[rebuild] Deploy complete pattern analysis implementation

indicator_chart_generator.py

@@ -1,22 +0,0 @@
-from indicator_analyzer import IndicatorAnalyzer
-from lightweight_charts import Chart
-
-class AutoIndicatorGenerator:
-    def __init__(self):
-        self.analyzer = IndicatorAnalyzer()
-        
-    def generate_indicator_charts(self, ohlcv_data):
-        indicators = self.analyzer.analyze_indicators(ohlcv_data)
-        
-        charts = []
-        for indicator in indicators:
-            chart = Chart()
-            chart.candlestick(ohlcv_data)
-            chart.add_indicator(
-                type=indicator['type'],
-                values=indicator['values'],
-                parameters=indicator['parameters']
-            )
-            charts.append(chart)
-            
-        return charts

test_charts.py

@@ -1,34 +0,0 @@
-import numpy as np
-import pandas as pd
-from auto_chart_generator import AutoChartGenerator
-from indicator_chart_generator import AutoIndicatorGenerator
-
-# Generate 150 days of OHLCV data
-np.random.seed(42)
-days = 150
-base_price = 100
-
-price_changes = np.random.normal(0.001, 0.02, days).cumsum()
-prices = base_price * (1 + price_changes)
-
-test_data = {
-    'open': prices * (1 + np.random.normal(0, 0.005, days)),
-    'high': prices * (1 + np.random.normal(0.01, 0.008, days)),
-    'low': prices * (1 + np.random.normal(-0.01, 0.008, days)),
-    'close': prices * (1 + np.random.normal(0, 0.005, days)),
-    'volume': np.random.normal(1000000, 200000, days)
-}
-
-df = pd.DataFrame(test_data)
-df['high'] = df[['open', 'high', 'close']].max(axis=1)
-df['low'] = df[['open', 'low', 'close']].min(axis=1)
-
-# Test pattern charts
-pattern_gen = AutoChartGenerator()
-pattern_charts = pattern_gen.generate_pattern_charts(df)
-print("Generated Pattern Charts:", len(pattern_charts))
-
-# Test indicator charts
-indicator_gen = AutoIndicatorGenerator()
-indicator_charts = indicator_gen.generate_indicator_charts(df)
-print("Generated Indicator Charts:", len(indicator_charts))

test_data.py

@@ -1,23 +0,0 @@
-import numpy as np
-import pandas as pd
-
-# Generate 150 days test data
-days = 150
-base_price = 100
-
-# Generate price changes with realistic volatility
-price_changes = np.random.normal(0.001, 0.02, days).cumsum()
-base_prices = base_price * (1 + price_changes)
-
-# Create DataFrame with all OHLCV components
-test_data = pd.DataFrame({
-    'open': base_prices * (1 + np.random.normal(0, 0.005, days)),  # Opening prices
-    'high': base_prices * (1 + np.random.normal(0.01, 0.008, days)),  # Day's high prices
-    'low': base_prices * (1 + np.random.normal(-0.01, 0.008, days)),  # Day's low prices
-    'close': base_prices * (1 + np.random.normal(0, 0.005, days)),  # Closing prices
-    'volume': np.random.normal(1000000, 200000, days).astype(int)  # Daily volume
-})
-
-# Ensure high is always highest and low is always lowest
-test_data['high'] = test_data[['open', 'high', 'close']].max(axis=1)
-test_data['low'] = test_data[['open', 'low', 'close']].min(axis=1)

test_imports.py

@@ -1,29 +0,0 @@
-try:
-    import gradio as gr
-    print("✅ Gradio imported successfully")
-except:
-    print("❌ Gradio import failed")
-
-try:
-    from transformers import AutoModelForCausalLM, AutoTokenizer
-    print("✅ Transformers imported successfully")
-except:
-    print("❌ Transformers import failed")
-
-try:
-    from lightweight_charts import Chart
-    print("✅ Lightweight Charts imported successfully")
-except:
-    print("❌ Lightweight Charts import failed")
-
-try:
-    from fastapi import FastAPI
-    print("✅ FastAPI imported successfully")
-except:
-    print("❌ FastAPI import failed")
-
-try:
-    from datasets import Dataset, load_dataset
-    print("✅ Datasets imported successfully")
-except:
-    print("❌ Datasets import failed")

test_indicators.py

@@ -1,28 +0,0 @@
-import numpy as np
-import pandas as pd
-from indicator_analyzer import IndicatorAnalyzer
-
-# Generate 150 days of OHLCV data
-np.random.seed(42)
-days = 150
-base_price = 100
-
-price_changes = np.random.normal(0.001, 0.02, days).cumsum()
-prices = base_price * (1 + price_changes)
-
-test_data = {
-    'open': prices * (1 + np.random.normal(0, 0.005, days)),
-    'high': prices * (1 + np.random.normal(0.01, 0.008, days)),
-    'low': prices * (1 + np.random.normal(-0.01, 0.008, days)),
-    'close': prices * (1 + np.random.normal(0, 0.005, days)),
-    'volume': np.random.normal(1000000, 200000, days)
-}
-
-df = