let portfolioAnalysis = null;
+
+    $(document).ready(function() {
+        // 从本地存储加载投资组合
+        loadPortfolio();
+
+        // 添加股票按钮点击事件
+        $('#add-stock-btn').click(function() {
+            addStockToPortfolio();
+        });
+    });
+
+    // 从本地存储加载投资组合
+    function loadPortfolio() {
+        const savedPortfolio = localStorage.getItem('portfolio');
+        if (savedPortfolio) {
+            portfolio = JSON.parse(savedPortfolio);
+            renderPortfolio();
+            analyzePortfolio();
+        }
+    }
+
+    // 渲染投资组合
+    function renderPortfolio() {
+        if (portfolio.length === 0) {
+            $('#portfolio-empty').show();
+            $('#portfolio-content').hide();
+            $('#portfolio-analysis').hide();
+            $('#portfolio-recommendations').hide();
+            return;
+        }
+
+        $('#portfolio-empty').hide();
+        $('#portfolio-content').show();
+        $('#portfolio-analysis').show();
+        $('#portfolio-recommendations').show();
+
+        let html = '';
+        portfolio.forEach((stock, index) => {
+            const scoreClass = getScoreColorClass(stock.score || 0);
+            const priceChangeClass = (stock.price_change || 0) >= 0 ? 'trend-up' : 'trend-down';
+            const priceChangeIcon = (stock.price_change || 0) >= 0 ? '<i class="fas fa-caret-up"></i>' : '<i class="fas fa-caret-down"></i>';
+
+            // 显示加载状态或实际数据
+            const stockName = stock.loading ?
+                '<span class="text-muted"><i class="fas fa-spinner fa-pulse"></i> 加载中...</span>' :
+                (stock.stock_name || '未知');
+
+            const industryDisplay = stock.industry || '-';
+
+            html += `
+                <tr>
+                    <td>${stock.stock_code}</td>
+                    <td>${stockName}</td>
+                    <td>${industryDisplay}</td>
+                    <td>${stock.weight}%</td>
+                    <td>${stock.price ? formatNumber(stock.price, 2) : '-'}</td>
+                    <td class="${priceChangeClass}">${stock.price_change ? (priceChangeIcon + ' ' + formatPercent(stock.price_change, 2)) : '-'}</td>
+                    <td><span class="badge ${scoreClass}">${stock.score || '-'}</span></td>
+                    <td>${stock.recommendation || '-'}</td>
+                    <td>
+                        <div class="btn-group btn-group-sm" role="group">
+                            <a href="/stock_detail/${stock.stock_code}" class="btn btn-outline-primary">
+                                <i class="fas fa-chart-line"></i>
+                            </a>
+                            <button type="button" class="btn btn-outline-danger" onclick="removeStock(${index})">
+                                <i class="fas fa-trash"></i>
+                            </button>
+                        </div>
+                    </td>
+                </tr>
+            `;
+        });
+
+        $('#portfolio-table').html(html);
+    }
+
+    // 添加股票到投资组合
+    function addStockToPortfolio() {
+        const stockCode = $('#add-stock-code').val().trim();
+        const weight = parseInt($('#add-stock-weight').val() || 10);
+
+        if (!stockCode) {
+            showError('请输入股票代码');
+            return;
+        }
+
+        // 检查是否已存在
+        const existingIndex = portfolio.findIndex(s => s.stock_code === stockCode);
+        if (existingIndex >= 0) {
+            showError('此股票已在投资组合中');
+            return;
+        }
+
+        // 添加到投资组合
+        portfolio.push({
+            stock_code: stockCode,
+            weight: weight,
+            stock_name: '加载中...',
+            industry: '-',
+            price: null,
+            price_change: null,
+            score: null,
+            recommendation: null,
+            loading: true  // 添加加载状态标志
+        });
+
+        // 保存到本地存储
+        savePortfolio();
+
+        // 关闭模态框
+        $('#addStockModal').modal('hide');
+
+        // 重置表单
+        $('#add-stock-form')[0].reset();
+
+        // 获取股票数据
+        fetchStockData(stockCode);
+    }
+
+    // 添加重试加载功能
+    function retryFetchStockData(stockCode) {
+        showInfo(`正在重新获取 ${stockCode} 的数据...`);
+        fetchStockData(stockCode);
+    }
+
+    // 在渲染函数中添加重试按钮
+    html += `
+        <tr>
+            <td>${stock.stock_code}</td>
+            <td>${stockName} ${stock.stock_name === '获取失败' ?
+                `<button class="btn btn-sm btn-link p-0 ml-2" onclick="retryFetchStockData('${stock.stock_code}')">
+                    <i class="fas fa-sync-alt"></i> 重试
+                </button>` : ''}
+            </td>
+            ...
+    `;
+
+    // 获取股票数据
+    function fetchStockData(stockCode) {
+        const index = portfolio.findIndex(s => s.stock_code === stockCode);
+        if (index < 0) return;
+
+        // 显示加载状态
+        portfolio[index].loading = true;
+        savePortfolio();
+        renderPortfolio();
+
+        $.ajax({
+            url: '/analyze',
+            type: 'POST',
+            contentType: 'application/json',
+            data: JSON.stringify({
+                stock_codes: [stockCode],
+                market_type: 'A'
+            }),
+            success: function(response) {
+
+                if (response.results && response.results.length > 0) {
+                    const result = response.results[0];
+
+
+                    // 确保使用null检查来处理缺失值
+                    portfolio[index].stock_name = result.stock_name || '未知';
+                    portfolio[index].industry = result.industry || '未知';
+                    portfolio[index].price = result.price || 0;
+                    portfolio[index].price_change = result.price_change || 0;
+                    portfolio[index].score = result.score || 0;
+                    portfolio[index].recommendation = result.recommendation || '-';
+                    portfolio[index].loading = false; // 清除加载状态
+
+                    // 保存更新后的投资组合
+                    savePortfolio();
+
+                    // 分析投资组合
+                    analyzePortfolio();
+
+                    showSuccess(`已添加 ${result.stock_name || stockCode} 到投资组合`);
+                } else {
+                    portfolio[index].stock_name = '数据获取失败';
+                    portfolio[index].loading = false;
+                    savePortfolio();
+                    renderPortfolio();
+                    showError(`获取股票 ${stockCode} 数据失败`);
+                }
+            },
+            error: function(error) {
+                portfolio[index].stock_name = '获取失败';
+                portfolio[index].loading = false;
+                savePortfolio();
+                renderPortfolio();
+                showError(`获取股票 ${stockCode} 数据失败`);
+            }
+        });
+    }
+
+    // 从投资组合中移除股票
+    function removeStock(index) {
+        if (confirm('确定要从投资组合中移除此股票吗？')) {
+            portfolio.splice(index, 1);
+            savePortfolio();
+            renderPortfolio();
+            analyzePortfolio();
+        }
+    }
+
+    // 保存投资组合到本地存储
+    function savePortfolio() {
+        localStorage.setItem('portfolio', JSON.stringify(portfolio));
+        renderPortfolio();
+    }
+
+
+    // 分析投资组合
+    function analyzePortfolio() {
+        if (portfolio.length === 0) return;
+
+        // 计算投资组合评分
+        let totalScore = 0;
+        let totalWeight = 0;
+        let industriesMap = {};
+
+        portfolio.forEach(stock => {
+            if (stock.score) {
+                totalScore += stock.score * stock.weight;
+                totalWeight += stock.weight;
+
+                // 统计行业分布
+                const industry = stock.industry || '其他';
+                if (industriesMap[industry]) {
+                    industriesMap[industry] += stock.weight;
+                } else {
+                    industriesMap[industry] = stock.weight;
+                }
+            }
+        });
+
+        // 确保总权重不为零
+        if (totalWeight > 0) {
+            const portfolioScore = Math.round(totalScore / totalWeight);
+
+            // 更新评分显示
+            $('#portfolio-score').text(portfolioScore);
+
+            // 简化的维度评分计算
+            const technicalScore = Math.round(portfolioScore * 0.4);
+            const fundamentalScore = Math.round(portfolioScore * 0.4);
+            const capitalFlowScore = Math.round(portfolioScore * 0.2);
+
+            $('#technical-score').text(technicalScore + '/40');
+            $('#fundamental-score').text(fundamentalScore + '/40');
+            $('#capital-flow-score').text(capitalFlowScore + '/20');
+
+            $('#technical-progress').css('width', (technicalScore / 40 * 100) + '%');
+            $('#fundamental-progress').css('width', (fundamentalScore / 40 * 100) + '%');
+            $('#capital-flow-progress').css('width', (capitalFlowScore / 20 * 100) + '%');
+
+            // 更新投资组合评分图表
+            renderPortfolioScoreChart(portfolioScore);
+
+            // 更新行业分布图表
+            renderIndustryChart(industriesMap);
+
+            // 生成投资建议
+            generateRecommendations(portfolioScore);
+        }
+    }
+
+    // 渲染投资组合评分图表
+    function renderPortfolioScoreChart(score) {
+        const options = {
+            series: [score],
+            chart: {
+                height: 150,
+                type: 'radialBar',
+            },
+            plotOptions: {
+                radialBar: {
+                    hollow: {
+                        size: '70%',
+                    },
+                    dataLabels: {
+                        show: false
+                    }
+                }
+            },
+            colors: [getScoreColor(score)],
+            stroke: {
+                lineCap: 'round'
+            }
+        };
+
+        // 清除旧图表
+        $('#portfolio-score-chart').empty();
+
+        const chart = new ApexCharts(document.querySelector("#portfolio-score-chart"), options);
+        chart.render();
+    }
+
+    // 渲染行业分布图表
+    function renderIndustryChart(industriesMap) {
+        // 转换数据格式为图表所需
+        const seriesData = [];
+        const labels = [];
+
+        for (const industry in industriesMap) {
+            if (industriesMap.hasOwnProperty(industry)) {
+                seriesData.push(industriesMap[industry]);
+                labels.push(industry);
+            }
+        }
+
+        const options = {
+            series: seriesData,
+            chart: {
+                type: 'pie',
+                height: 300
+            },
+            labels: labels,
+            responsive: [{
+                breakpoint: 480,
+                options: {
+                    chart: {
+                        height: 200
+                    },
+                    legend: {
+                        position: 'bottom'
+                    }
+                }
+            }],
+            tooltip: {
+                y: {
+                    formatter: function(value) {
+                        return value + '%';
+                    }
+                }
+            }
+        };
+
+        // 清除旧图表
+        $('#industry-chart').empty();
+
+        const chart = new ApexCharts(document.querySelector("#industry-chart"), options);
+        chart.render();
+    }
+
+    // 生成投资建议
+    function generateRecommendations(portfolioScore) {
+        let recommendations = [];
+
+        // 根据总分生成基本建议
+        if (portfolioScore >= 80) {
+            recommendations.push({
+                text: '您的投资组合整体评级优秀，当前市场环境下建议保持较高仓位',
+                type: 'success'
+            });
+        } else if (portfolioScore >= 60) {
+            recommendations.push({
+                text: '您的投资组合整体评级良好，可以考虑适度增加仓位',
+                type: 'primary'
+            });
+        } else if (portfolioScore >= 40) {
+            recommendations.push({
+                text: '您的投资组合整体评级一般，建议持币观望，等待更好的入场时机',
+                type: 'warning'
+            });
+        } else {
+            recommendations.push({
+                text: '您的投资组合整体评级较弱，建议减仓规避风险',
+                type: 'danger'
+            });
+        }
+
+        // 检查行业集中度
+        const industries = {};
+        let totalWeight = 0;
+
+        portfolio.forEach(stock => {
+            const industry = stock.industry || '其他';
+            if (industries[industry]) {
+                industries[industry] += stock.weight;
+            } else {
+                industries[industry] = stock.weight;
+            }
+            totalWeight += stock.weight;
+        });
+
+        // 计算行业集中度
+        let maxIndustryWeight = 0;
+        let maxIndustry = '';
+
+        for (const industry in industries) {
+            if (industries[industry] > maxIndustryWeight) {
+                maxIndustryWeight = industries[industry];
+                maxIndustry = industry;
+            }
+        }
+
+        const industryConcentration = maxIndustryWeight / totalWeight;
+
+        if (industryConcentration > 0.5) {
+            recommendations.push({
+                text: `行业集中度较高，${maxIndustry}行业占比${Math.round(industryConcentration * 100)}%，建议适当分散投资降低非系统性风险`,
+                type: 'warning'
+            });
+        }
+
+        // 检查需要调整的个股
+        const weakStocks = portfolio.filter(stock => stock.score && stock.score < 40);
+        if (weakStocks.length > 0) {
+            const stockNames = weakStocks.map(s => `${s.stock_name}(${s.stock_code})`).join('、');
+            recommendations.push({
+                text: `以下个股评分较低，建议考虑调整：${stockNames}`,
+                type: 'danger'
+            });
+        }
+
+        const strongStocks = portfolio.filter(stock => stock.score && stock.score > 70);
+        if (strongStocks.length > 0 && portfolioScore < 60) {
+            const stockNames = strongStocks.map(s => `${s.stock_name}(${s.stock_code})`).join('、');
+            recommendations.push({
+                text: `以下个股表现强势，可考虑增加配置比例：${stockNames}`,
+                type: 'success'
+            });
+        }
+
+        // 渲染建议
+        let html = '';
+        recommendations.forEach(rec => {
+            html += `<li class="list-group-item list-group-item-${rec.type}">${rec.text}</li>`;
+        });
+
+        $('#recommendations-list').html(html);
+    }
+
+    // 获取评分颜色
+    function getScoreColor(score) {
+        if (score >= 80) return '#28a745'; // 绿色
+        if (score >= 60) return '#007bff'; // 蓝色
+        if (score >= 40) return '#ffc107'; // 黄色
+        return '#dc3545'; // 红色
+    }
+</script>
+{% endblock %}

templates/qa.html

@@ -0,0 +1,453 @@
+{% extends "layout.html" %}
+
+{% block title %}智能问答 - 智能分析系统{% endblock %}
+
+{% block content %}
+<div class="container-fluid py-3">
+    <div id="alerts-container"></div>
+
+    <div class="row mb-3">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header py-2">
+                    <h5 class="mb-0">智能问答</h5>
+                </div>
+                <div class="card-body py-2">
+                    <form id="qa-form" class="row g-2">
+                        <div class="col-md-4">
+                            <div class="input-group input-group-sm">
+                                <span class="input-group-text">股票代码</span>
+                                <input type="text" class="form-control" id="stock-code" placeholder="例如: 600519" required>
+                            </div>
+                        </div>
+                        <div class="col-md-3">
+                            <div class="input-group input-group-sm">
+                                <span class="input-group-text">市场</span>
+                                <select class="form-select" id="market-type">
+                                    <option value="A" selected>A股</option>
+                                    <option value="HK">港股</option>
+                                    <option value="US">美股</option>
+                                </select>
+                            </div>
+                        </div>
+                        <div class="col-md-3">
+                            <button type="submit" class="btn btn-primary btn-sm w-100">
+                                <i class="fas fa-info-circle"></i> 选择股票
+                            </button>
+                        </div>
+                    </form>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <div class="row" id="chat-container" style="display: none;">
+        <div class="col-md-3">
+            <div class="card mb-3">
+                <div class="card-header py-2">
+                    <h5 class="mb-0" id="stock-info-header">股票信息</h5>
+                </div>
+                <div class="card-body">
+                    <h4 id="selected-stock-name" class="mb-1">--</h4>
+                    <p id="selected-stock-code" class="text-muted mb-3">--</p>
+                    <p class="mb-1"><span class="text-muted">行业:</span> <span id="selected-stock-industry">--</span></p>
+                    <p class="mb-1"><span class="text-muted">现价:</span> <span id="selected-stock-price">--</span></p>
+                    <p class="mb-1"><span class="text-muted">涨跌幅:</span> <span id="selected-stock-change">--</span></p>
+                    <hr class="my-3">
+                    <h6>常见问题</h6>
+                    <div class="list-group list-group-flush">
+                        <button class="list-group-item list-group-item-action common-question" data-question="这只股票的主要支撑位是多少？">主要支撑位分析</button>
+                        <button class="list-group-item list-group-item-action common-question" data-question="该股票近期的技术面走势如何？">技术面走势分析</button>
+                        <button class="list-group-item list-group-item-action common-question" data-question="这只股票的基本面情况如何？">基本面情况分析</button>
+                        <button class="list-group-item list-group-item-action common-question" data-question="该股票主力资金最近的流入情况？">主力资金流向</button>
+                        <button class="list-group-item list-group-item-action common-question" data-question="这只股票近期有哪些重要事件？">近期重要事件</button>
+                        <button class="list-group-item list-group-item-action common-question" data-question="您对这只股票有什么投资建议？">综合投资建议</button>
+                    </div>
+                </div>
+            </div>
+        </div>
+        <div class="col-md-9">
+            <div class="card mb-3">
+                <div class="card-header py-2">
+                    <h5 class="mb-0">与AI助手对话</h5>
+                </div>
+                <div class="card-body p-0">
+                    <div id="chat-messages" class="p-3" style="height: 400px; overflow-y: auto;">
+                        <div class="chat-message system-message">
+                            <div class="message-content">
+                                <p>您好！我是股票分析AI助手，请输入您想了解的关于当前股票的问题。</p>
+                            </div>
+                        </div>
+                    </div>
+                    <div class="p-3 border-top">
+                        <form id="question-form" class="d-flex">
+                            <input type="text" id="question-input" class="form-control me-2" placeholder="输入您的问题..." required>
+                            <button type="submit" class="btn btn-primary">
+                                <i class="fas fa-paper-plane"></i>
+                            </button>
+                        </form>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <div id="loading-panel" class="text-center py-5" style="display: none;">
+        <div class="spinner-border text-primary" role="status">
+            <span class="visually-hidden">Loading...</span>
+        </div>
+        <p class="mt-3 mb-0">正在获取股票数据...</p>
+    </div>
+</div>
+{% endblock %}
+
+{% block head %}
+<style>
+    .chat-message {
+        margin-bottom: 15px;
+        display: flex;
+        flex-direction: column;
+    }
+
+    .user-message {
+        align-items: flex-end;
+    }
+
+    .system-message {
+        align-items: flex-start;
+    }
+
+    .message-content {
+        max-width: 80%;
+        padding: 10px 15px;
+        border-radius: 15px;
+        position: relative;
+    }
+
+    .user-message .message-content {
+        background-color: #007bff;
+        color: white;
+        border-bottom-right-radius: 0;
+    }
+
+    .system-message .message-content {
+        background-color: #f1f1f1;
+        color: #333;
+        border-bottom-left-radius: 0;
+    }
+
+    .message-content p {
+        margin-bottom: 0.5rem;
+    }
+
+    .message-content p:last-child {
+        margin-bottom: 0;
+    }
+
+    .message-time {
+        font-size: 0.75rem;
+        color: #aaa;
+        margin-top: 4px;
+    }
+
+    .common-question {
+        padding: 0.5rem 0.75rem;
+        font-size: 0.875rem;
+    }
+
+    .keyword {
+        color: #2c7be5;
+        font-weight: 600;
+    }
+
+    .term {
+        color: #d6336c;
+        font-weight: 500;
+        padding: 0 2px;
+    }
+
+    .price {
+        color: #00a47c;
+        font-family: 'Roboto Mono', monospace;
+        background: #f3faf8;
+        padding: 2px 4px;
+        border-radius: 3px;
+    }
+
+    .trend-up {
+        color: #28a745;
+    }
+
+    .trend-down {
+        color: #dc3545;
+    }
+</style>
+{% endblock %}
+
+{% block scripts %}
+<script>
+    let selectedStock = {
+        code: '',
+        name: '',
+        market_type: 'A'
+    };
+
+    $(document).ready(function() {
+        // 选择股票表单提交
+        $('#qa-form').submit(function(e) {
+            e.preventDefault();
+            const stockCode = $('#stock-code').val().trim();
+            const marketType = $('#market-type').val();
+
+            if (!stockCode) {
+                showError('请输入股票代码！');
+                return;
+            }
+
+            selectStock(stockCode, marketType);
+        });
+
+        // 问题表单提交
+        $('#question-form').submit(function(e) {
+            e.preventDefault();
+            const question = $('#question-input').val().trim();
+
+            if (!question) {
+                return;
+            }
+
+            if (!selectedStock.code) {
+                showError('请先选择一只股票');
+                return;
+            }
+
+            addUserMessage(question);
+            $('#question-input').val('');
+            askQuestion(question);
+        });
+
+        // 常见问题点击
+        $('.common-question').click(function() {
+            const question = $(this).data('question');
+
+            if (!selectedStock.code) {
+                showError('请先选择一只股票');
+                return;
+            }
+
+            $('#question-input').val(question);
+            $('#question-form').submit();
+        });
+    });
+
+    function selectStock(stockCode, marketType) {
+        $('#loading-panel').show();
+        $('#chat-container').hide();
+
+        // 重置对话区域
+        $('#chat-messages').html(`
+            <div class="chat-message system-message">
+                <div class="message-content">
+                    <p>您好！我是股票分析AI助手，请输入您想了解的关于当前股票的问题。</p>
+                </div>
+            </div>
+        `);
+
+        // 获取股票基本信息
+        $.ajax({
+            url: '/analyze',
+            type: 'POST',
+            contentType: 'application/json',
+            data: JSON.stringify({
+                stock_codes: [stockCode],
+                market_type: marketType
+            }),
+            success: function(response) {
+                $('#loading-panel').hide();
+
+                if (response.results && response.results.length > 0) {
+                    const stockInfo = response.results[0];
+
+                    // 保存选中的股票信息
+                    selectedStock = {
+                        code: stockCode,
+                        name: stockInfo.stock_name || '未���',
+                        market_type: marketType,
+                        industry: stockInfo.industry || '未知',
+                        price: stockInfo.price || 0,
+                        price_change: stockInfo.price_change || 0
+                    };
+
+                    // 更新股票信息区域
+                    updateStockInfo();
+
+                    // 显示聊天界面
+                    $('#chat-container').show();
+
+                    // 欢迎消息
+                    addSystemMessage(`我已加载 ${selectedStock.name}(${selectedStock.code}) 的数据，您可以问我关于这只股票的问题。`);
+                } else {
+                    showError('未找到股票信息，请检查股票代码是否正确');
+                }
+            },
+            error: function(xhr, status, error) {
+                $('#loading-panel').hide();
+                let errorMsg = '获取股票信息失败';
+                if (xhr.responseJSON && xhr.responseJSON.error) {
+                    errorMsg += ': ' + xhr.responseJSON.error;
+                } else if (error) {
+                    errorMsg += ': ' + error;
+                }
+                showError(errorMsg);
+            }
+        });
+    }
+
+    function updateStockInfo() {
+        // 更新股票信息区域
+        $('#stock-info-header').text(selectedStock.name);
+        $('#selected-stock-name').text(selectedStock.name);
+        $('#selected-stock-code').text(selectedStock.code);
+        $('#selected-stock-industry').text(selectedStock.industry);
+        $('#selected-stock-price').text('¥' + formatNumber(selectedStock.price, 2));
+
+        const priceChangeClass = selectedStock.price_change >= 0 ? 'trend-up' : 'trend-down';
+        const priceChangeIcon = selectedStock.price_change >= 0 ? '<i class="fas fa-caret-up"></i> ' : '<i class="fas fa-caret-down"></i> ';
+        $('#selected-stock-change').html(`<span class="${priceChangeClass}">${priceChangeIcon}${formatPercent(selectedStock.price_change, 2)}</span>`);
+    }
+
+    function askQuestion(question) {
+        // 显示思考中消息
+        const thinkingMessageId = 'thinking-' + Date.now();
+        addSystemMessage('<i class="fas fa-spinner fa-pulse"></i> 正在思考...', thinkingMessageId);
+
+        // 发送问题到API
+        $.ajax({
+            url: '/api/qa',
+            type: 'POST',
+            contentType: 'application/json',
+            data: JSON.stringify({
+                stock_code: selectedStock.code,
+                question: question,
+                market_type: selectedStock.market_type
+            }),
+            success: function(response) {
+                // 移除思考中消息
+                $(`#${thinkingMessageId}`).remove();
+
+                // 添加回答
+                addSystemMessage(formatAnswer(response.answer));
+
+                // 滚动到底部
+                scrollToBottom();
+            },
+            error: function(xhr, status, error) {
+                // 移除思考中消息
+                $(`#${thinkingMessageId}`).remove();
+
+                // 添加错误消息
+                let errorMsg = '无法回答您的问题';
+                if (xhr.responseJSON && xhr.responseJSON.error) {
+                    errorMsg += ': ' + xhr.responseJSON.error;
+                } else if (error) {
+                    errorMsg += ': ' + error;
+                }
+
+                addSystemMessage(`<span class="text-danger">${errorMsg}</span>`);
+
+                // 滚动到底部
+                scrollToBottom();
+            }
+        });
+    }
+
+    function addUserMessage(message) {
+        const time = new Date().toLocaleTimeString();
+
+        const messageHtml = `
+            <div class="chat-message user-message">
+                <div class="message-content">
+                    <p>${message}</p>
+                </div>
+                <div class="message-time">${time}</div>
+            </div>
+        `;
+
+        $('#chat-messages').append(messageHtml);
+        scrollToBottom();
+    }
+
+    function addSystemMessage(message, id = null) {
+        const time = new Date().toLocaleTimeString();
+        const idAttribute = id ? `id="${id}"` : '';
+
+        const messageHtml = `
+            <div class="chat-message system-message" ${idAttribute}>
+                <div class="message-content">
+                    <p>${message}</p>
+                </div>
+                <div class="message-time">${time}</div>
+            </div>
+        `;
+
+        $('#chat-messages').append(messageHtml);
+        scrollToBottom();
+    }
+
+    function scrollToBottom() {
+        const chatContainer = document.getElementById('chat-messages');
+        chatContainer.scrollTop = chatContainer.scrollHeight;
+    }
+
+    function formatAnswer(text) {
+        if (!text) return '';
+
+        // First, make the text safe for HTML
+        const safeText = text
+            .replace(/&/g, '&amp;')
+            .replace(/</g, '&lt;')
+            .replace(/>/g, '&gt;');
+
+        // Replace basic Markdown elements
+        let formatted = safeText
+            // Bold text with ** or __
+            .replace(/\*\*(.*?)\*\*/g, '<strong class="keyword">$1</strong>')
+            .replace(/__(.*?)__/g, '<strong>$1</strong>')
+
+            // Italic text with * or _
+            .replace(/\*(.*?)\*/g, '<em>$1</em>')
+            .replace(/_(.*?)_/g, '<em>$1</em>')
+
+            // Headers - only h4, h5, h6 for chat
+            .replace(/^#### (.*?)$/gm, '<h6>$1</h6>')
+            .replace(/^### (.*?)$/gm, '<h6>$1</h6>')
+            .replace(/^## (.*?)$/gm, '<h6>$1</h6>')
+            .replace(/^# (.*?)$/gm, '<h6>$1</h6>')
+
+            // Apply special styling to financial terms
+            .replace(/支撑位/g, '<span class="keyword">支撑位</span>')
+            .replace(/压力位/g, '<span class="keyword">压力位</span>')
+            .replace(/趋势/g, '<span class="keyword">趋势</span>')
+            .replace(/均线/g, '<span class="keyword">均线</span>')
+            .replace(/MACD/g, '<span class="term">MACD</span>')
+            .replace(/RSI/g, '<span class="term">RSI</span>')
+            .replace(/KDJ/g, '<span class="term">KDJ</span>')
+
+            // Highlight price patterns and movements
+            .replace(/([上涨升])/g, '<span class="trend-up">$1</span>')
+            .replace(/([下跌降])/g, '<span class="trend-down">$1</span>')
+            .replace(/(买入|做多|多头|突破)/g, '<span class="trend-up">$1</span>')
+            .replace(/(卖出|做空|空头|跌破)/g, '<span class="trend-down">$1</span>')
+
+            // Highlight price values (matches patterns like 31.25, 120.50)
+            .replace(/(\d+\.\d{2})/g, '<span class="price">$1</span>')
+
+            // Convert line breaks to paragraph tags
+            .replace(/\n\n+/g, '</p><p class="mb-2">')
+            .replace(/\n/g, '<br>');
+
+        // Wrap in paragraph tags for consistent styling
+        return '<p class="mb-2">' + formatted + '</p>';
+    }
+</script>
+{% endblock %}

templates/risk_monitor.html

@@ -0,0 +1,1041 @@
+{% extends "layout.html" %}
+
+{% block title %}风险监控 - 智能分析系统{% endblock %}
+
+{% block content %}
+<div class="container-fluid py-3">
+    <div id="alerts-container"></div>
+
+    <div class="row mb-3">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header py-2">
+                    <h5 class="mb-0">风险监控</h5>
+                </div>
+                <div class="card-body py-2">
+                    <ul class="nav nav-tabs" id="risk-tabs" role="tablist">
+                        <li class="nav-item" role="presentation">
+                            <button class="nav-link active" id="stock-risk-tab" data-bs-toggle="tab" data-bs-target="#stock-risk" type="button" role="tab" aria-controls="stock-risk" aria-selected="true">个股风险</button>
+                        </li>
+                        <li class="nav-item" role="presentation">
+                            <button class="nav-link" id="portfolio-risk-tab" data-bs-toggle="tab" data-bs-target="#portfolio-risk" type="button" role="tab" aria-controls="portfolio-risk" aria-selected="false">组合风险</button>
+                        </li>
+                    </ul>
+                    <div class="tab-content mt-3" id="risk-tabs-content">
+                        <div class="tab-pane fade show active" id="stock-risk" role="tabpanel" aria-labelledby="stock-risk-tab">
+                            <form id="stock-risk-form" class="row g-2">
+                                <div class="col-md-4">
+                                    <div class="input-group input-group-sm">
+                                        <span class="input-group-text">股票代码</span>
+                                        <input type="text" class="form-control" id="stock-code" placeholder="例如: 600519" required>
+                                    </div>
+                                </div>
+                                <div class="col-md-3">
+                                    <div class="input-group input-group-sm">
+                                        <span class="input-group-text">市场</span>
+                                        <select class="form-select" id="market-type">
+                                            <option value="A" selected>A股</option>
+                                            <option value="HK">港股</option>
+                                            <option value="US">美股</option>
+                                        </select>
+                                    </div>
+                                </div>
+                                <div class="col-md-3">
+                                    <button type="submit" class="btn btn-primary btn-sm w-100">
+                                        <i class="fas fa-search"></i> 分析风险
+                                    </button>
+                                </div>
+                            </form>
+                        </div>
+                        <div class="tab-pane fade" id="portfolio-risk" role="tabpanel" aria-labelledby="portfolio-risk-tab">
+                            <div class="alert alert-info">
+                                <i class="fas fa-info-circle"></i> 分析投资组合的整体风险，识别高风险股票和风险集中度。
+                            </div>
+                            <button id="analyze-portfolio-btn" class="btn btn-primary btn-sm">
+                                <i class="fas fa-briefcase"></i> 分析我的投资组合
+                            </button>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <div id="loading-panel" class="text-center py-5" style="display: none;">
+        <div class="spinner-border text-primary" role="status">
+            <span class="visually-hidden">Loading...</span>
+        </div>
+        <p class="mt-3 mb-0">正在分析风险，请稍候...</p>
+    </div>
+
+    <!-- 个股风险分析结果 -->
+    <div id="stock-risk-result" style="display: none;">
+        <div class="row g-3 mb-3">
+            <div class="col-md-6">
+                <div class="card h-100">
+                    <div class="card-header py-2 d-flex justify-content-between align-items-center">
+                        <h5 class="mb-0">风险概览</h5>
+                        <span id="risk-level-badge" class="badge"></span>
+                    </div>
+                    <div class="card-body">
+                        <div class="row mb-3">
+                            <div class="col-md-7">
+                                <h3 id="stock-name" class="mb-0 fs-4"></h3>
+                                <p id="stock-info" class="text-muted mb-0 small"></p>
+                            </div>
+                            <div class="col-md-5 text-end">
+                                <div id="risk-gauge-chart" style="height: 120px;"></div>
+                            </div>
+                        </div>
+                        <div class="row">
+                            <div class="col-12">
+                                <h6>风险预警</h6>
+                                <div id="risk-alerts" class="mt-2">
+                                    <!-- 风险预警内容将在JS中动态填充 -->
+                                </div>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+            <div class="col-md-6">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">风险构成</h5>
+                    </div>
+                    <div class="card-body">
+                        <div id="risk-radar-chart" style="height: 220px;"></div>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <div class="row g-3 mb-3">
+            <div class="col-md-6">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">波动率风险</h5>
+                    </div>
+                    <div class="card-body">
+                        <div class="row">
+                            <div class="col-md-6">
+                                <h6>波动率指标</h6>
+                                <p><span class="text-muted">当前波动率:</span> <span id="current-volatility" class="fw-bold"></span></p>
+                                <p><span class="text-muted">变化率:</span> <span id="volatility-change" class="fw-bold"></span></p>
+                                <p><span class="text-muted">风险等级:</span> <span id="volatility-risk-level" class="fw-bold"></span></p>
+                                <p class="small text-muted" id="volatility-comment"></p>
+                            </div>
+                            <div class="col-md-6">
+                                <div id="volatility-chart" style="height: 150px;"></div>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+            <div class="col-md-6">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">趋势风险</h5>
+                    </div>
+                    <div class="card-body">
+                        <div class="row">
+                            <div class="col-md-6">
+                                <h6>趋势指标</h6>
+                                <p><span class="text-muted">当前趋势:</span> <span id="current-trend" class="fw-bold"></span></p>
+                                <p><span class="text-muted">均线关系:</span> <span id="ma-relationship" class="fw-bold"></span></p>
+                                <p><span class="text-muted">风险等级:</span> <span id="trend-risk-level" class="fw-bold"></span></p>
+                                <p class="small text-muted" id="trend-comment"></p>
+                            </div>
+                            <div class="col-md-6">
+                                <div id="trend-chart" style="height: 150px;"></div>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <div class="row g-3 mb-3">
+            <div class="col-md-6">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">反转风险</h5>
+                    </div>
+                    <div class="card-body">
+                        <div class="row">
+                            <div class="col-md-6">
+                                <h6>反转信号</h6>
+                                <p><span class="text-muted">反转信号数:</span> <span id="reversal-signals" class="fw-bold"></span></p>
+                                <p><span class="text-muted">可能方向:</span> <span id="reversal-direction" class="fw-bold"></span></p>
+                                <p><span class="text-muted">风险等级:</span> <span id="reversal-risk-level" class="fw-bold"></span></p>
+                                <p class="small text-muted" id="reversal-comment"></p>
+                            </div>
+                            <div class="col-md-6">
+                                <div id="reversal-chart" style="height: 150px;"></div>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+            <div class="col-md-6">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">成交量风险</h5>
+                    </div>
+                    <div class="card-body">
+                        <div class="row">
+                            <div class="col-md-6">
+                                <h6>成交量指标</h6>
+                                <p><span class="text-muted">成交量比率:</span> <span id="volume-ratio" class="fw-bold"></span></p>
+                                <p><span class="text-muted">成交量模式:</span> <span id="volume-pattern" class="fw-bold"></span></p>
+                                <p><span class="text-muted">风险等级:</span> <span id="volume-risk-level" class="fw-bold"></span></p>
+                                <p class="small text-muted" id="volume-comment"></p>
+                            </div>
+                            <div class="col-md-6">
+                                <div id="volume-chart" style="height: 150px;"></div>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <!-- 投资组合风险分析结果 -->
+    <div id="portfolio-risk-result" style="display: none;">
+        <div class="row g-3 mb-3">
+            <div class="col-md-6">
+                <div class="card h-100">
+                    <div class="card-header py-2 d-flex justify-content-between align-items-center">
+                        <h5 class="mb-0">组合风险概览</h5>
+                        <span id="portfolio-risk-level-badge" class="badge"></span>
+                    </div>
+                    <div class="card-body">
+                        <div class="row mb-3">
+                            <div class="col-md-7">
+                                <h3 class="mb-0 fs-4">我的投资组合</h3>
+                                <p class="text-muted mb-0 small">包含 <span id="portfolio-stock-count">0</span> 只股票</p>
+                            </div>
+                            <div class="col-md-5 text-end">
+                                <div id="portfolio-risk-gauge-chart" style="height: 120px;"></div>
+                            </div>
+                        </div>
+                        <div class="row">
+                            <div class="col-12">
+                                <h6>风险集中度</h6>
+                                <p><span class="text-muted">行业集中度:</span> <span id="industry-concentration" class="fw-bold"></span></p>
+                                <p><span class="text-muted">高风险股票占比:</span> <span id="high-risk-concentration" class="fw-bold"></span></p>
+                                <div id="portfolio-risk-alerts" class="mt-2">
+                                    <!-- 风险预警内容将在JS中动态填充 -->
+                                </div>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+            <div class="col-md-6">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">行业分布</h5>
+                    </div>
+                    <div class="card-body">
+                        <div id="industry-distribution-chart" style="height: 220px;"></div>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <div class="row g-3 mb-3">
+            <div class="col-12">
+                <div class="card">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">高风险股票</h5>
+                    </div>
+                    <div class="card-body p-0">
+                        <div class="table-responsive">
+                            <table class="table table-sm table-striped table-hover mb-0">
+                                <thead>
+                                    <tr>
+                                        <th>代码</th>
+                                        <th>名称</th>
+                                        <th>风险评分</th>
+                                        <th>风险等级</th>
+                                        <th>权重</th>
+                                        <th>主要风险</th>
+                                        <th>操作</th>
+                                    </tr>
+                                </thead>
+                                <tbody id="high-risk-stocks-table">
+                                    <!-- 高风险股票列表将在JS中动态填充 -->
+                                </tbody>
+                            </table>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <div class="row g-3 mb-3">
+            <div class="col-12">
+                <div class="card">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">风险预警列表</h5>
+                    </div>
+                    <div class="card-body p-0">
+                        <div class="table-responsive">
+                            <table class="table table-sm table-striped table-hover mb-0">
+                                <thead>
+                                    <tr>
+                                        <th>代码</th>
+                                        <th>名称</th>
+                                        <th>预警类型</th>
+                                        <th>风险等级</th>
+                                        <th>预警信息</th>
+                                    </tr>
+                                </thead>
+                                <tbody id="risk-alerts-table">
+                                    <!-- 风险预警列表将在JS中动态填充 -->
+                                </tbody>
+                            </table>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+</div>
+{% endblock %}
+
+{% block scripts %}
+<script>
+    $(document).ready(function() {
+        // 个股风险分析表单提交
+        $('#stock-risk-form').submit(function(e) {
+            e.preventDefault();
+            const stockCode = $('#stock-code').val().trim();
+            const marketType = $('#market-type').val();
+
+            if (!stockCode) {
+                showError('请输入股票代码！');
+                return;
+            }
+
+            analyzeStockRisk(stockCode, marketType);
+        });
+
+        // 分析投资组合风险按钮点击
+        $('#analyze-portfolio-btn').click(function() {
+            analyzePortfolioRisk();
+        });
+    });
+
+    function analyzeStockRisk(stockCode, marketType) {
+        $('#loading-panel').show();
+        $('#stock-risk-result').hide();
+        $('#portfolio-risk-result').hide();
+
+        $.ajax({
+            url: '/api/risk_analysis',
+            type: 'POST',
+            contentType: 'application/json',
+            data: JSON.stringify({
+                stock_code: stockCode,
+                market_type: marketType
+            }),
+            success: function(response) {
+                $('#loading-panel').hide();
+                renderStockRiskAnalysis(response, stockCode);
+                $('#stock-risk-result').show();
+            },
+            error: function(xhr, status, error) {
+                $('#loading-panel').hide();
+                let errorMsg = '获取风险分析数据失败';
+                if (xhr.responseJSON && xhr.responseJSON.error) {
+                    errorMsg += ': ' + xhr.responseJSON.error;
+                } else if (error) {
+                    errorMsg += ': ' + error;
+                }
+                showError(errorMsg);
+            }
+        });
+    }
+
+    function analyzePortfolioRisk() {
+        // 尝试从本地存储获取投资组合数据
+        const savedPortfolio = localStorage.getItem('portfolio');
+        if (!savedPortfolio) {
+            showError('您的投资组合为空，请先添加股票到投资组合');
+            return;
+        }
+
+        const portfolio = JSON.parse(savedPortfolio);
+        if (portfolio.length === 0) {
+            showError('您的投资组合为空，请先添加股票到投资组合');
+            return;
+        }
+
+        $('#loading-panel').show();
+        $('#stock-risk-result').hide();
+        $('#portfolio-risk-result').hide();
+
+        $.ajax({
+            url: '/api/portfolio_risk',
+            type: 'POST',
+            contentType: 'application/json',
+            data: JSON.stringify({
+                portfolio: portfolio
+            }),
+            success: function(response) {
+                $('#loading-panel').hide();
+                renderPortfolioRiskAnalysis(response, portfolio);
+                $('#portfolio-risk-result').show();
+            },
+            error: function(xhr, status, error) {
+                $('#loading-panel').hide();
+                let errorMsg = '获取投资组合风险分析数据失败';
+                if (xhr.responseJSON && xhr.responseJSON.error) {
+                    errorMsg += ': ' + xhr.responseJSON.error;
+                } else if (error) {
+                    errorMsg += ': ' + error;
+                }
+                showError(errorMsg);
+            }
+        });
+    }
+
+    function renderStockRiskAnalysis(data, stockCode) {
+        // 设置基本信息
+        $('#stock-name').text(data.stock_name || stockCode);
+        $('#stock-info').text(data.industry || '未知行业');
+
+        // 设置风险等级
+        const riskScore = data.total_risk_score || 0;
+        const riskLevel = data.risk_level || '未知';
+        const riskLevelBadgeClass = getRiskLevelBadgeClass(riskLevel);
+        $('#risk-level-badge').text(riskLevel).removeClass().addClass(`badge ${riskLevelBadgeClass}`);
+
+        // 设置风险预警
+        renderRiskAlerts(data.alerts || []);
+
+        // 渲染风险仪表图
+        renderRiskGaugeChart(riskScore);
+
+        // 渲染风险雷达图
+        renderRiskRadarChart(data);
+
+        // 设置波动率风险
+        const volatilityRisk = data.volatility_risk || {};
+        $('#current-volatility').text(formatPercent(volatilityRisk.value || 0, 2));
+
+        const volatilityChange = volatilityRisk.change || 0;
+        const volatilityChangeClass = volatilityChange >= 0 ? 'trend-up' : 'trend-down';
+        $('#volatility-change').text(formatPercent(volatilityChange, 2)).addClass(volatilityChangeClass);
+
+        $('#volatility-risk-level').text(volatilityRisk.risk_level || '未知');
+        $('#volatility-comment').text('波动率反映价格波动的剧烈程度，高波动率意味着高风险');
+
+        // 设置趋势风险
+        const trendRisk = data.trend_risk || {};
+        $('#current-trend').text(trendRisk.trend || '未知');
+        $('#ma-relationship').text(trendRisk.ma_relationship || '未知');
+        $('#trend-risk-level').text(trendRisk.risk_level || '未知');
+        $('#trend-comment').text('下降趋势中的股票有更高的风险，特别是在空头排列时');
+
+        // 设置反转风险
+        const reversalRisk = data.reversal_risk || {};
+        $('#reversal-signals').text(reversalRisk.reversal_signals || 0);
+        $('#reversal-direction').text(reversalRisk.direction || '未知');
+        $('#reversal-risk-level').text(reversalRisk.risk_level || '未知');
+        $('#reversal-comment').text('多个技术指标同时出现反转信号，意味着趋势可能即将改变');
+
+        // 设置成交量风险
+        const volumeRisk = data.volume_risk || {};
+        $('#volume-ratio').text(formatNumber(volumeRisk.volume_ratio || 0, 2));
+        $('#volume-pattern').text(volumeRisk.pattern || '未知');
+        $('#volume-risk-level').text(volumeRisk.risk_level || '未知');
+        $('#volume-comment').text('成交量异常变化常常预示价格波动，尤其是量价背离时');
+
+        // 渲染各个风险维度的图表
+        renderVolatilityChart([5.2, 3.8, 4.5, 7.2, 6.3]);
+        renderTrendChart([110, 108, 106, 102, 98]);
+        renderReversalChart([55, 60, 65, 72, 68]);
+        renderVolumeChart([1.2, 0.8, 1.5, 2.8, 2.1]);
+    }
+
+    function renderPortfolioRiskAnalysis(data, portfolio) {
+        // 设置基本信息
+        $('#portfolio-stock-count').text(portfolio.length);
+
+        // 设置风险等级
+        const riskScore = data.portfolio_risk_score || 0;
+        const riskLevel = data.risk_level || '未知';
+        const riskLevelBadgeClass = getRiskLevelBadgeClass(riskLevel);
+        $('#portfolio-risk-level-badge').text(riskLevel).removeClass().addClass(`badge ${riskLevelBadgeClass}`);
+
+        // 设置风险集中度
+        const riskConcentration = data.risk_concentration || {};
+        $('#industry-concentration').text(`${riskConcentration.max_industry || '未知'} (${formatPercent(riskConcentration.max_industry_weight || 0, 1)})`);
+        $('#high-risk-concentration').text(formatPercent(riskConcentration.high_risk_weight || 0, 1));
+
+        // 设置风险预警
+        renderPortfolioRiskAlerts(data.alerts || []);
+
+        // 渲染投资组合风险仪表图
+        renderPortfolioRiskGaugeChart(riskScore);
+
+        // 渲染行业分布图
+        renderIndustryDistributionChart(portfolio);
+
+        // 渲染高风险股票列表
+        renderHighRiskStocksTable(data.high_risk_stocks || []);
+
+        // 渲染风险预警列表
+        renderRiskAlertsTable(data.alerts || []);
+    }
+
+    function renderRiskAlerts(alerts) {
+        let html = '';
+
+        if (alerts.length === 0) {
+            html = '<div class="alert alert-success">未发现显著风险因素</div>';
+        } else {
+            alerts.forEach(alert => {
+                const alertClass = getRiskAlertClass(alert.level);
+                html += `
+                    <div class="alert ${alertClass} mb-2">
+                        <strong>${alert.type}风险:</strong> ${alert.message}
+                    </div>
+                `;
+            });
+        }
+
+        $('#risk-alerts').html(html);
+    }
+
+    function renderPortfolioRiskAlerts(alerts) {
+        let html = '';
+
+        if (alerts.length === 0) {
+            html = '<div class="alert alert-success">投资组合风险均衡，未发现显著风险集中</div>';
+        } else {
+            let alertCount = 0;
+            alerts.forEach(alert => {
+                if (alertCount < 3) { // 只显示前3条
+                    const alertClass = getRiskAlertClass(alert.level);
+                    html += `
+                        <div class="alert ${alertClass} mb-2">
+                            <strong>${alert.stock_code}:</strong> ${alert.message}
+                        </div>
+                    `;
+                    alertCount++;
+                }
+            });
+
+            if (alerts.length > 3) {
+                html += `<p class="text-muted small">还有 ${alerts.length - 3} 条风险预警，请查看下方详情表格</p>`;
+            }
+        }
+
+        $('#portfolio-risk-alerts').html(html);
+    }
+
+    function renderRiskGaugeChart(score) {
+        const options = {
+            series: [score],
+            chart: {
+                height: 120,
+                type: 'radialBar',
+            },
+            plotOptions: {
+                radialBar: {
+                    hollow: {
+                        size: '70%',
+                    },
+                    dataLabels: {
+                        show: true,
+                        name: {
+                            show: false
+                        },
+                        value: {
+                            fontSize: '16px',
+                            fontWeight: 'bold',
+                            offsetY: 5
+                        }
+                    },
+                    track: {
+                        background: '#f2f2f2'
+                    }
+                }
+            },
+            fill: {
+                colors: [getRiskColor(score)]
+            },
+            labels: ['风险分数']
+        };
+
+        const chart = new ApexCharts(document.querySelector("#risk-gauge-chart"), options);
+        chart.render();
+    }
+
+    function renderPortfolioRiskGaugeChart(score) {
+        const options = {
+            series: [score],
+            chart: {
+                height: 120,
+                type: 'radialBar',
+            },
+            plotOptions: {
+                radialBar: {
+                    hollow: {
+                        size: '70%',
+                    },
+                    dataLabels: {
+                        show: true,
+                        name: {
+                            show: false
+                        },
+                        value: {
+                            fontSize: '16px',
+                            fontWeight: 'bold',
+                            offsetY: 5
+                        }
+                    },
+                    track: {
+                        background: '#f2f2f2'
+                    }
+                }
+            },
+            fill: {
+                colors: [getRiskColor(score)]
+            },
+            labels: ['风险分数']
+        };
+
+        const chart = new ApexCharts(document.querySelector("#portfolio-risk-gauge-chart"), options);
+        chart.render();
+    }
+
+    function renderRiskRadarChart(data) {
+        const volatilityRisk = data.volatility_risk?.score || 0;
+        const trendRisk = data.trend_risk?.score || 0;
+        const reversalRisk = data.reversal_risk?.score || 0;
+        const volumeRisk = data.volume_risk?.score || 0;
+
+        const options = {
+            series: [{
+                name: '风险评分',
+                data: [volatilityRisk, trendRisk, reversalRisk, volumeRisk]
+            }],
+            chart: {
+                height: 220,
+                type: 'radar',
+                toolbar: {
+                    show: false
+                }
+            },
+            xaxis: {
+                categories: ['波动率风险', '趋势风险', '反转风险', '成交量风险']
+            },
+            fill: {
+                opacity: 0.7,
+                colors: ['#dc3545']
+            },
+            markers: {
+                size: 4
+            },
+            title: {
+                text: '风险雷达图',
+                align: 'center',
+                style: {
+                    fontSize: '14px'
+                }
+            }
+        };
+
+        const chart = new ApexCharts(document.querySelector("#risk-radar-chart"), options);
+        chart.render();
+    }
+
+    function renderIndustryDistributionChart(portfolio) {
+        // 根据投资组合计算行业分布
+        const industries = {};
+        let totalWeight = 0;
+
+        portfolio.forEach(stock => {
+            const industry = stock.industry || '未知';
+            const weight = stock.weight || 1;
+
+            if (industries[industry]) {
+                industries[industry] += weight;
+            } else {
+                industries[industry] = weight;
+            }
+
+            totalWeight += weight;
+        });
+
+        const series = [];
+        const labels = [];
+
+        for (const industry in industries) {
+            if (industries.hasOwnProperty(industry)) {
+                series.push(industries[industry]);
+                labels.push(industry);
+            }
+        }
+
+        const options = {
+            series: series,
+            chart: {
+                height: 220,
+                type: 'pie',
+            },
+            labels: labels,
+            colors: ['#4e73df', '#1cc88a', '#36b9cc', '#f6c23e', '#e74a3b', '#858796', '#5a5c69', '#6f42c1'],
+            legend: {
+                position: 'bottom'
+            },
+            tooltip: {
+                y: {
+                    formatter: function(value) {
+                        return value + ' (' + ((value / totalWeight) * 100).toFixed(1) + '%)';
+                    }
+                }
+            }
+        };
+
+        const chart = new ApexCharts(document.querySelector("#industry-distribution-chart"), options);
+        chart.render();
+    }
+
+    function renderVolatilityChart(data) {
+        const options = {
+            series: [{
+                name: '波动率(%)',
+                data: data
+            }],
+            chart: {
+                height: 150,
+                type: 'line',
+                toolbar: {
+                    show: false
+                }
+            },
+            stroke: {
+                curve: 'smooth',
+                width: 3
+            },
+            xaxis: {
+                labels: {
+                    show: false
+                }
+            },
+            yaxis: {
+                labels: {
+                    formatter: function(val) {
+                        return val.toFixed(1) + '%';
+                    },
+                    style: {
+                        fontSize: '10px'
+                    }
+                }
+            },
+            colors: ['#dc3545'],
+            tooltip: {
+                y: {
+                    formatter: function(value) {
+                        return value.toFixed(2) + '%';
+                    }
+                }
+            },
+            markers: {
+                size: 3
+            }
+        };
+
+        const chart = new ApexCharts(document.querySelector("#volatility-chart"), options);
+        chart.render();
+    }
+
+    function renderTrendChart(data) {
+        const options = {
+            series: [{
+                name: '价格',
+                data: data
+            }, {
+                name: 'MA20',
+                data: [109, 107, 105, 103, 101]
+            }],
+            chart: {
+                height: 150,
+                type: 'line',
+                toolbar: {
+                    show: false
+                }
+            },
+            stroke: {
+                curve: 'straight',
+                width: [3, 2]
+            },
+            xaxis: {
+                labels: {
+                    show: false
+                }
+            },
+            yaxis: {
+                labels: {
+                    formatter: function(val) {
+                        return val.toFixed(0);
+                    },
+                    style: {
+                        fontSize: '10px'
+                    }
+                }
+            },
+            colors: ['#dc3545', '#007bff'],
+            tooltip: {
+                y: {
+                    formatter: function(value) {
+                        return value.toFixed(2);
+                    }
+                }
+            },
+            markers: {
+                size: 3
+            },
+            legend: {
+                show: false
+            }
+        };
+
+        const chart = new ApexCharts(document.querySelector("#trend-chart"), options);
+        chart.render();
+    }
+
+    function renderReversalChart(data) {
+        const options = {
+            series: [{
+                name: 'RSI',
+                data: data
+            }],
+            chart: {
+                height: 150,
+                type: 'line',
+                toolbar: {
+                    show: false
+                }
+            },
+            stroke: {
+                curve: 'smooth',
+                width: 3
+            },
+            xaxis: {
+                labels: {
+                    show: false
+                }
+            },
+            yaxis: {
+                min: 0,
+                max: 100,
+                labels: {
+                    formatter: function(val) {
+                        return val.toFixed(0);
+                    },
+                    style: {
+                        fontSize: '10px'
+                    }
+                }
+            },
+            colors: ['#ffc107'],
+            tooltip: {
+                y: {
+                    formatter: function(value) {
+                        return value.toFixed(2);
+                    }
+                }
+            },
+            markers: {
+                size: 3
+            },
+            annotations: {
+                yaxis: [{
+                    y: 70,
+                    borderColor: '#dc3545',
+                    label: {
+                        text: '超买',
+                        style: {
+                            color: '#fff',
+                            background: '#dc3545'
+                        }
+                    }
+                }, {
+                    y: 30,
+                    borderColor: '#28a745',
+                    label: {
+                        text: '超卖',
+                        style: {
+                            color: '#fff',
+                            background: '#28a745'
+                        }
+                    }
+                }]
+            }
+        };
+
+        const chart = new ApexCharts(document.querySelector("#reversal-chart"), options);
+        chart.render();
+    }
+
+    function renderVolumeChart(data) {
+        const options = {
+            series: [{
+                name: '成交量比率',
+                data: data
+            }],
+            chart: {
+                height: 150,
+                type: 'bar',
+                toolbar: {
+                    show: false
+                }
+            },
+            xaxis: {
+                labels: {
+                    show: false
+                }
+            },
+            yaxis: {
+                labels: {
+                    formatter: function(val) {
+                        return val.toFixed(1) + 'x';
+                    },
+                    style: {
+                        fontSize: '10px'
+                    }
+                }
+            },
+            colors: ['#4e73df'],
+            tooltip: {
+                y: {
+                    formatter: function(value) {
+                        return value.toFixed(2) + 'x';
+                    }
+                }
+            },
+            plotOptions: {
+                bar: {
+                    columnWidth: '50%'
+                }
+            },
+            dataLabels: {
+                enabled: false
+            }
+        };
+
+        const chart = new ApexCharts(document.querySelector("#volume-chart"), options);
+        chart.render();
+    }
+
+    function renderHighRiskStocksTable(highRiskStocks) {
+        let html = '';
+
+        if (highRiskStocks.length === 0) {
+            html = '<tr><td colspan="7" class="text-center">未发现高风险股票</td></tr>';
+        } else {
+            highRiskStocks.forEach(stock => {
+                const riskScoreClass = getRiskScoreClass(stock.risk_score);
+                const riskLevelBadgeClass = getRiskLevelBadgeClass(stock.risk_level);
+
+                html += `
+                    <tr>
+                        <td>${stock.stock_code}</td>
+                        <td>${stock.stock_name || '未知'}</td>
+                        <td><span class="badge ${riskScoreClass}">${stock.risk_score}</span></td>
+                        <td><span class="badge ${riskLevelBadgeClass}">${stock.risk_level}</span></td>
+                        <td>${formatPercent(stock.weight || 0, 1)}</td>
+                        <td>${stock.main_risk || '未知'}</td>
+                        <td>
+                            <a href="/stock_detail/${stock.stock_code}" class="btn btn-sm btn-outline-info me-1">
+                                <i class="fas fa-chart-line"></i>
+                            </a>
+                            <a href="/risk_monitor?stock=${stock.stock_code}" class="btn btn-sm btn-outline-danger">
+                                <i class="fas fa-exclamation-triangle"></i>
+                            </a>
+                        </td>
+                    </tr>
+                `;
+            });
+        }
+
+        $('#high-risk-stocks-table').html(html);
+    }
+
+    function renderRiskAlertsTable(alerts) {
+        let html = '';
+
+        if (alerts.length === 0) {
+            html = '<tr><td colspan="5" class="text-center">暂无风险预警</td></tr>';
+        } else {
+            alerts.forEach(alert => {
+                const riskLevelBadgeClass = getRiskLevelBadgeClass(alert.level);
+
+                html += `
+                    <tr>
+                        <td>${alert.stock_code}</td>
+                        <td>${alert.stock_name || '未知'}</td>
+                        <td>${alert.type}</td>
+                        <td><span class="badge ${riskLevelBadgeClass}">${alert.level}</span></td>
+                        <td>${alert.message}</td>
+                    </tr>
+                `;
+            });
+        }
+
+        $('#risk-alerts-table').html(html);
+    }
+
+    function getRiskLevelBadgeClass(level) {
+        switch (level) {
+            case '极高':
+                return 'bg-danger';
+            case '高':
+                return 'bg-warning text-dark';
+            case '中等':
+                return 'bg-info text-dark';
+            case '低':
+                return 'bg-success';
+            case '极低':
+                return 'bg-secondary';
+            default:
+                return 'bg-secondary';
+        }
+    }
+
+    function getRiskAlertClass(level) {
+        switch (level) {
+            case '高':
+                return 'alert-danger';
+            case '中':
+                return 'alert-warning';
+            case '低':
+                return 'alert-info';
+            default:
+                return 'alert-secondary';
+        }
+    }
+
+    function getRiskScoreClass(score) {
+        if (score >= 80) return 'bg-danger';
+        if (score >= 60) return 'bg-warning text-dark';
+        if (score >= 40) return 'bg-info text-dark';
+        return 'bg-success';
+    }
+
+    function getRiskColor(score) {
+        if (score >= 80) return '#dc3545'; // 红色
+        if (score >= 60) return '#ffc107'; // 黄色
+        if (score >= 40) return '#17a2b8'; // 蓝色
+        return '#28a745'; // 绿色
+    }
+</script>
+{% endblock %}

templates/scenario_predict.html

@@ -0,0 +1,388 @@
+{% extends "layout.html" %}
+
+{% block title %}情景预测 - {{ stock_code }} - 智能分析系统{% endblock %}
+
+{% block content %}
+<div class="container-fluid py-3">
+    <div id="alerts-container"></div>
+
+    <div class="row mb-3">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header py-2">
+                    <h5 class="mb-0">多情景预测</h5>
+                </div>
+                <div class="card-body py-2">
+                    <form id="scenario-form" class="row g-2">
+                        <div class="col-md-3">
+                            <div class="input-group input-group-sm">
+                                <span class="input-group-text">股票代码</span>
+                                <input type="text" class="form-control" id="stock-code" placeholder="例如: 600519" required>
+                            </div>
+                        </div>
+                        <div class="col-md-3">
+                            <div class="input-group input-group-sm">
+                                <span class="input-group-text">市场</span>
+                                <select class="form-select" id="market-type">
+                                    <option value="A" selected>A股</option>
+                                    <option value="HK">港股</option>
+                                    <option value="US">美股</option>
+                                </select>
+                            </div>
+                        </div>
+                        <div class="col-md-3">
+                            <div class="input-group input-group-sm">
+                                <span class="input-group-text">预测天数</span>
+                                <select class="form-select" id="days">
+                                    <option value="30">30天</option>
+                                    <option value="60" selected>60天</option>
+                                    <option value="90">90天</option>
+                                    <option value="180">180天</option>
+                                </select>
+                            </div>
+                        </div>
+                        <div class="col-md-3">
+                            <button type="submit" class="btn btn-primary btn-sm w-100">
+                                <i class="fas fa-chart-line"></i> 预测
+                            </button>
+                        </div>
+                    </form>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <div id="loading-panel" class="text-center py-5" style="display: none;">
+        <div class="spinner-border text-primary" role="status">
+            <span class="visually-hidden">Loading...</span>
+        </div>
+        <p class="mt-3 mb-0">正在生成预测结果...</p>
+        <p class="text-muted small mt-2">
+            <i class="fas fa-info-circle"></i>
+            AI分析需要一些时间，请耐心等待
+        </p>
+    </div>
+
+    <div id="scenario-result" style="display: none;">
+        <div class="row g-3 mb-3">
+            <div class="col-12">
+                <div class="card">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">价格预测图</h5>
+                    </div>
+                    <div class="card-body p-0">
+                        <div id="price-prediction-chart" style="height: 400px;"></div>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <div class="row g-3 mb-3">
+            <div class="col-md-4">
+                <div class="card h-100">
+                    <div class="card-header py-2 bg-success text-white">
+                        <h5 class="mb-0">乐观情景</h5>
+                    </div>
+                    <div class="card-body">
+                        <div class="d-flex justify-content-between mb-3">
+                            <div>
+                                <h6>目标价</h6>
+                                <h3 id="optimistic-price" class="text-success">--</h3>
+                            </div>
+                            <div>
+                                <h6>预期涨幅</h6>
+                                <h3 id="optimistic-change" class="text-success">--</h3>
+                            </div>
+                        </div>
+                        <p id="optimistic-analysis" class="small"></p>
+                    </div>
+                </div>
+            </div>
+            <div class="col-md-4">
+                <div class="card h-100">
+                    <div class="card-header py-2 bg-primary text-white">
+                        <h5 class="mb-0">中性情景</h5>
+                    </div>
+                    <div class="card-body">
+                        <div class="d-flex justify-content-between mb-3">
+                            <div>
+                                <h6>目标价</h6>
+                                <h3 id="neutral-price" class="text-primary">--</h3>
+                            </div>
+                            <div>
+                                <h6>预期涨幅</h6>
+                                <h3 id="neutral-change" class="text-primary">--</h3>
+                            </div>
+                        </div>
+                        <p id="neutral-analysis" class="small"></p>
+                    </div>
+                </div>
+            </div>
+            <div class="col-md-4">
+                <div class="card h-100">
+                    <div class="card-header py-2 bg-danger text-white">
+                        <h5 class="mb-0">悲观情景</h5>
+                    </div>
+                    <div class="card-body">
+                        <div class="d-flex justify-content-between mb-3">
+                            <div>
+                                <h6>目标价</h6>
+                                <h3 id="pessimistic-price" class="text-danger">--</h3>
+                            </div>
+                            <div>
+                                <h6>预期涨幅</h6>
+                                <h3 id="pessimistic-change" class="text-danger">--</h3>
+                            </div>
+                        </div>
+                        <p id="pessimistic-analysis" class="small"></p>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <div class="row g-3 mb-3">
+            <div class="col-12">
+                <div class="card">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">风险与机会</h5>
+                    </div>
+                    <div class="card-body">
+                        <div class="row">
+                            <div class="col-md-6">
+                                <h6 class="text-danger"><i class="fas fa-exclamation-triangle"></i> 风险因素</h6>
+                                <ul id="risk-factors" class="small"></ul>
+                            </div>
+                            <div class="col-md-6">
+                                <h6 class="text-success"><i class="fas fa-lightbulb"></i> 有利因素</h6>
+                                <ul id="opportunity-factors" class="small"></ul>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+</div>
+{% endblock %}
+
+{% block scripts %}
+<script>
+    $(document).ready(function() {
+        $('#scenario-form').submit(function(e) {
+            e.preventDefault();
+            const stockCode = $('#stock-code').val().trim();
+            const marketType = $('#market-type').val();
+            const days = $('#days').val();
+
+            if (!stockCode) {
+                showError('请输入股票代码！');
+                return;
+            }
+
+            fetchScenarioPrediction(stockCode, marketType, days);
+        });
+    });
+
+    function fetchScenarioPrediction(stockCode, marketType, days) {
+        $('#loading-panel').show();
+        $('#scenario-result').hide();
+
+        $.ajax({
+            url: '/api/scenario_predict',
+            type: 'POST',
+            contentType: 'application/json',
+            data: JSON.stringify({
+                stock_code: stockCode,
+                market_type: marketType,
+                days: parseInt(days)
+            }),
+            success: function(response) {
+                $('#loading-panel').hide();
+                renderScenarioPrediction(response, stockCode);
+                $('#scenario-result').show();
+            },
+            error: function(xhr, status, error) {
+                $('#loading-panel').hide();
+                let errorMsg = '获取情景预测失败';
+                if (xhr.responseJSON && xhr.responseJSON.error) {
+                    errorMsg += ': ' + xhr.responseJSON.error;
+                } else if (error) {
+                    errorMsg += ': ' + error;
+                }
+                showError(errorMsg);
+            }
+        });
+    }
+
+    function renderScenarioPrediction(data, stockCode) {
+        // 设置乐观情景数据
+        $('#optimistic-price').text('¥' + formatNumber(data.optimistic.target_price, 2));
+        $('#optimistic-change').text(formatPercent(data.optimistic.change_percent, 2));
+        $('#optimistic-analysis').text(data.optimistic_analysis || '暂无分析');
+
+        // 设置中性情景数据
+        $('#neutral-price').text('¥' + formatNumber(data.neutral.target_price, 2));
+        $('#neutral-change').text(formatPercent(data.neutral.change_percent, 2));
+        $('#neutral-analysis').text(data.neutral_analysis || '暂无分析');
+
+        // 设置悲观情景数据
+        $('#pessimistic-price').text('¥' + formatNumber(data.pessimistic.target_price, 2));
+        $('#pessimistic-change').text(formatPercent(data.pessimistic.change_percent, 2));
+        $('#pessimistic-analysis').text(data.pessimistic_analysis || '暂无分析');
+
+        // 设置风险与机会因素
+        setDefaultRiskOpportunityFactors();
+
+        // 渲染价格预测图表
+        renderPricePredictionChart(data);
+    }
+
+    function setDefaultRiskOpportunityFactors() {
+        // 示例风险因素
+        const riskFactors = [
+            '宏观经济下行压力增大',
+            '行业政策收紧可能性',
+            '原材料价格上涨',
+            '市场竞争加剧',
+            '技术迭代风险'
+        ];
+
+        // 示例有利因素
+        const opportunityFactors = [
+            '行业景气度持续向好',
+            '公司新产品上市',
+            '成本控制措施见效',
+            '产能扩张计划',
+            '国际市场开拓机会'
+        ];
+
+        // 填充HTML
+        $('#risk-factors').html('');
+        riskFactors.forEach(factor => {
+            $('#risk-factors').append(`<li>${factor}</li>`);
+        });
+
+        $('#opportunity-factors').html('');
+        opportunityFactors.forEach(factor => {
+            $('#opportunity-factors').append(`<li>${factor}</li>`);
+        });
+    }
+
+    function renderPricePredictionChart(data) {
+        // 准备数据
+        const currentPrice = data.current_price;
+
+        // 提取日期和价格路径
+        const dates = Object.keys(data.optimistic.path);
+        const optimisticPrices = Object.values(data.optimistic.path);
+        const neutralPrices = Object.values(data.neutral.path);
+        const pessimisticPrices = Object.values(data.pessimistic.path);
+
+        const options = {
+            series: [
+                {
+                    name: '乐观情景',
+                    data: optimisticPrices.map((price, i) => ({
+                        x: new Date(dates[i]),
+                        y: price
+                    }))
+                },
+                {
+                    name: '中性情景',
+                    data: neutralPrices.map((price, i) => ({
+                        x: new Date(dates[i]),
+                        y: price
+                    }))
+                },
+                {
+                    name: '悲观情景',
+                    data: pessimisticPrices.map((price, i) => ({
+                        x: new Date(dates[i]),
+                        y: price
+                    }))
+                }
+            ],
+            chart: {
+                height: 400,
+                type: 'line',
+                zoom: {
+                    enabled: true
+                },
+                toolbar: {
+                    show: true
+                }
+            },
+            colors: ['#20E647', '#2E93fA', '#FF4560'],
+            dataLabels: {
+                enabled: false
+            },
+            stroke: {
+                curve: 'smooth',
+                width: [3, 3, 3]
+            },
+            title: {
+                text: '多情景预测',
+                align: 'left'
+            },
+            grid: {
+                borderColor: '#e7e7e7',
+                row: {
+                    colors: ['#f3f3f3', 'transparent'],
+                    opacity: 0.5
+                },
+            },
+            markers: {
+                size: 1
+            },
+            xaxis: {
+                type: 'datetime',
+                title: {
+                    text: '日期'
+                }
+            },
+            yaxis: {
+                title: {
+                    text: '价格 (¥)'
+                },
+                labels: {
+                    formatter: function(val) {
+                        return formatNumber(val, 2);
+                    }
+                }
+            },
+            legend: {
+                position: 'top',
+                horizontalAlign: 'right'
+            },
+            tooltip: {
+                shared: true,
+                intersect: false,
+                y: {
+                    formatter: function(value) {
+                        return '¥' + formatNumber(value, 2);
+                    }
+                }
+            },
+            annotations: {
+                yaxis: [
+                    {
+                        y: currentPrice,
+                        borderColor: '#000',
+                        label: {
+                            borderColor: '#000',
+                            style: {
+                                color: '#fff',
+                                background: '#000'
+                            },
+                            text: '当前价格: ¥' + formatNumber(currentPrice, 2)
+                        }
+                    }
+                ]
+            }
+        };
+
+        const chart = new ApexCharts(document.querySelector("#price-prediction-chart"), options);
+        chart.render();
+    }
+</script>
+{% endblock %}

templates/stock_detail.html

@@ -0,0 +1,1200 @@
+{% extends "layout.html" %}
+
+{% block title %}股票详情 - {{ stock_code }} - 智能分析系统{% endblock %}
+
+{% block content %}
+<div class="container-fluid py-1" style="padding-top: 0.2rem !important; padding-bottom: 0.2rem !important;">
+
+    <div id="alerts-container"></div>
+
+    <!-- 调整布局: 减少垂直方向上的间距 -->
+    <div class="d-flex justify-content-between align-items-center mb-1" style="margin-top: 0.2rem; margin-bottom: 0.2rem;">
+        <h4 id="stock-title" class="mb-0 fw-bold" style="font-size: 1.1rem; line-height: 1.2;">股票详情加载中...</h4>
+        <div class="d-flex align-items-center">
+            <select class="form-select form-select-sm me-2" id="market-type" style="max-width: 100px; height: 32px; padding-top: 2px; padding-bottom: 2px;">
+                <option value="A" {% if market_type == 'A' %}selected{% endif %}>A股</option>
+                <option value="HK" {% if market_type == 'HK' %}selected{% endif %}>港股</option>
+                <option value="US" {% if market_type == 'US' %}selected{% endif %}>美股</option>
+            </select>
+            <select class="form-select form-select-sm me-2" id="analysis-period" style="max-width: 100px; height: 32px; padding-top: 2px; padding-bottom: 2px;">
+                <option value="1m">1个月</option>
+                <option value="3m">3个月</option>
+                <option value="6m">6个月</option>
+                <option value="1y" selected>1年</option>
+            </select>
+            <button id="refresh-btn" class="btn btn-primary btn-sm" style="height: 32px; padding: 2px 8px;">
+                <i class="fas fa-sync-alt"></i>
+            </button>
+        </div>
+    </div>
+
+    <div id="loading-panel" class="text-center py-5">
+        <div class="spinner-border text-primary" role="status">
+            <span class="visually-hidden">Loading...</span>
+        </div>
+        <p class="mt-3 mb-0">正在加载股票数据和分析结果...</p>
+        <p class="text-muted small mt-2">
+            <i class="fas fa-info-circle"></i>
+            AI分析需要30-300秒，已处理<span id="processing-time">0</span>秒
+        </p>
+        <div class="progress mt-3" style="height: 5px;">
+            <div class="progress-bar progress-bar-striped progress-bar-animated" role="progressbar" style="width: 100%"></div>
+        </div>
+        <button id="cancel-analysis-btn" class="btn btn-outline-secondary mt-3">
+            <i class="fas fa-times"></i> 取消分析
+        </button>
+    </div>
+
+    <div id="error-retry" class="text-center mt-3" style="display: none;">
+        <button id="retry-button" class="btn btn-primary mt-2">
+            <i class="fas fa-sync-alt"></i> 重试分析
+        </button>
+        <p class="text-muted small mt-2">
+            如果重试失败，请访问<a href="/dashboard">仪表盘</a>尝试其他股票
+        </p>
+    </div>
+
+    <div id="analysis-result" style="display: none;">
+        <div class="row g-3 mb-3">
+            <div class="col-md-6">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">股票概要</h5>
+                    </div>
+                    <div class="card-body">
+                        <div class="row mb-3">
+                            <div class="col-md-7">
+                                <h3 id="stock-name" class="mb-0 fs-4"></h3>
+                                <p id="stock-info" class="text-muted mb-0 small"></p>
+                            </div>
+                            <div class="col-md-5 text-end">
+                                <h2 id="stock-price" class="mb-0 fs-4"></h2>
+                                <p id="price-change" class="mb-0"></p>
+                            </div>
+                        </div>
+                        <div class="row">
+                            <div class="col-md-6">
+                                <div class="mb-2">
+                                    <span class="text-muted small">综合评分:</span>
+                                    <div class="mt-1">
+                                        <span id="total-score" class="badge rounded-pill score-pill"></span>
+                                    </div>
+                                </div>
+                                <div class="mb-2">
+                                    <span class="text-muted small">投资建议:</span>
+                                    <p id="recommendation" class="mb-0 text-strong"></p>
+                                </div>
+                            </div>
+                            <div class="col-md-6">
+                                <div class="mb-2">
+                                    <span class="text-muted small">技术面指标:</span>
+                                    <ul class="list-unstyled mt-1 mb-0 small">
+                                        <li><span class="text-muted">RSI:</span> <span id="rsi-value"></span></li>
+                                        <li><span class="text-muted">MA趋势:</span> <span id="ma-trend"></span></li>
+                                        <li><span class="text-muted">MACD信号:</span> <span id="macd-signal"></span></li>
+                                        <li><span class="text-muted">成交量:</span> <span id="volume-status"></span></li>
+                                    </ul>
+                                </div>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+            <div class="col-md-6">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">多维度评分</h5>
+                    </div>
+                    <div class="card-body">
+                        <div class="row">
+                            <div class="col-md-6">
+                                <div id="score-chart" style="height: 180px;"></div>
+                            </div>
+                            <div class="col-md-6">
+                                <div id="radar-chart" style="height: 180px;"></div>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <div class="row g-3 mb-3">
+            <div class="col-12">
+                <div class="card">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">价格与技术指标</h5>
+                    </div>
+                    <div class="card-body p-0">
+                        <div id="price-chart" style="height: 400px;"></div>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <div class="row g-3 mb-3">
+            <div class="col-12">
+                <div class="card">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">MACD & RSI 指标</h5>
+                    </div>
+                    <div class="card-body p-0">
+                        <div id="indicators-chart" style="height: 350px;"></div>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <div class="row g-3 mb-3">
+            <div class="col-md-4">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">支撑与压力位</h5>
+                    </div>
+                    <div class="card-body">
+                        <table class="table table-sm">
+                            <thead>
+                                <tr>
+                                    <th>类型</th>
+                                    <th>价格</th>
+                                    <th>距离</th>
+                                </tr>
+                            </thead>
+                            <tbody id="support-resistance-table">
+                                <!-- 支撑压力位数据将在JS中动态填充 -->
+                            </tbody>
+                        </table>
+                    </div>
+                </div>
+            </div>
+            <div class="col-md-8">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">AI分析建议</h5>
+                    </div>
+                    <div class="card-body">
+                        <div id="ai-analysis" class="analysis-section">
+                            <!-- AI分析结果将在JS中动态填充 -->
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+</div>
+{% endblock %}
+
+{% block scripts %}
+<script>
+    const stockCode = '{{ stock_code }}';
+    let marketType = '{{ market_type }}';
+    let period = '1y';
+    let stockData = [];
+    let analysisResult = null;
+
+    $(document).ready(function() {
+        // 初始加载
+        loadStockData();
+
+        // 刷新按钮点击事件
+        $('#refresh-btn').click(function() {
+            marketType = $('#market-type').val();
+            period = $('#analysis-period').val();
+            loadStockData();
+        });
+
+        // 市场类型改变事件
+        $('#market-type').change(function() {
+            marketType = $(this).val();
+        });
+
+        // 分析周期改变事件
+        $('#analysis-period').change(function() {
+            period = $(this).val();
+        });
+    });
+
+    // 加载股票数据
+    function loadStockData() {
+        $('#loading-panel').show();
+        $('#analysis-result').hide();
+
+        // 获取股票数据
+        $.ajax({
+            url: `/api/stock_data?stock_code=${stockCode}&market_type=${marketType}&period=${period}`,
+            type: 'GET',
+            dataType: 'json',
+            success: function(response) {
+
+                // 检查response是否有data属性
+                if (!response.data) {
+                    $('#loading-panel').hide();
+                    showError('响应格式不正确: 缺少data字段');
+                    return;
+                }
+
+                if (response.data.length === 0) {
+                    $('#loading-panel').hide();
+                    showError('未找到股票数据');
+                    return;
+                }
+
+                stockData = response.data;
+
+                // 获取增强分析数据
+                loadAnalysisResult();
+            },
+            error: function(xhr, status, error) {
+                $('#loading-panel').hide();
+
+                let errorMsg = '获取股票数据失败';
+                if (xhr.responseJSON && xhr.responseJSON.error) {
+                    errorMsg += ': ' + xhr.responseJSON.error;
+                } else if (error) {
+                    errorMsg += ': ' + error;
+                }
+                showError(errorMsg);
+            }
+        });
+    }
+
+    // 加载分析结果
+    function loadAnalysisResult() {
+
+        // 显示加载状态并启动进度更新
+        $('#loading-panel').show();
+        $('#analysis-result').hide();
+        $('#error-retry').hide();
+
+        // 添加处理时间计数器
+        let processingTime = 0;
+        const processingTimer = setInterval(function() {
+            processingTime++;
+            $('#processing-time').text(processingTime);
+        }, 1000);
+
+        // 使用新的API启动分析任务
+        $.ajax({
+            url: '/api/start_stock_analysis',
+            type: 'POST',
+            contentType: 'application/json',
+            data: JSON.stringify({
+                stock_code: stockCode,
+                market_type: marketType
+            }),
+            success: function(response) {
+
+                // 检查是否已有结果
+                if (response.status === 'completed' && response.result) {
+                    // 任务已完成，直接处理结果
+                    handleAnalysisResult(response.result);
+                    clearInterval(processingTimer);
+                    return;
+                }
+
+                // 开始轮询任务状态
+                pollAnalysisStatus(response.task_id, processingTime, processingTimer);
+            },
+            error: function(xhr, status, error) {
+                clearInterval(processingTimer);
+                handleAnalysisError(xhr, status, error);
+            }
+        });
+    }
+
+    // 轮询分析任务状态
+    function pollAnalysisStatus(taskId, startTime, timerInterval) {
+        let elapsedTime = startTime || 0;
+        let pollInterval;
+
+        // 保存当前任务ID，用于取消
+        window.currentAnalysisTaskId = taskId;
+
+        // 立即执行一次，然后设置定时器
+        checkStatus();
+
+        function checkStatus() {
+            $.ajax({
+                url: `/api/analysis_status/${taskId}`,
+                type: 'GET',
+                success: function(response) {
+                    // 更新计时和进度
+                    elapsedTime = startTime + 1;
+                    const progress = response.progress || 0;
+
+                    // 更新进度显示
+                    $('#processing-time').text(elapsedTime);
+
+                    // 根据任务状态处理
+                    if (response.status === 'completed') {
+                        // 分析完成，停止轮询
+                        clearInterval(pollInterval);
+                        clearInterval(timerInterval);
+
+                        // 处理结果
+                        handleAnalysisResult(response.result);
+                    } else if (response.status === 'failed') {
+                        // 分析失败，停止轮询
+                        clearInterval(pollInterval);
+                        clearInterval(timerInterval);
+
+                        $('#loading-panel').hide();
+
+                        showError('分析失败: ' + (response.error || '未知错误'));
+                        $('#error-retry').show();
+                    } else {
+                        // 任务仍在进行中，继续轮询
+                        if (!pollInterval) {
+                            pollInterval = setInterval(checkStatus, 2000);
+                        }
+                    }
+                },
+                error: function(xhr, status, error) {
+                    if (!pollInterval) {
+                        pollInterval = setInterval(checkStatus, 3000);
+                    }
+                }
+            });
+        }
+    }
+
+    // 处理分析结果
+    function handleAnalysisResult(result) {
+        try {
+            // 设置全局变量
+            analysisResult = result;
+
+            // 渲染分析结果
+            renderAnalysisResult();
+
+            // 更新UI
+            $('#loading-panel').hide();
+            $('#analysis-result').show();
+        } catch (error) {
+            $('#loading-panel').hide();
+            showError('处理分析结果时出错: ' + error.message);
+        }
+    }
+
+    // 处理分析错误
+    function handleAnalysisError(xhr, status, error) {
+        $('#loading-panel').hide();
+
+        let errorMsg = '获取分析数据失败';
+        if (status === 'timeout') {
+            errorMsg = '请求超时，分析可能需要较长时间，请稍后再试';
+        } else if (xhr.status === 524 || xhr.status === 504) {
+            errorMsg = '请求超时，服务器处理时间过长';
+        } else if (xhr.responseJSON && xhr.responseJSON.error) {
+            errorMsg += ': ' + xhr.responseJSON.error;
+        } else if (error) {
+            errorMsg += ': ' + error;
+        }
+
+        showError(errorMsg);
+        $('#error-retry').show();
+    }
+
+    // 取消按钮功能
+    $('#cancel-analysis-btn').click(function() {
+        if (window.currentAnalysisTaskId) {
+            $.ajax({
+                url: `/api/cancel_analysis/${window.currentAnalysisTaskId}`,
+                type: 'POST',
+                success: function(response) {
+                    $('#loading-panel').hide();
+                    showInfo('分析已取消');
+                },
+                error: function(error) {
+                    console.error('取消分析失败:', error);
+                }
+            });
+        } else {
+            $('#loading-panel').hide();
+        }
+    });
+
+    // 重试按钮功能
+    $('#retry-button').click(function() {
+        $('#error-retry').hide();
+        loadAnalysisResult();
+    });
+
+    // 通用安全格式化函数
+    function safeFormat(value, decimals=2) {
+      try {
+        // 处理numpy对象残留
+        if (value && typeof value === 'object') {
+          if (value._dtype === 'float64') {
+            return parseFloat(value._values[0]).toFixed(decimals);
+          }
+          if (value._dtype === 'int64') {
+            return parseInt(value._values[0]);
+          }
+        }
+        return parseFloat(value).toFixed(decimals);
+      } catch (e) {
+        console.error('Format error:', e);
+        return '--';
+      }
+    }
+
+    // 使用示例
+    $('#rsi-value').text(safeFormat(analysisResult.technical_analysis.indicators.rsi));
+
+    // 渲染分析结果
+    function renderAnalysisResult() {
+        if (!analysisResult) {
+            showError("分析结果为空");
+            return;
+        }
+
+        try {
+
+            // 使用全新的安全访问函数
+            function safeGet(obj, path, defaultValue) {
+                if (!obj) return defaultValue;
+
+                const props = path.split('.');
+                let current = obj;
+
+                for (let i = 0; i < props.length; i++) {
+                    if (current === undefined || current === null) {
+                        console.warn(`属性路径 ${path} 在 ${props.slice(0, i).join('.')} 处中断`);
+                        return defaultValue;
+                    }
+                    current = current[props[i]];
+                }
+
+                return current !== undefined && current !== null ? current : defaultValue;
+            }
+
+            // 安全检查技术分析数据
+            if (!analysisResult.technical_analysis) {
+                analysisResult.technical_analysis = {
+                    trend: {ma_trend: 'UNKNOWN', ma_status: '未知', ma_values: {}},
+                    indicators: {rsi: 50, macd: 0, macd_signal: 0, macd_histogram: 0, volatility: 0},
+                    volume: {current_volume: 0, volume_ratio: 0, volume_status: 'NORMAL'},
+                    support_resistance: {support_levels: {}, resistance_levels: {}}
+                };
+            }
+
+            // 使用安全函数获取所有数据
+            const stockName = safeGet(analysisResult, 'basic_info.stock_name', '未知');
+            const stockCode = safeGet(analysisResult, 'basic_info.stock_code', '未知');
+            const industry = safeGet(analysisResult, 'basic_info.industry', '未知');
+            const analysisDate = safeGet(analysisResult, 'basic_info.analysis_date', '未知日期');
+
+            // 更新页面标题
+            $('#stock-title').text(`${stockName} (${stockCode}) 股票分析`);
+
+            // 渲染股票基本信息
+            $('#stock-name').text(`${stockName} (${stockCode})`);
+            $('#stock-info').text(`${industry} | ${analysisDate}`);
+
+            // 渲染价格信息
+            const currentPrice = safeGet(analysisResult, 'price_data.current_price', 0);
+            const priceChange = safeGet(analysisResult, 'price_data.price_change', 0);
+            const priceChangeValue = safeGet(analysisResult, 'price_data.price_change_value', 0);
+
+            $('#stock-price').text('¥' + formatNumber(currentPrice, 2));
+            const priceChangeClass = priceChange >= 0 ? 'trend-up' : 'trend-down';
+            const priceChangeIcon = priceChange >= 0 ? '<i class="fas fa-caret-up"></i>' : '<i class="fas fa-caret-down"></i>';
+            $('#price-change').html(`<span class="${priceChangeClass}">${priceChangeIcon} ${formatNumber(priceChangeValue, 2)} (${formatPercent(priceChange, 2)})</span>`);
+
+            // 渲染评分和建议
+            const totalScore = safeGet(analysisResult, 'scores.total', 0);
+            const scoreClass = getScoreColorClass(totalScore);
+            $('#total-score').text(totalScore).addClass(scoreClass);
+            $('#recommendation').text(safeGet(analysisResult, 'recommendation.action', '无建议'));
+
+            // 渲染技术指标 - 所有属性都使用安全访问
+            $('#rsi-value').text(formatNumber(safeGet(analysisResult, 'technical_analysis.indicators.rsi', 0), 2));
+
+            const maTrend = safeGet(analysisResult, 'technical_analysis.trend.ma_trend', 'UNKNOWN');
+            const maStatus = safeGet(analysisResult, 'technical_analysis.trend.ma_status', '未知');
+            const maTrendClass = getTrendColorClass(maTrend);
+            const maTrendIcon = getTrendIcon(maTrend);
+            $('#ma-trend').html(`<span class="${maTrendClass}">${maTrendIcon} ${maStatus}</span>`);
+
+            // MACD信号
+            const macd = safeGet(analysisResult, 'technical_analysis.indicators.macd', 0);
+            const macdSignal = safeGet(analysisResult, 'technical_analysis.indicators.macd_signal', 0);
+            const macdStatus = macd > macdSignal ? 'BUY' : 'SELL';
+            const macdClass = macdStatus === 'BUY' ? 'trend-up' : 'trend-down';
+            const macdIcon = macdStatus === 'BUY' ? '<i class="fas fa-arrow-up"></i>' : '<i class="fas fa-arrow-down"></i>';
+            $('#macd-signal').html(`<span class="${macdClass}">${macdIcon} ${macdStatus}</span>`);
+
+            // 成交量状态
+            const volumeStatus = safeGet(analysisResult, 'technical_analysis.volume.volume_status', 'NORMAL');
+            const volumeClass = volumeStatus === 'HIGH' ? 'trend-up' : 'trend-down';
+            const volumeIcon = volumeStatus === 'HIGH' ? '<i class="fas fa-arrow-up"></i>' : '<i class="fas fa-arrow-down"></i>';
+            $('#volume-status').html(`<span class="${volumeClass}">${volumeIcon} ${volumeStatus}</span>`);
+
+            // 支撑压力位 - 完全重写为更安全的版本
+            let supportResistanceHtml = '';
+
+            // 渲染压力位
+            const shortTermResistance = safeGet(analysisResult, 'technical_analysis.support_resistance.resistance_levels.short_term', []);
+            if (shortTermResistance.length > 0) {
+                const resistance = shortTermResistance[0];
+                const distance = ((resistance - currentPrice) / currentPrice * 100).toFixed(2);
+                supportResistanceHtml += `
+                    <tr>
+                        <td><span class="badge bg-danger">短期压力</span></td>
+                        <td>${formatNumber(resistance, 2)}</td>
+                        <td>+${distance}%</td>
+                    </tr>
+                `;
+            }
+
+            const mediumTermResistance = safeGet(analysisResult, 'technical_analysis.support_resistance.resistance_levels.medium_term', []);
+            if (mediumTermResistance.length > 0) {
+                const resistance = mediumTermResistance[0];
+                const distance = ((resistance - currentPrice) / currentPrice * 100).toFixed(2);
+                supportResistanceHtml += `
+                    <tr>
+                        <td><span class="badge bg-warning text-dark">中期压力</span></td>
+                        <td>${formatNumber(resistance, 2)}</td>
+                        <td>+${distance}%</td>
+                    </tr>
+                `;
+            }
+
+            // 渲染支撑位
+            const shortTermSupport = safeGet(analysisResult, 'technical_analysis.support_resistance.support_levels.short_term', []);
+            if (shortTermSupport.length > 0) {
+                const support = shortTermSupport[0];
+                const distance = ((support - currentPrice) / currentPrice * 100).toFixed(2);
+                supportResistanceHtml += `
+                    <tr>
+                        <td><span class="badge bg-success">短期支撑</span></td>
+                        <td>${formatNumber(support, 2)}</td>
+                        <td>${distance}%</td>
+                    </tr>
+                `;
+            }
+
+            const mediumTermSupport = safeGet(analysisResult, 'technical_analysis.support_resistance.support_levels.medium_term', []);
+            if (mediumTermSupport.length > 0) {
+                const support = mediumTermSupport[0];
+                const distance = ((support - currentPrice) / currentPrice * 100).toFixed(2);
+                supportResistanceHtml += `
+                    <tr>
+                        <td><span class="badge bg-info">中期支撑</span></td>
+                        <td>${formatNumber(support, 2)}</td>
+                        <td>${distance}%</td>
+                    </tr>
+                `;
+            }
+
+            if (supportResistanceHtml === '') {
+                supportResistanceHtml = '<tr><td colspan="3" class="text-center">暂无支撑压力位数据</td></tr>';
+            }
+
+            $('#support-resistance-table').html(supportResistanceHtml);
+
+            // 渲染AI分析
+            const aiAnalysis = safeGet(analysisResult, 'ai_analysis', '暂无AI分析');
+            $('#ai-analysis').html(formatAIAnalysis(aiAnalysis));
+
+            // 安全地绘制图表
+            try {
+                renderScoreChart();
+            } catch (e) {
+                $('#score-chart').html('<div class="text-center text-muted">评分图表渲染失败</div>');
+            }
+
+            try {
+                renderRadarChart();
+            } catch (e) {
+                $('#radar-chart').html('<div class="text-center text-muted">雷达图表渲染失败</div>');
+            }
+
+            try {
+                renderPriceChart();
+            } catch (e) {
+                $('#price-chart').html('<div class="text-center text-muted">价格图表渲染失败</div>');
+            }
+
+            try {
+                renderIndicatorsChart();
+            } catch (e) {
+                $('#indicators-chart').html('<div class="text-center text-muted">指标图表渲染失败</div>');
+            }
+
+        } catch (error) {
+            showError(`渲染分析结果时出错: ${error.message}`);
+        }
+    }
+
+    // 绘制评分图表
+    function renderScoreChart() {
+        if (!analysisResult) return;
+
+        const totalScore = analysisResult.scores.total || 0;
+
+        const options = {
+            series: [totalScore],
+            chart: {
+                height: 180,
+                type: 'radialBar',
+                toolbar: {
+                    show: false
+                }
+            },
+            plotOptions: {
+                radialBar: {
+                    hollow: {
+                        size: '70%',
+                    },
+                    dataLabels: {
+                        showOn: 'always',
+                        name: {
+                            show: true,
+                            fontSize: '14px',
+                            fontWeight: 600,
+                            offsetY: -10
+                        },
+                        value: {
+                            formatter: function(val) {
+                                return val;
+                            },
+                            fontSize: '22px',
+                            fontWeight: 700,
+                            offsetY: 5
+                        }
+                    }
+                }
+            },
+            fill: {
+                type: 'gradient',
+                gradient: {
+                    shade: 'dark',
+                    type: 'horizontal',
+                    gradientToColors: ['#ABE5A1'],
+                    stops: [0, 100]
+                }
+            },
+            stroke: {
+                lineCap: 'round'
+            },
+            labels: ['总分'],
+            colors: ['#20E647']
+        };
+
+        // 清除旧图表
+        $('#score-chart').empty();
+
+        const chart = new ApexCharts(document.querySelector("#score-chart"), options);
+        chart.render();
+    }
+
+    // 绘制雷达图
+    function renderRadarChart() {
+        if (!analysisResult) return;
+
+        const trendScore = analysisResult.scores.trend || 0;
+        const indicatorsScore = analysisResult.scores.indicators || 0;
+        const supportResistanceScore = analysisResult.scores.support_resistance || 0;
+        const volatilityVolumeScore = analysisResult.scores.volatility_volume || 0;
+
+        const options = {
+            series: [{
+                name: '评分',
+                data: [
+                    trendScore,
+                    indicatorsScore,
+                    supportResistanceScore,
+                    volatilityVolumeScore
+                ]
+            }],
+            chart: {
+                height: 180,
+                type: 'radar',
+                toolbar: {
+                    show: false
+                }
+            },
+            xaxis: {
+                categories: ['趋势', '指标', '支压', '波动量']
+            },
+            yaxis: {
+                max: 10,
+                min: 0
+            },
+            fill: {
+                opacity: 0.5,
+                colors: ['#4e73df']
+            },
+            markers: {
+                size: 4
+            }
+        };
+
+        // 清除旧图表
+        $('#radar-chart').empty();
+
+        const chart = new ApexCharts(document.querySelector("#radar-chart"), options);
+        chart.render();
+    }
+
+    // 绘制价格图表
+    function renderPriceChart() {
+
+        try {
+            // Create a fully separate array for each price series (no OHLC array)
+            const closePrices = stockData.map(item => {
+              // 处理numpy日期格式
+              let dateStr = item.date;
+              if (dateStr && typeof dateStr === 'object') {
+                dateStr = dateStr.toString().split('T')[0];
+              }
+
+              return {
+                x: new Date(dateStr + 'T00:00:00'), // 标准化日期
+                y: safeFormat(item.close)
+              };
+            });
+
+            const ma5Data = stockData.map(item => ({
+                x: new Date(item.date),
+                y: parseFloat(item.MA5 || 0)
+            }));
+
+            const ma20Data = stockData.map(item => ({
+                x: new Date(item.date),
+                y: parseFloat(item.MA20 || 0)
+            }));
+
+            const ma60Data = stockData.map(item => ({
+                x: new Date(item.date),
+                y: parseFloat(item.MA60 || 0)
+            }));
+
+            // Create chart options using line chart instead of candlestick
+            const priceOptions = {
+                series: [
+                    {
+                        name: '收盘价',
+                        type: 'line',
+                        data: closePrices
+                    },
+                    {
+                        name: 'MA5',
+                        type: 'line',
+                        data: ma5Data
+                    },
+                    {
+                        name: 'MA20',
+                        type: 'line',
+                        data: ma20Data
+                    },
+                    {
+                        name: 'MA60',
+                        type: 'line',
+                        data: ma60Data
+                    }
+                ],
+                chart: {
+                    height: 400,
+                    type: 'line',
+                    toolbar: {
+                        show: true
+                    },
+                    animations: {
+                        enabled: false
+                    }
+                },
+                stroke: {
+                    width: [3, 2, 2, 2],
+                    curve: 'straight'
+                },
+                title: {
+                    text: `价格走势图`,
+                    align: 'left'
+                },
+                xaxis: {
+                    type: 'datetime'
+                },
+                yaxis: {
+                    labels: {
+                        formatter: function(value) {
+                            return formatNumber(value, 2);
+                        }
+                    }
+                },
+                tooltip: {
+                    enabled: true,
+                    shared: true,
+                    intersect: false,
+                    x: {
+                        format: 'yyyy-MM-dd'
+                    },
+                    y: {
+                        formatter: function(value) {
+                            return formatNumber(value, 2);
+                        }
+                    }
+                },
+                legend: {
+                    show: true,
+                    position: 'top',
+                    horizontalAlign: 'left'
+                },
+                markers: {
+                    size: 0
+                },
+                grid: {
+                    show: true
+                }
+            };
+
+
+            $('#price-chart').empty();
+
+
+            const chart = new ApexCharts(document.querySelector("#price-chart"), priceOptions);
+
+            chart.render();
+
+
+        } catch (error) {
+            // Show error message
+            $('#price-chart').html('<div class="alert alert-danger">图表加载失败: ' + error.message + '</div>');
+        }
+    }
+
+    // Format AI analysis text
+    function formatAIAnalysis(text) {
+        if (!text) return '';
+
+        // First, make the text safe for HTML
+        const safeText = text
+            .replace(/&/g, '&amp;')
+            .replace(/</g, '&lt;')
+            .replace(/>/g, '&gt;');
+
+        // Replace basic Markdown elements
+        let formatted = safeText
+            // Bold text with ** or __
+            .replace(/\*\*(.*?)\*\*/g, '<strong class="keyword">$1</strong>')
+            .replace(/__(.*?)__/g, '<strong>$1</strong>')
+
+            // Italic text with * or _
+            .replace(/\*(.*?)\*/g, '<em>$1</em>')
+            .replace(/_(.*?)_/g, '<em>$1</em>')
+
+            // Headers
+            .replace(/^# (.*?)$/gm, '<h4 class="mt-3 mb-2">$1</h4>')
+            .replace(/^## (.*?)$/gm, '<h5 class="mt-2 mb-2">$1</h5>')
+
+            // Apply special styling to financial terms
+            .replace(/支撑位/g, '<span class="keyword">支撑位</span>')
+            .replace(/压力位/g, '<span class="keyword">压力位</span>')
+            .replace(/趋势/g, '<span class="keyword">趋势</span>')
+            .replace(/均线/g, '<span class="keyword">均线</span>')
+            .replace(/MACD/g, '<span class="term">MACD</span>')
+            .replace(/RSI/g, '<span class="term">RSI</span>')
+            .replace(/KDJ/g, '<span class="term">KDJ</span>')
+
+            // Highlight price patterns and movements
+            .replace(/([上涨升])/g, '<span class="trend-up">$1</span>')
+            .replace(/([下跌降])/g, '<span class="trend-down">$1</span>')
+            .replace(/(买入|做多|多头|突破)/g, '<span class="trend-up">$1</span>')
+            .replace(/(卖出|做空|空头|跌破)/g, '<span class="trend-down">$1</span>')
+
+            // Highlight price values (matches patterns like 31.25, 120.50)
+            .replace(/(\d+\.\d{2})/g, '<span class="price">$1</span>')
+
+            // Convert line breaks to paragraph tags
+            .replace(/\n\n+/g, '</p><p class="analysis-para">')
+            .replace(/\n/g, '<br>');
+
+        // Wrap in paragraph tags for consistent styling
+        return '<p class="analysis-para">' + formatted + '</p>';
+    }
+
+    function renderPriceChartWithOHLC() {
+
+
+        try {
+            // Create OHLC data table to display below the chart
+            let ohlcTableHtml = '<div class="ohlc-data mt-3"><h6>价格数据 (最近5天)</h6><table class="table table-sm table-bordered">';
+            ohlcTableHtml += '<thead><tr><th>日期</th><th>开盘</th><th>最高</th><th>最低</th><th>收盘</th></tr></thead><tbody>';
+
+            // Get last 5 days of data
+            const recentData = stockData.slice(-5);
+            recentData.forEach(item => {
+                ohlcTableHtml += `<tr>
+                    <td>${item.date}</td>
+                    <td>${formatNumber(item.open, 2)}</td>
+                    <td>${formatNumber(item.high, 2)}</td>
+                    <td>${formatNumber(item.low, 2)}</td>
+                    <td>${formatNumber(item.close, 2)}</td>
+                </tr>`;
+            });
+            ohlcTableHtml += '</tbody></table></div>';
+
+            // Create price line chart
+        const closePrices = stockData.map(item => {
+          // 处理numpy日期格式
+          let dateStr = item.date;
+          if (dateStr && typeof dateStr === 'object') {
+            dateStr = dateStr.toString().split('T')[0];
+          }
+
+          return {
+            x: new Date(dateStr + 'T00:00:00'), // 标准化日期
+            y: safeFormat(item.close)
+          };
+        });
+
+            const ma5Data = stockData.map(item => ({
+                x: new Date(item.date),
+                y: parseFloat(item.MA5 || 0)
+            }));
+
+            const ma20Data = stockData.map(item => ({
+                x: new Date(item.date),
+                y: parseFloat(item.MA20 || 0)
+            }));
+
+            const ma60Data = stockData.map(item => ({
+                x: new Date(item.date),
+                y: parseFloat(item.MA60 || 0)
+            }));
+
+            const priceOptions = {
+                series: [
+                    {
+                        name: '收盘价',
+                        type: 'line',
+                        data: closePrices
+                    },
+                    {
+                        name: 'MA5',
+                        type: 'line',
+                        data: ma5Data
+                    },
+                    {
+                        name: 'MA20',
+                        type: 'line',
+                        data: ma20Data
+                    },
+                    {
+                        name: 'MA60',
+                        type: 'line',
+                        data: ma60Data
+                    }
+                ],
+                chart: {
+                    height: 350,
+                    type: 'line',
+                    toolbar: {
+                        show: true
+                    },
+                    animations: {
+                        enabled: false
+                    }
+                },
+                stroke: {
+                    width: [3, 2, 2, 2]
+                },
+                colors: ['#FF4560', '#008FFB', '#00E396', '#775DD0'],
+                title: {
+                    text: `价格走势图 (收盘价)`,
+                    align: 'left'
+                },
+                xaxis: {
+                    type: 'datetime'
+                },
+                yaxis: {
+                    labels: {
+                        formatter: function(value) {
+                            return formatNumber(value, 2);
+                        }
+                    }
+                },
+                tooltip: {
+                    enabled: true,
+                    shared: true,
+                    intersect: false,
+                    x: {
+                        format: 'yyyy-MM-dd'
+                    }
+                },
+                legend: {
+                    show: true
+                }
+            };
+
+
+            // Clear the chart div and prepare it for the chart + table
+            $('#price-chart').empty();
+
+            // Create a container for the chart
+            $('#price-chart').append('<div id="price-line-chart"></div>');
+
+            const chart = new ApexCharts(document.querySelector("#price-line-chart"), priceOptions);
+
+            chart.render();
+
+            // Append the OHLC table below the chart
+            $('#price-chart').append(ohlcTableHtml);
+
+        } catch (error) {
+            $('#price-chart').html('<div class="alert alert-danger">图表加载失败: ' + error.message + '</div>');
+        }
+    }
+
+
+    // 绘制技术指标图表
+    function renderIndicatorsChart() {
+
+        try {
+            // Create chart options inline without using variables
+            const indicatorOptions = {
+                series: [
+                    {
+                        name: 'MACD',
+                        type: 'line',
+                        data: stockData.map(item => ({
+                            x: new Date(item.date),
+                            y: parseFloat(item.MACD || 0)
+                        }))
+                    },
+                    {
+                        name: 'Signal',
+                        type: 'line',
+                        data: stockData.map(item => ({
+                            x: new Date(item.date),
+                            y: parseFloat(item.Signal || 0)
+                        }))
+                    },
+                    {
+                        name: 'Histogram',
+                        type: 'bar',
+                        data: stockData.map(item => ({
+                            x: new Date(item.date),
+                            y: parseFloat(item.MACD_hist || 0)
+                        }))
+                    },
+                    {
+                        name: 'RSI',
+                        type: 'line',
+                        data: stockData.map(item => ({
+                            x: new Date(item.date),
+                            y: parseFloat(item.RSI || 0)
+                        }))
+                    }
+                ],
+                chart: {
+                    height: 350,
+                    type: 'line',
+                    stacked: false,
+                    toolbar: {
+                        show: true
+                    },
+                    // Disable animations
+                    animations: {
+                        enabled: false
+                    }
+                },
+                stroke: {
+                    width: [3, 3, 0, 3],
+                    curve: 'smooth'
+                },
+                xaxis: {
+                    type: 'datetime'
+                },
+                yaxis: [
+                    {
+                        title: {
+                            text: 'MACD',
+                        },
+                        seriesName: 'MACD',
+                        labels: {
+                            formatter: function(value) {
+                                return formatNumber(value, 3);
+                            }
+                        }
+                    },
+                    {
+                        show: false,
+                        seriesName: 'Signal'
+                    },
+                    {
+                        show: false,
+                        seriesName: 'Histogram'
+                    },
+                    {
+                        opposite: true,
+                        title: {
+                            text: 'RSI'
+                        },
+                        min: 0,
+                        max: 100,
+                        seriesName: 'RSI',
+                        labels: {
+                            formatter: function(value) {
+                                return formatNumber(value, 2);
+                            }
+                        }
+                    }
+                ],
+                // Simplified tooltip configuration
+                tooltip: {
+                    enabled: true,
+                    shared: true,
+                    intersect: false,  // Important for preventing null element errors
+                    hideEmptySeries: true,
+                    x: {
+                        format: 'yyyy-MM-dd'
+                    },
+                    y: {
+                        formatter: function(value, { seriesIndex }) {
+                            // Simplified formatter function
+                            if (seriesIndex === 0) return `MACD: ${formatNumber(value, 3)}`;
+                            if (seriesIndex === 1) return `Signal: ${formatNumber(value, 3)}`;
+                            if (seriesIndex === 2) return `Histogram: ${formatNumber(value, 3)}`;
+                            if (seriesIndex === 3) return `RSI: ${formatNumber(value, 2)}`;
+                            return formatNumber(value, 2);
+                        }
+                    }
+                },
+                colors: ['#008FFB', '#00E396', '#CED4DC', '#FEB019'],
+                legend: {
+                    show: true,
+                    position: 'top',
+                    horizontalAlign: 'left',
+                    floating: false
+                },
+                // Explicitly set marker options to prevent errors
+                markers: {
+                    size: 4,
+                    strokeWidth: 0
+                }
+            };
+
+            $('#indicators-chart').empty();
+
+            const chart = new ApexCharts(document.querySelector("#indicators-chart"), indicatorOptions);
+
+            chart.render();
+
+        } catch (error) {
+            $('#indicators-chart').html('<div class="alert alert-danger">指标图表加载失败: ' + error.message + '</div>');
+        }
+    }
+
+    // 添加到script部分
+    $('#retry-button').click(function() {
+        // 隐藏错误和重试区域
+        $('#error-retry').hide();
+        // 重新加载分析
+        loadAnalysisResult();
+    });
+
+</script>
+{% endblock %}

us_stock_service.py

@@ -0,0 +1,67 @@
+# -*- coding: utf-8 -*-
+"""
+智能分析系统（股票） - 股票市场数据分析系统
+修改：熊猫大侠
+版本：v2.1.0
+"""
+# us_stock_service.py
+import akshare as ak
+import pandas as pd
+import logging
+
+
+class USStockService:
+    def __init__(self):
+        logging.basicConfig(level=logging.INFO,
+                            format='%(asctime)s - %(levelname)s - %(message)s')
+        self.logger = logging.getLogger(__name__)
+
+    def search_us_stocks(self, keyword):
+        """
+        搜索美股代码
+        :param keyword: 搜索关键词
+        :return: 匹配的股票列表
+        """
+        try:
+            # 获取美股数据
+            df = ak.stock_us_spot_em()
+
+            # 转换列名
+            df = df.rename(columns={
+                "序号": "index",
+                "名称": "name",
+                "最新价": "price",
+                "涨跌额": "price_change",
+                "涨跌幅": "price_change_percent",
+                "开盘价": "open",
+                "最高价": "high",
+                "最低价": "low",
+                "昨收价": "pre_close",
+                "总市值": "market_value",
+                "市盈率": "pe_ratio",
+                "成交量": "volume",
+                "成交额": "turnover",
+                "振幅": "amplitude",
+                "换手率": "turnover_rate",
+                "代码": "symbol"
+            })
+
+            # 模糊匹配搜索
+            mask = df['name'].str.contains(keyword, case=False, na=False)
+            results = df[mask]
+
+            # 格式化返回结果并处理 NaN 值
+            formatted_results = []
+            for _, row in results.iterrows():
+                formatted_results.append({
+                    'name': row['name'] if pd.notna(row['name']) else '',
+                    'symbol': str(row['symbol']) if pd.notna(row['symbol']) else '',
+                    'price': float(row['price']) if pd.notna(row['price']) else 0.0,
+                    'market_value': float(row['market_value']) if pd.notna(row['market_value']) else 0.0
+                })
+
+            return formatted_results
+
+        except Exception as e:
+            self.logger.error(f"搜索美股代码时出错: {str(e)}")
+            raise Exception(f"搜索美股代码失败: {str(e)}")

web_server.py

@@ -0,0 +1,1538 @@
+# -*- coding: utf-8 -*-
+"""
+智能分析系统（股票） - 股票市场数据分析系统
+修改：熊猫大侠
+版本：v2.1.0
+"""
+# web_server.py
+
+import numpy as np
+import pandas as pd
+from flask import Flask, render_template, request, jsonify, redirect, url_for
+from stock_analyzer import StockAnalyzer
+from us_stock_service import USStockService
+import threading
+import logging
+from logging.handlers import RotatingFileHandler
+import traceback
+import os
+import json
+from datetime import date, datetime, timedelta
+from flask_cors import CORS
+import time
+from flask_caching import Cache
+import threading
+import sys
+from flask_swagger_ui import get_swaggerui_blueprint
+from database import get_session, StockInfo, AnalysisResult, Portfolio, USE_DATABASE
+from dotenv import load_dotenv
+from industry_analyzer import IndustryAnalyzer
+
+# 加载环境变量
+load_dotenv()
+
+# 检查是否需要初始化数据库
+if USE_DATABASE:
+    init_db()
+
+# 配置Swagger
+SWAGGER_URL = '/api/docs'
+API_URL = '/static/swagger.json'
+swaggerui_blueprint = get_swaggerui_blueprint(
+    SWAGGER_URL,
+    API_URL,
+    config={
+        'app_name': "股票智能分析系统 API文档"
+    }
+)
+
+app = Flask(__name__)
+CORS(app, resources={r"/*": {"origins": "*"}})
+analyzer = StockAnalyzer()
+us_stock_service = USStockService()
+
+# 配置缓存
+cache_config = {
+    'CACHE_TYPE': 'SimpleCache',
+    'CACHE_DEFAULT_TIMEOUT': 300
+}
+
+# 如果配置了Redis，使用Redis作为缓存后端
+if os.getenv('USE_REDIS_CACHE', 'False').lower() == 'true' and os.getenv('REDIS_URL'):
+    cache_config = {
+        'CACHE_TYPE': 'RedisCache',
+        'CACHE_REDIS_URL': os.getenv('REDIS_URL'),
+        'CACHE_DEFAULT_TIMEOUT': 300
+    }
+
+cache = Cache(config={'CACHE_TYPE': 'SimpleCache'})
+cache.init_app(app)
+
+app.register_blueprint(swaggerui_blueprint, url_prefix=SWAGGER_URL)
+
+# 确保全局变量在重新加载时不会丢失
+if 'analyzer' not in globals():
+    try:
+        from stock_analyzer import StockAnalyzer
+
+        analyzer = StockAnalyzer()
+        print("成功初始化全局StockAnalyzer实例")
+    except Exception as e:
+        print(f"初始化StockAnalyzer时出错: {e}", file=sys.stderr)
+        raise
+
+# 导入新模块
+from fundamental_analyzer import FundamentalAnalyzer
+from capital_flow_analyzer import CapitalFlowAnalyzer
+from scenario_predictor import ScenarioPredictor
+from stock_qa import StockQA
+from risk_monitor import RiskMonitor
+from index_industry_analyzer import IndexIndustryAnalyzer
+
+# 初始化模块实例
+fundamental_analyzer = FundamentalAnalyzer()
+capital_flow_analyzer = CapitalFlowAnalyzer()
+scenario_predictor = ScenarioPredictor(analyzer, os.getenv('OPENAI_API_KEY'), os.getenv('OPENAI_API_MODEL'))
+stock_qa = StockQA(analyzer, os.getenv('OPENAI_API_KEY'), os.getenv('OPENAI_API_MODEL'))
+risk_monitor = RiskMonitor(analyzer)
+index_industry_analyzer = IndexIndustryAnalyzer(analyzer)
+industry_analyzer = IndustryAnalyzer()
+
+# 线程本地存储
+thread_local = threading.local()
+
+
+def get_analyzer():
+    """获取线程本地的分析器实例"""
+    # 如果线程本地存储中没有分析器实例，创建一个新的
+    if not hasattr(thread_local, 'analyzer'):
+        thread_local.analyzer = StockAnalyzer()
+    return thread_local.analyzer
+
+
+# 配置日志
+logging.basicConfig(level=logging.INFO)
+handler = RotatingFileHandler('flask_app.log', maxBytes=10000000, backupCount=5)
+handler.setFormatter(logging.Formatter(
+    '[%(asctime)s] %(levelname)s in %(module)s: %(message)s'
+))
+app.logger.addHandler(handler)
+
+# 扩展任务管理系统以支持不同类型的任务
+task_types = {
+    'scan': 'market_scan',  # 市场扫描任务
+    'analysis': 'stock_analysis'  # 个股分析任务
+}
+
+# 任务数据存储
+tasks = {
+    'market_scan': {},  # 原来的scan_tasks
+    'stock_analysis': {}  # 新的个股分析任务
+}
+
+
+def get_task_store(task_type):
+    """获取指定类型的任务存储"""
+    return tasks.get(task_type, {})
+
+
+def generate_task_key(task_type, **params):
+    """生成任务键"""
+    if task_type == 'stock_analysis':
+        # 对于个股分析，使用股票代码和市场类型作为键
+        return f"{params.get('stock_code')}_{params.get('market_type', 'A')}"
+    return None  # 其他任务类型不使用预生成的键
+
+
+def get_or_create_task(task_type, **params):
+    """获取或创建任务"""
+    store = get_task_store(task_type)
+    task_key = generate_task_key(task_type, **params)
+
+    # 检查是否有现有任务
+    if task_key and task_key in store:
+        task = store[task_key]
+        # 检查任务是否仍然有效
+        if task['status'] in [TASK_PENDING, TASK_RUNNING]:
+            return task['id'], task, False
+        if task['status'] == TASK_COMPLETED and 'result' in task:
+            # 任务已完成且有结果，重用它
+            return task['id'], task, False
+
+    # 创建新任务
+    task_id = generate_task_id()
+    task = {
+        'id': task_id,
+        'key': task_key,  # 存储任务键以便以后查找
+        'type': task_type,
+        'status': TASK_PENDING,
+        'progress': 0,
+        'created_at': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
+        'updated_at': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
+        'params': params
+    }
+
+    with task_lock:
+        if task_key:
+            store[task_key] = task
+        store[task_id] = task
+
+    return task_id, task, True
+
+
+# 添加到web_server.py顶部
+# 任务管理系统
+scan_tasks = {}  # 存储扫描任务的状态和结果
+task_lock = threading.Lock()  # 用于线程安全操作
+
+# 任务状态常量
+TASK_PENDING = 'pending'
+TASK_RUNNING = 'running'
+TASK_COMPLETED = 'completed'
+TASK_FAILED = 'failed'
+
+
+def generate_task_id():
+    """生成唯一的任务ID"""
+    import uuid
+    return str(uuid.uuid4())
+
+
+def start_market_scan_task_status(task_id, status, progress=None, result=None, error=None):
+    """更新任务状态 - 保持原有签名"""
+    with task_lock:
+        if task_id in scan_tasks:
+            task = scan_tasks[task_id]
+            task['status'] = status
+            if progress is not None:
+                task['progress'] = progress
+            if result is not None:
+                task['result'] = result
+            if error is not None:
+                task['error'] = error
+            task['updated_at'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+
+
+def update_task_status(task_type, task_id, status, progress=None, result=None, error=None):
+    """更新任务状态"""
+    store = get_task_store(task_type)
+    with task_lock:
+        if task_id in store:
+            task = store[task_id]
+            task['status'] = status
+            if progress is not None:
+                task['progress'] = progress
+            if result is not None:
+                task['result'] = result
+            if error is not None:
+                task['error'] = error
+            task['updated_at'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+
+            # 更新键索引的任务
+            if 'key' in task and task['key'] in store:
+                store[task['key']] = task
+
+
+analysis_tasks = {}
+
+
+def get_or_create_analysis_task(stock_code, market_type='A'):
+    """获取或创建个股分析任务"""
+    # 创建一个键，用于查找现有任务
+    task_key = f"{stock_code}_{market_type}"
+
+    with task_lock:
+        # 检查是否有现有任务
+        for task_id, task in analysis_tasks.items():
+            if task.get('key') == task_key:
+                # 检查任务是否仍然有效
+                if task['status'] in [TASK_PENDING, TASK_RUNNING]:
+                    return task_id, task, False
+                if task['status'] == TASK_COMPLETED and 'result' in task:
+                    # 任务已完成且有结果，重用它
+                    return task_id, task, False
+
+        # 创建新任务
+        task_id = generate_task_id()
+        task = {
+            'id': task_id,
+            'key': task_key,
+            'status': TASK_PENDING,
+            'progress': 0,
+            'created_at': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
+            'updated_at': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
+            'params': {
+                'stock_code': stock_code,
+                'market_type': market_type
+            }
+        }
+
+        analysis_tasks[task_id] = task
+
+        return task_id, task, True
+
+
+def update_analysis_task(task_id, status, progress=None, result=None, error=None):
+    """更新个股分析任务状态"""
+    with task_lock:
+        if task_id in analysis_tasks:
+            task = analysis_tasks[task_id]
+            task['status'] = status
+            if progress is not None:
+                task['progress'] = progress
+            if result is not None:
+                task['result'] = result
+            if error is not None:
+                task['error'] = error
+            task['updated_at'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+
+
+# 定义自定义JSON编码器
+
+
+# 在web_server.py中，更新convert_numpy_types函数以处理NaN值
+
+# 将NumPy类型转换为Python原生类型的函数
+def convert_numpy_types(obj):
+    """递归地将字典和列表中的NumPy类型转换为Python原生类型"""
+    try:
+        import numpy as np
+        import math
+
+        if isinstance(obj, dict):
+            return {key: convert_numpy_types(value) for key, value in obj.items()}
+        elif isinstance(obj, list):
+            return [convert_numpy_types(item) for item in obj]
+        elif isinstance(obj, np.integer):
+            return int(obj)
+        elif isinstance(obj, np.floating):
+            # Handle NaN and Infinity specifically
+            if np.isnan(obj):
+                return None
+            elif np.isinf(obj):
+                return None if obj < 0 else 1e308  # Use a very large number for +Infinity
+            return float(obj)
+        elif isinstance(obj, np.ndarray):
+            return obj.tolist()
+        elif isinstance(obj, np.bool_):
+            return bool(obj)
+        # Handle Python's own float NaN and Infinity
+        elif isinstance(obj, float):
+            if math.isnan(obj):
+                return None
+            elif math.isinf(obj):
+                return None
+            return obj
+        # 添加对date和datetime类型的处理
+        elif isinstance(obj, (date, datetime)):
+            return obj.isoformat()
+        else:
+            return obj
+    except ImportError:
+        # 如果没有安装numpy，但需要处理date和datetime
+        import math
+        if isinstance(obj, dict):
+            return {key: convert_numpy_types(value) for key, value in obj.items()}
+        elif isinstance(obj, list):
+            return [convert_numpy_types(item) for item in obj]
+        elif isinstance(obj, (date, datetime)):
+            return obj.isoformat()
+        # Handle Python's own float NaN and Infinity
+        elif isinstance(obj, float):
+            if math.isnan(obj):
+                return None
+            elif math.isinf(obj):
+                return None
+            return obj
+        return obj
+
+
+# 同样更新 NumpyJSONEncoder 类
+class NumpyJSONEncoder(json.JSONEncoder):
+    def default(self, obj):
+        # For NumPy data types
+        try:
+            import numpy as np
+            import math
+            if isinstance(obj, np.integer):
+                return int(obj)
+            elif isinstance(obj, np.floating):
+                # Handle NaN and Infinity specifically
+                if np.isnan(obj):
+                    return None
+                elif np.isinf(obj):
+                    return None
+                return float(obj)
+            elif isinstance(obj, np.ndarray):
+                return obj.tolist()
+            elif isinstance(obj, np.bool_):
+                return bool(obj)
+            # Handle Python's own float NaN and Infinity
+            elif isinstance(obj, float):
+                if math.isnan(obj):
+                    return None
+                elif math.isinf(obj):
+                    return None
+                return obj
+        except ImportError:
+            # Handle Python's own float NaN and Infinity if numpy is not available
+            import math
+            if isinstance(obj, float):
+                if math.isnan(obj):
+                    return None
+                elif math.isinf(obj):
+                    return None
+
+        # 添加对date和datetime类型的处理
+        if isinstance(obj, (date, datetime)):
+            return obj.isoformat()
+
+        return super(NumpyJSONEncoder, self).default(obj)
+
+
+# 使用我们的编码器的自定义 jsonify 函数
+def custom_jsonify(data):
+    return app.response_class(
+        json.dumps(convert_numpy_types(data), cls=NumpyJSONEncoder),
+        mimetype='application/json'
+    )
+
+
+# 保持API兼容的路由
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+
+@app.route('/analyze', methods=['POST'])
+def analyze():
+    try:
+        data = request.json
+        stock_codes = data.get('stock_codes', [])
+        market_type = data.get('market_type', 'A')
+
+        if not stock_codes:
+            return jsonify({'error': '请输入代码'}), 400
+
+        app.logger.info(f"分析股票请求: {stock_codes}, 市场类型: {market_type}")
+
+        # 设置最大处理时间，每只股票10秒
+        max_time_per_stock = 10  # 秒
+        max_total_time = max(30, min(60, len(stock_codes) * max_time_per_stock))  # 至少30秒，最多60秒
+
+        start_time = time.time()
+        results = []
+
+        for stock_code in stock_codes:
+            try:
+                # 检查是否已超时
+                if time.time() - start_time > max_total_time:
+                    app.logger.warning(f"分析股票请求已超过{max_total_time}秒，提前返回已处理的{len(results)}只股票")
+                    break
+
+                # 使用线程本地缓存的分析器实例
+                current_analyzer = get_analyzer()
+                result = current_analyzer.quick_analyze_stock(stock_code.strip(), market_type)
+
+                app.logger.info(
+                    f"分析结果: 股票={stock_code}, 名称={result.get('stock_name', '未知')}, 行业={result.get('industry', '未知')}")
+                results.append(result)
+            except Exception as e:
+                app.logger.error(f"分析股票 {stock_code} 时出错: {str(e)}")
+                results.append({
+                    'stock_code': stock_code,
+                    'error': str(e),
+                    'stock_name': '分析失败',
+                    'industry': '未知'
+                })
+
+        return jsonify({'results': results})
+    except Exception as e:
+        app.logger.error(f"分析股票时出错: {traceback.format_exc()}")
+        return jsonify({'error': str(e)}), 500
+
+
+@app.route('/api/north_flow_history', methods=['POST'])
+def api_north_flow_history():
+    try:
+        data = request.json
+        stock_code = data.get('stock_code')
+        days = data.get('days', 10)  # 默认为10天，对应前端的默认选项
+
+        # 计算 end_date 为当前时间
+        end_date = datetime.now().strftime('%Y%m%d')
+
+        # 计算 start_date 为 end_date 减去指定的天数
+        start_date = (datetime.now() - timedelta(days=int(days))).strftime('%Y%m%d')
+
+        if not stock_code:
+            return jsonify({'error': '请提供股票代码'}), 400
+
+        # 调用北向资金历史数据方法
+        from capital_flow_analyzer import CapitalFlowAnalyzer
+
+        analyzer = CapitalFlowAnalyzer()
+        result = analyzer.get_north_flow_history(stock_code, start_date, end_date)
+
+        return custom_jsonify(result)
+    except Exception as e:
+        app.logger.error(f"获取北向资金历史数据出错: {traceback.format_exc()}")
+        return jsonify({'error': str(e)}), 500
+
+
+@app.route('/search_us_stocks', methods=['GET'])
+def search_us_stocks():
+    try:
+        keyword = request.args.get('keyword', '')
+        if not keyword:
+            return jsonify({'error': '请输入搜索关键词'}), 400
+
+        results = us_stock_service.search_us_stocks(keyword)
+        return jsonify({'results': results})
+
+    except Exception as e:
+        app.logger.error(f"搜索美股代码时出错: {str(e)}")
+        return jsonify({'error': str(e)}), 500
+
+
+# 新增可视化分析页面路由
+@app.route('/dashboard')
+def dashboard():
+    return render_template('dashboard.html')
+
+
+@app.route('/stock_detail/<string:stock_code>')
+def stock_detail(stock_code):
+    market_type = request.args.get('market_type', 'A')
+    return render_template('stock_detail.html', stock_code=stock_code, market_type=market_type)
+
+
+@app.route('/portfolio')
+def portfolio():
+    return render_template('portfolio.html')
+
+
+@app.route('/market_scan')
+def market_scan():
+    return render_template('market_scan.html')
+
+
+# 基本面分析页面
+@app.route('/fundamental')
+def fundamental():
+    return render_template('fundamental.html')
+
+
+# 资金流向页面
+@app.route('/capital_flow')
+def capital_flow():
+    return render_template('capital_flow.html')
+
+
+# 情景预测页面
+@app.route('/scenario_predict')
+def scenario_predict():
+    return render_template('scenario_predict.html')
+
+
+# 风险监控页面
+@app.route('/risk_monitor')
+def risk_monitor_page():
+    return render_template('risk_monitor.html')
+
+
+# 智能问答页面
+@app.route('/qa')
+def qa_page():
+    return render_template('qa.html')
+
+
+# 行业分析页面
+@app.route('/industry_analysis')
+def industry_analysis():
+    return render_template('industry_analysis.html')
+
+
+def make_cache_key_with_stock():
+    """创建包含股票代码的自定义缓存键"""
+    path = request.path
+
+    # 从请求体中获取股票代码
+    stock_code = None
+    if request.is_json:
+        stock_code = request.json.get('stock_code')
+
+    # 构建包含股票代码的键
+    if stock_code:
+        return f"{path}_{stock_code}"
+    else:
+        return path
+
+
+@app.route('/api/start_stock_analysis', methods=['POST'])
+def start_stock_analysis():
+    """启动个股分析任务"""
+    try:
+        data = request.json
+        stock_code = data.get('stock_code')
+        market_type = data.get('market_type', 'A')
+
+        if not stock_code:
+            return jsonify({'error': '请输入股票代码'}), 400
+
+        app.logger.info(f"准备分析股票: {stock_code}")
+
+        # 获取或创建任务
+        task_id, task, is_new = get_or_create_task(
+            'stock_analysis',
+            stock_code=stock_code,
+            market_type=market_type
+        )
+
+        # 如果是已完成的任务，直接返回结果
+        if task['status'] == TASK_COMPLETED and 'result' in task:
+            app.logger.info(f"使用缓存的分析结果: {stock_code}")
+            return jsonify({
+                'task_id': task_id,
+                'status': task['status'],
+                'result': task['result']
+            })
+
+        # 如果是新创建的任务，启动后台处理
+        if is_new:
+            app.logger.info(f"创建新的分析任务: {task_id}")
+
+            # 启动后台线程执行分析
+            def run_analysis():
+                try:
+                    update_task_status('stock_analysis', task_id, TASK_RUNNING, progress=10)
+
+                    # 执行分析
+                    result = analyzer.perform_enhanced_analysis(stock_code, market_type)
+
+                    # 更新任务状态为完成
+                    update_task_status('stock_analysis', task_id, TASK_COMPLETED, progress=100, result=result)
+                    app.logger.info(f"分析任务 {task_id} 完成")
+
+                except Exception as e:
+                    app.logger.error(f"分析任务 {task_id} 失败: {str(e)}")
+                    app.logger.error(traceback.format_exc())
+                    update_task_status('stock_analysis', task_id, TASK_FAILED, error=str(e))
+
+            # 启动后台线程
+            thread = threading.Thread(target=run_analysis)
+            thread.daemon = True
+            thread.start()
+
+        # 返回任务ID和状态
+        return jsonify({
+            'task_id': task_id,
+            'status': task['status'],
+            'message': f'已启动分析任务: {stock_code}'
+        })
+
+    except Exception as e:
+        app.logger.error(f"启动个股分析任务时出错: {traceback.format_exc()}")
+        return jsonify({'error': str(e)}), 500
+
+
+@app.route('/api/analysis_status/<task_id>', methods=['GET'])
+def get_analysis_status(task_id):
+    """获取个股分析任务状态"""
+    store = get_task_store('stock_analysis')
+    with task_lock:
+        if task_id not in store:
+            return jsonify({'error': '找不到指定的分析任务'}), 404
+
+        task = store[task_id]
+
+        # 基本状态信息
+        status = {
+            'id': task['id'],
+            'status': task['status'],
+            'progress': task.get('progress', 0),
+            'created_at': task['created_at'],
+            'updated_at': task['updated_at']
+        }
+
+        # 如果任务完成，包含结果
+        if task['status'] == TASK_COMPLETED and 'result' in task:
+            status['result'] = task['result']
+
+        # 如果任务失败，包含错误信息
+        if task['status'] == TASK_FAILED and 'error' in task:
+            status['error'] = task['error']
+
+        return custom_jsonify(status)
+
+
+@app.route('/api/cancel_analysis/<task_id>', methods=['POST'])
+def cancel_analysis(task_id):
+    """取消个股分析任务"""
+    store = get_task_store('stock_analysis')
+    with task_lock:
+        if task_id not in store:
+            return jsonify({'error': '找不到指定的分析任务'}), 404
+
+        task = store[task_id]
+
+        if task['status'] in [TASK_COMPLETED, TASK_FAILED]:
+            return jsonify({'message': '任务已完成或失败，无法取消'})
+
+        # 更新状态为失败
+        task['status'] = TASK_FAILED
+        task['error'] = '用户取消任务'
+        task['updated_at'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+
+        # 更新键索引的任务
+        if 'key' in task and task['key'] in store:
+            store[task['key']] = task
+
+        return jsonify({'message': '任务已取消'})
+
+
+# 保留原有API用于向后兼容
+@app.route('/api/enhanced_analysis', methods=['POST'])
+def enhanced_analysis():
+    """原增强分析API的向后兼容版本"""
+    try:
+        data = request.json
+        stock_code = data.get('stock_code')
+        market_type = data.get('market_type', 'A')
+
+        if not stock_code:
+            return custom_jsonify({'error': '请输入股票代码'}), 400
+
+        # 调用新的任务系统，但模拟同步行为
+        # 这会导致和之前一样的超时问题，但保持兼容
+        timeout = 300
+        start_time = time.time()
+
+        # 获取或创建任务
+        task_id, task, is_new = get_or_create_task(
+            'stock_analysis',
+            stock_code=stock_code,
+            market_type=market_type
+        )
+
+        # 如果是已完成的任务，直接返回结果
+        if task['status'] == TASK_COMPLETED and 'result' in task:
+            app.logger.info(f"使用缓存的分析结果: {stock_code}")
+            return custom_jsonify({'result': task['result']})
+
+        # 启动分析（如果是新任务）
+        if is_new:
+            # 同步执行分析
+            try:
+                result = analyzer.perform_enhanced_analysis(stock_code, market_type)
+                update_task_status('stock_analysis', task_id, TASK_COMPLETED, progress=100, result=result)
+                app.logger.info(f"分析完成: {stock_code}，耗时 {time.time() - start_time:.2f} 秒")
+                return custom_jsonify({'result': result})
+            except Exception as e:
+                app.logger.error(f"分析过程中出错: {str(e)}")
+                update_task_status('stock_analysis', task_id, TASK_FAILED, error=str(e))
+                return custom_jsonify({'error': f'分析过程中出错: {str(e)}'}), 500
+        else:
+            # 已存在正在处理��任务，等待其完成
+            max_wait = timeout - (time.time() - start_time)
+            wait_interval = 0.5
+            waited = 0
+
+            while waited < max_wait:
+                with task_lock:
+                    current_task = store[task_id]
+                    if current_task['status'] == TASK_COMPLETED and 'result' in current_task:
+                        return custom_jsonify({'result': current_task['result']})
+                    if current_task['status'] == TASK_FAILED:
+                        error = current_task.get('error', '任务失败，无详细信息')
+                        return custom_jsonify({'error': error}), 500
+
+                time.sleep(wait_interval)
+                waited += wait_interval
+
+            # 超时
+            return custom_jsonify({'error': '处理超时，请稍后重试'}), 504
+
+    except Exception as e:
+        app.logger.error(f"执行增强版分析时出错: {traceback.format_exc()}")
+        return custom_jsonify({'error': str(e)}), 500
+
+
+# 添加在web_server.py主代码中
+@app.errorhandler(404)
+def not_found(error):
+    """处理404错误"""
+    if request.path.startswith('/api/'):
+        # 为API请求返回JSON格式的错误
+        return jsonify({
+            'error': '找不到请求的API端点',
+            'path': request.path,
+            'method': request.method
+        }), 404
+    # 为网页请求返回HTML错误页
+    return render_template('error.html', error_code=404, message="找不到请求的页面"), 404
+
+
+@app.errorhandler(500)
+def server_error(error):
+    """处理500错误"""
+    app.logger.error(f"服务器错误: {str(error)}")
+    if request.path.startswith('/api/'):
+        # 为API请求返回JSON格式的错误
+        return jsonify({
+            'error': '服务器内部错误',
+            'message': str(error)
+        }), 500
+    # 为网页请求返回HTML错误页
+    return render_template('error.html', error_code=500, message="服务器内部错误"), 500
+
+
+# Update the get_stock_data function in web_server.py to handle date formatting properly
+@app.route('/api/stock_data', methods=['GET'])
+@cache.cached(timeout=300, query_string=True)
+def get_stock_data():
+    try:
+        stock_code = request.args.get('stock_code')
+        market_type = request.args.get('market_type', 'A')
+        period = request.args.get('period', '1y')  # 默认1年
+
+        if not stock_code:
+            return custom_jsonify({'error': '请提供股票代码'}), 400
+
+        # 根据period计算start_date
+        end_date = datetime.now().strftime('%Y%m%d')
+        if period == '1m':
+            start_date = (datetime.now() - timedelta(days=30)).strftime('%Y%m%d')
+        elif period == '3m':
+            start_date = (datetime.now() - timedelta(days=90)).strftime('%Y%m%d')
+        elif period == '6m':
+            start_date = (datetime.now() - timedelta(days=180)).strftime('%Y%m%d')
+        elif period == '1y':
+            start_date = (datetime.now() - timedelta(days=365)).strftime('%Y%m%d')
+        else:
+            start_date = (datetime.now() - timedelta(days=365)).strftime('%Y%m%d')
+
+        # 获取股票历史数据
+        app.logger.info(
+            f"获取股票 {stock_code} 的历史数据，市场: {market_type}, 起始日期: {start_date}, 结束日期: {end_date}")
+        df = analyzer.get_stock_data(stock_code, market_type, start_date, end_date)
+
+        # 计算技术指标
+        app.logger.info(f"计算股票 {stock_code} 的技术指标")
+        df = analyzer.calculate_indicators(df)
+
+        # 检查数据是否为空
+        if df.empty:
+            app.logger.warning(f"股票 {stock_code} 的数据为空")
+            return custom_jsonify({'error': '未找到股票数据'}), 404
+
+        # 将DataFrame转为JSON格式
+        app.logger.info(f"将数据转换为JSON格式，行数: {len(df)}")
+
+        # 确保日期列是字符串格式 - 修复缓存问题
+        if 'date' in df.columns:
+            try:
+                if pd.api.types.is_datetime64_any_dtype(df['date']):
+                    df['date'] = df['date'].dt.strftime('%Y-%m-%d')
+                else:
+                    df = df.copy()
+                    df['date'] = pd.to_datetime(df['date'], errors='coerce')
+                    df['date'] = df['date'].dt.strftime('%Y-%m-%d')
+            except Exception as e:
+                app.logger.error(f"处理日期列时出错: {str(e)}")
+                df['date'] = df['date'].astype(str)
+
+        # 将NaN值替换为None
+        df = df.replace({np.nan: None, np.inf: None, -np.inf: None})
+
+        records = df.to_dict('records')
+
+        app.logger.info(f"数据处理完成，返回 {len(records)} 条记录")
+        return custom_jsonify({'data': records})
+    except Exception as e:
+        app.logger.error(f"获取股票数据时出错: {str(e)}")
+        app.logger.error(traceback.format_exc())
+        return custom_jsonify({'error': str(e)}), 500
+
+
+# @app.route('/api/market_scan', methods=['POST'])
+# def api_market_scan():
+#     try:
+#         data = request.json
+#         stock_list = data.get('stock_list', [])
+#         min_score = data.get('min_score', 60)
+#         market_type = data.get('market_type', 'A')
+
+#         if not stock_list:
+#             return jsonify({'error': '请提供股票列表'}), 400
+
+#         # 限制股票数量，避免过长处理时间
+#         if len(stock_list) > 100:
+#             app.logger.warning(f"股票列表过长 ({len(stock_list)}只)，截取前100只")
+#             stock_list = stock_list[:100]
+
+#         # 执行市场扫描
+#         app.logger.info(f"开始扫描 {len(stock_list)} 只股票，最低分数: {min_score}")
+
+#         # 使用线程池优化处理
+#         results = []
+#         max_workers = min(10, len(stock_list))  # 最多10个工作线程
+
+#         # 设置较长的超时时间
+#         timeout = 300  # 5分钟
+
+#         def scan_thread():
+#             try:
+#                 return analyzer.scan_market(stock_list, min_score, market_type)
+#             except Exception as e:
+#                 app.logger.error(f"扫描线程出错: {str(e)}")
+#                 return []
+
+#         thread = threading.Thread(target=lambda: results.append(scan_thread()))
+#         thread.start()
+#         thread.join(timeout)
+
+#         if thread.is_alive():
+#             app.logger.error(f"市场扫描超时，已扫描 {len(stock_list)} 只股票超过 {timeout} 秒")
+#             return custom_jsonify({'error': '扫描超时，请减少股票数量或稍后再试'}), 504
+
+#         if not results or not results[0]:
+#             app.logger.warning("扫描结果为空")
+#             return custom_jsonify({'results': []})
+
+#         scan_results = results[0]
+#         app.logger.info(f"扫描完成，找到 {len(scan_results)} 只符合条件的股票")
+
+#         # 使用自定义JSON格式处理NumPy数据类型
+#         return custom_jsonify({'results': scan_results})
+#     except Exception as e:
+#         app.logger.error(f"执行市场扫描时出错: {traceback.format_exc()}")
+#         return custom_jsonify({'error': str(e)}), 500
+
+@app.route('/api/start_market_scan', methods=['POST'])
+def start_market_scan():
+    """启动市场扫描任务"""
+    try:
+        data = request.json
+        stock_list = data.get('stock_list', [])
+        min_score = data.get('min_score', 60)
+        market_type = data.get('market_type', 'A')
+
+        if not stock_list:
+            return jsonify({'error': '请提供股票列表'}), 400
+
+        # 限制股票数量，避免过长处理时间
+        if len(stock_list) > 100:
+            app.logger.warning(f"股票列表过长 ({len(stock_list)}只)，截取前100只")
+            stock_list = stock_list[:100]
+
+        # 创建新任务
+        task_id = generate_task_id()
+        task = {
+            'id': task_id,
+            'status': TASK_PENDING,
+            'progress': 0,
+            'total': len(stock_list),
+            'created_at': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
+            'updated_at': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
+            'params': {
+                'stock_list': stock_list,
+                'min_score': min_score,
+                'market_type': market_type
+            }
+        }
+
+        with task_lock:
+            scan_tasks[task_id] = task
+
+        # 启动后台线程执行扫描
+        def run_scan():
+            try:
+                start_market_scan_task_status(task_id, TASK_RUNNING)
+
+                # 执行分批处理
+                results = []
+                total = len(stock_list)
+                batch_size = 10
+
+                for i in range(0, total, batch_size):
+                    if task_id not in scan_tasks or scan_tasks[task_id]['status'] != TASK_RUNNING:
+                        # 任务被取消
+                        app.logger.info(f"扫描任务 {task_id} 被取消")
+                        return
+
+                    batch = stock_list[i:i + batch_size]
+                    batch_results = []
+
+                    for stock_code in batch:
+                        try:
+                            report = analyzer.quick_analyze_stock(stock_code, market_type)
+                            if report['score'] >= min_score:
+                                batch_results.append(report)
+                        except Exception as e:
+                            app.logger.error(f"分析股票 {stock_code} 时出错: {str(e)}")
+                            continue
+
+                    results.extend(batch_results)
+
+                    # 更新进度
+                    progress = min(100, int((i + len(batch)) / total * 100))
+                    start_market_scan_task_status(task_id, TASK_RUNNING, progress=progress)
+
+                # 按得分排序
+                results.sort(key=lambda x: x['score'], reverse=True)
+
+                # 更新任务状态为完成
+                start_market_scan_task_status(task_id, TASK_COMPLETED, progress=100, result=results)
+                app.logger.info(f"扫描任务 {task_id} 完成，找到 {len(results)} 只符合条件的股票")
+
+            except Exception as e:
+                app.logger.error(f"扫描任务 {task_id} 失败: {str(e)}")
+                app.logger.error(traceback.format_exc())
+                start_market_scan_task_status(task_id, TASK_FAILED, error=str(e))
+
+        # 启动后台线程
+        thread = threading.Thread(target=run_scan)
+        thread.daemon = True
+        thread.start()
+
+        return jsonify({
+            'task_id': task_id,
+            'status': 'pending',
+            'message': f'已启动扫描任务，正在处理 {len(stock_list)} 只股票'
+        })
+
+    except Exception as e:
+        app.logger.error(f"启动市场扫描任务时出错: {traceback.format_exc()}")
+        return jsonify({'error': str(e)}), 500
+
+
+@app.route('/api/scan_status/<task_id>', methods=['GET'])
+def get_scan_status(task_id):
+    """获取扫描任务状态"""
+    with task_lock:
+        if task_id not in scan_tasks:
+            return jsonify({'error': '找不到指定的扫描任务'}), 404
+
+        task = scan_tasks[task_id]
+
+        # 基本状态信息
+        status = {
+            'id': task['id'],
+            'status': task['status'],
+            'progress': task.get('progress', 0),
+            'total': task.get('total', 0),
+            'created_at': task['created_at'],
+            'updated_at': task['updated_at']
+        }
+
+        # 如果任务完成，包含结果
+        if task['status'] == TASK_COMPLETED and 'result' in task:
+            status['result'] = task['result']
+
+        # 如果任务失败，包含错误信息
+        if task['status'] == TASK_FAILED and 'error' in task:
+            status['error'] = task['error']
+
+        return custom_jsonify(status)
+
+
+@app.route('/api/cancel_scan/<task_id>', methods=['POST'])
+def cancel_scan(task_id):
+    """取消扫描任务"""
+    with task_lock:
+        if task_id not in scan_tasks:
+            return jsonify({'error': '找不到指定的扫描任务'}), 404
+
+        task = scan_tasks[task_id]
+
+        if task['status'] in [TASK_COMPLETED, TASK_FAILED]:
+            return jsonify({'message': '任务已完成或失败，无法取消'})
+
+        # 更新状态为失败
+        task['status'] = TASK_FAILED
+        task['error'] = '用户取消任务'
+        task['updated_at'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+
+        return jsonify({'message': '任务已取消'})
+
+
+@app.route('/api/index_stocks', methods=['GET'])
+def get_index_stocks():
+    """获取指数成分股"""
+    try:
+        import akshare as ak
+        index_code = request.args.get('index_code', '000300')  # 默认沪深300
+
+        # 获取指数成分股
+        app.logger.info(f"获取指数 {index_code} 成分股")
+        if index_code == '000300':
+            # 沪深300成分股
+            stocks = ak.index_stock_cons_weight_csindex(symbol="000300")
+        elif index_code == '000905':
+            # 中证500成分股
+            stocks = ak.index_stock_cons_weight_csindex(symbol="000905")
+        elif index_code == '000852':
+            # 中证1000成分股
+            stocks = ak.index_stock_cons_weight_csindex(symbol="000852")
+        elif index_code == '000001':
+            # 上证指数
+            stocks = ak.index_stock_cons_weight_csindex(symbol="000001")
+        else:
+            return jsonify({'error': '不支持的指数代码'}), 400
+
+        # 提取股票代码列表
+        stock_list = stocks['成分券代码'].tolist() if '成分券代码' in stocks.columns else []
+        app.logger.info(f"找到 {len(stock_list)} 只成分股")
+
+        return jsonify({'stock_list': stock_list})
+    except Exception as e:
+        app.logger.error(f"获取指数成分股时出错: {traceback.format_exc()}")
+        return jsonify({'error': str(e)}), 500
+
+
+@app.route('/api/industry_stocks', methods=['GET'])
+def get_industry_stocks():
+    """获取行业成分股"""
+    try:
+        import akshare as ak
+        industry = request.args.get('industry', '')
+
+        if not industry:
+            return jsonify({'error': '请提供行业名称'}), 400
+
+        # 获取行业成分股
+        app.logger.info(f"获取 {industry} 行业成分股")
+        stocks = ak.stock_board_industry_cons_em(symbol=industry)
+
+        # 提取股票代码列表
+        stock_list = stocks['代码'].tolist() if '代码' in stocks.columns else []
+        app.logger.info(f"找到 {len(stock_list)} 只 {industry} 行业股票")
+
+        return jsonify({'stock_list': stock_list})
+    except Exception as e:
+        app.logger.error(f"获取行业成分股时出错: {traceback.format_exc()}")
+        return jsonify({'error': str(e)}), 500
+
+
+# 添加到web_server.py
+def clean_old_tasks():
+    """清理旧的扫描任务"""
+    with task_lock:
+        now = datetime.now()
+        to_delete = []
+
+        for task_id, task in scan_tasks.items():
+            # 解析更新时间
+            try:
+                updated_at = datetime.strptime(task['updated_at'], '%Y-%m-%d %H:%M:%S')
+                # 如果任务完成或失败且超过1小时，或者任务状态异常且超过3小时，清理它
+                if ((task['status'] in [TASK_COMPLETED, TASK_FAILED] and
+                     (now - updated_at).total_seconds() > 3600) or
+                        ((now - updated_at).total_seconds() > 10800)):
+                    to_delete.append(task_id)
+            except:
+                # 日期解析错误，添加到删除列表
+                to_delete.append(task_id)
+
+        # 删除旧任务
+        for task_id in to_delete:
+            del scan_tasks[task_id]
+
+        return len(to_delete)
+
+
+# 修改 run_task_cleaner 函数，使其每 5 分钟运行一次并在 16:30 左右清理所有缓存
+def run_task_cleaner():
+    """定期运行任务清理，并在每天 16:30 左右清理所有缓存"""
+    while True:
+        try:
+            now = datetime.now()
+            # 判断是否在收盘时间附近（16:25-16:35）
+            is_market_close_time = (now.hour == 16 and 25 <= now.minute <= 35)
+
+            cleaned = clean_old_tasks()
+
+            # 如果是收盘时间，清理所有缓存
+            if is_market_close_time:
+                # 清理分析器的数据缓存
+                analyzer.data_cache.clear()
+
+                # 清理 Flask 缓存
+                cache.clear()
+
+                # 清理任务存储
+                with task_lock:
+                    for task_type in tasks:
+                        task_store = tasks[task_type]
+                        completed_tasks = [task_id for task_id, task in task_store.items()
+                                           if task['status'] == TASK_COMPLETED]
+                        for task_id in completed_tasks:
+                            del task_store[task_id]
+
+                app.logger.info("市场收盘时间检测到，已清理所有缓存数据")
+
+            if cleaned > 0:
+                app.logger.info(f"清理了 {cleaned} 个旧的扫描任务")
+        except Exception as e:
+            app.logger.error(f"任务清理出错: {str(e)}")
+
+        # 每 5 分钟运行一次，而不是每小时
+        time.sleep(600)
+
+
+# 基本面分析路由
+@app.route('/api/fundamental_analysis', methods=['POST'])
+def api_fundamental_analysis():
+    try:
+        data = request.json
+        stock_code = data.get('stock_code')
+
+        if not stock_code:
+            return jsonify({'error': '请提供股票代码'}), 400
+
+        # 获取基本面分析结果
+        result = fundamental_analyzer.calculate_fundamental_score(stock_code)
+
+        return custom_jsonify(result)
+    except Exception as e:
+        app.logger.error(f"基本面分析出错: {traceback.format_exc()}")
+        return jsonify({'error': str(e)}), 500
+
+
+# 资金流向分析路由
+# Add to web_server.py
+
+# 获取概念资金流向的API端点
+@app.route('/api/concept_fund_flow', methods=['GET'])
+def api_concept_fund_flow():
+    try:
+        period = request.args.get('period', '10日排行')  # Default to 10-day ranking
+
+        # Get concept fund flow data
+        result = capital_flow_analyzer.get_concept_fund_flow(period)
+
+        return custom_jsonify(result)
+    except Exception as e:
+        app.logger.error(f"Error getting concept fund flow: {traceback.format_exc()}")
+        return jsonify({'error': str(e)}), 500
+
+
+# 获取个股资金流向排名的API端点
+@app.route('/api/individual_fund_flow_rank', methods=['GET'])
+def api_individual_fund_flow_rank():
+    try:
+        period = request.args.get('period', '10日')  # Default to today
+
+        # Get individual fund flow ranking data
+        result = capital_flow_analyzer.get_individual_fund_flow_rank(period)
+
+        return custom_jsonify(result)
+    except Exception as e:
+        app.logger.error(f"Error getting individual fund flow ranking: {traceback.format_exc()}")
+        return jsonify({'error': str(e)}), 500
+
+
+# 获取个股资金流向的API端点
+@app.route('/api/individual_fund_flow', methods=['GET'])
+def api_individual_fund_flow():
+    try:
+        stock_code = request.args.get('stock_code')
+        market_type = request.args.get('market_type', '')  # Auto-detect if not provided
+        re_date = request.args.get('period-select')
+
+        if not stock_code:
+            return jsonify({'error': 'Stock code is required'}), 400
+
+        # Get individual fund flow data
+        result = capital_flow_analyzer.get_individual_fund_flow(stock_code, market_type, re_date)
+        return custom_jsonify(result)
+    except Exception as e:
+        app.logger.error(f"Error getting individual fund flow: {traceback.format_exc()}")
+        return jsonify({'error': str(e)}), 500
+
+
+# 获取板块内股票的API端点
+@app.route('/api/sector_stocks', methods=['GET'])
+def api_sector_stocks():
+    try:
+        sector = request.args.get('sector')
+
+        if not sector:
+            return jsonify({'error': 'Sector name is required'}), 400
+
+        # Get sector stocks data
+        result = capital_flow_analyzer.get_sector_stocks(sector)
+
+        return custom_jsonify(result)
+    except Exception as e:
+        app.logger.error(f"Error getting sector stocks: {traceback.format_exc()}")
+        return jsonify({'error': str(e)}), 500
+
+
+# Update the existing capital flow API endpoint
+@app.route('/api/capital_flow', methods=['POST'])
+def api_capital_flow():
+    try:
+        data = request.json
+        stock_code = data.get('stock_code')
+        market_type = data.get('market_type', '')  # Auto-detect if not provided
+
+        if not stock_code:
+            return jsonify({'error': 'Stock code is required'}), 400
+
+        # Calculate capital flow score
+        result = capital_flow_analyzer.calculate_capital_flow_score(stock_code, market_type)
+
+        return custom_jsonify(result)
+    except Exception as e:
+        app.logger.error(f"Error calculating capital flow score: {traceback.format_exc()}")
+        return jsonify({'error': str(e)}), 500
+
+
+# 情景预测路由
+@app.route('/api/scenario_predict', methods=['POST'])
+def api_scenario_predict():
+    try:
+        data = request.json
+        stock_code = data.get('stock_code')
+        market_type = data.get('market_type', 'A')
+        days = data.get('days', 60)
+
+        if not stock_code:
+            return jsonify({'error': '请提供股票代码'}), 400
+
+        # 获取情景预测结果
+        result = scenario_predictor.generate_scenarios(stock_code, market_type, days)
+
+        return custom_jsonify(result)
+    except Exception as e:
+        app.logger.error(f"情景预测出错: {traceback.format_exc()}")
+        return jsonify({'error': str(e)}), 500
+
+
+# 智能问答路由
+@app.route('/api/qa', methods=['POST'])
+def api_qa():
+    try:
+        data = request.json
+        stock_code = data.get('stock_code')
+        question = data.get('question')
+        market_type = data.get('market_type', 'A')
+
+        if not stock_code or not question:
+            return jsonify({'error': '请提供股票代码和问题'}), 400
+
+        # 获取智能问答结果
+        result = stock_qa.answer_question(stock_code, question, market_type)
+
+        return custom_jsonify(result)
+    except Exception as e:
+        app.logger.error(f"智能问答出错: {traceback.format_exc()}")
+        return jsonify({'error': str(e)}), 500
+
+
+# 风险分析路由
+@app.route('/api/risk_analysis', methods=['POST'])
+def api_risk_analysis():
+    try:
+        data = request.json
+        stock_code = data.get('stock_code')
+        market_type = data.get('market_type', 'A')
+
+        if not stock_code:
+            return jsonify({'error': '请提供股票代码'}), 400
+
+        # 获取风险分析结果
+        result = risk_monitor.analyze_stock_risk(stock_code, market_type)
+
+        return custom_jsonify(result)
+    except Exception as e:
+        app.logger.error(f"风险分析出错: {traceback.format_exc()}")
+        return jsonify({'error': str(e)}), 500
+
+
+# 投资组合风险分析路由
+@app.route('/api/portfolio_risk', methods=['POST'])
+def api_portfolio_risk():
+    try:
+        data = request.json
+        portfolio = data.get('portfolio', [])
+
+        if not portfolio:
+            return jsonify({'error': '请提供投资组合'}), 400
+
+        # 获取投资组合风险分析结果
+        result = risk_monitor.analyze_portfolio_risk(portfolio)
+
+        return custom_jsonify(result)
+    except Exception as e:
+        app.logger.error(f"投资组合风险分析出错: {traceback.format_exc()}")
+        return jsonify({'error': str(e)}), 500
+
+
+# 指数分析路由
+@app.route('/api/index_analysis', methods=['GET'])
+def api_index_analysis():
+    try:
+        index_code = request.args.get('index_code')
+        limit = int(request.args.get('limit', 30))
+
+        if not index_code:
+            return jsonify({'error': '请提供指数代码'}), 400
+
+        # 获取指数分析结果
+        result = index_industry_analyzer.analyze_index(index_code, limit)
+
+        return custom_jsonify(result)
+    except Exception as e:
+        app.logger.error(f"指数分析出错: {traceback.format_exc()}")
+        return jsonify({'error': str(e)}), 500
+
+
+# 行业分析路由
+@app.route('/api/industry_analysis', methods=['GET'])
+def api_industry_analysis():
+    try:
+        industry = request.args.get('industry')
+        limit = int(request.args.get('limit', 30))
+
+        if not industry:
+            return jsonify({'error': '请提供行业名称'}), 400
+
+        # 获取行业分析结果
+        result = index_industry_analyzer.analyze_industry(industry, limit)
+
+        return custom_jsonify(result)
+    except Exception as e:
+        app.logger.error(f"行业分析出错: {traceback.format_exc()}")
+        return jsonify({'error': str(e)}), 500
+
+
+@app.route('/api/industry_fund_flow', methods=['GET'])
+def api_industry_fund_flow():
+    """获取行业资金流向数据"""
+    try:
+        symbol = request.args.get('symbol', '即时')
+
+        result = industry_analyzer.get_industry_fund_flow(symbol)
+
+        return custom_jsonify(result)
+    except Exception as e:
+        app.logger.error(f"获取行业资金流向数据出错: {traceback.format_exc()}")
+        return jsonify({'error': str(e)}), 500
+
+
+@app.route('/api/industry_detail', methods=['GET'])
+def api_industry_detail():
+    """获取行业详细信息"""
+    try:
+        industry = request.args.get('industry')
+
+        if not industry:
+            return jsonify({'error': '请提供行业名称'}), 400
+
+        result = industry_analyzer.get_industry_detail(industry)
+
+        app.logger.info(f"返回前 (result)：{result}")
+        if not result:
+            return jsonify({'error': f'未找到行业 {industry} 的详细信息'}), 404
+
+        return custom_jsonify(result)
+    except Exception as e:
+        app.logger.error(f"获取行业详细信息出错: {traceback.format_exc()}")
+        return jsonify({'error': str(e)}), 500
+
+
+# 行业比较路由
+@app.route('/api/industry_compare', methods=['GET'])
+def api_industry_compare():
+    try:
+        limit = int(request.args.get('limit', 10))
+
+        # 获取行业比较结果
+        result = index_industry_analyzer.compare_industries(limit)
+
+        return custom_jsonify(result)
+    except Exception as e:
+        app.logger.error(f"行业比较出错: {traceback.format_exc()}")
+        return jsonify({'error': str(e)}), 500
+
+
+# 保存股票分析结果到数据库
+def save_analysis_result(stock_code, market_type, result):
+    """保存分析结果到数据库"""
+    if not USE_DATABASE:
+        return
+
+    try:
+        session = get_session()
+
+        # 创建新的分析结果记录
+        analysis = AnalysisResult(
+            stock_code=stock_code,
+            market_type=market_type,
+            score=result.get('scores', {}).get('total', 0),
+            recommendation=result.get('recommendation', {}).get('action', ''),
+            technical_data=result.get('technical_analysis', {}),
+            fundamental_data=result.get('fundamental_data', {}),
+            capital_flow_data=result.get('capital_flow_data', {}),
+            ai_analysis=result.get('ai_analysis', '')
+        )
+
+        session.add(analysis)
+        session.commit()
+
+    except Exception as e:
+        app.logger.error(f"保存分析结果到数据库时出错: {str(e)}")
+        if session:
+            session.rollback()
+    finally:
+        if session:
+            session.close()
+
+
+# 从数据库获取历史分析结果
+@app.route('/api/history_analysis', methods=['GET'])
+def get_history_analysis():
+    """获取股票的历史分析结果"""
+    if not USE_DATABASE:
+        return jsonify({'error': '数据库功能未启用'}), 400
+
+    stock_code = request.args.get('stock_code')
+    limit = int(request.args.get('limit', 10))
+
+    if not stock_code:
+        return jsonify({'error': '请提供股票代码'}), 400
+
+    try:
+        session = get_session()
+
+        # 查询历史分析结果
+        results = session.query(AnalysisResult) \
+            .filter(AnalysisResult.stock_code == stock_code) \
+            .order_by(AnalysisResult.analysis_date.desc()) \
+            .limit(limit) \
+            .all()
+
+        # 转换为字典列表
+        history = [result.to_dict() for result in results]
+
+        return jsonify({'history': history})
+
+    except Exception as e:
+        app.logger.error(f"获取历史分析结果时出错: {str(e)}")
+        return jsonify({'error': str(e)}), 500
+    finally:
+        if session:
+            session.close()
+
+
+# 在应用启动时启动清理线程（保持原有代码不变）
+cleaner_thread = threading.Thread(target=run_task_cleaner)
+cleaner_thread.daemon = True
+cleaner_thread.start()
+
+if __name__ == '__main__':
+    # 将 host 设置为 '0.0.0.0' 使其支持所有网络接口访问
+    app.run(host='0.0.0.0', port=8888, debug=False)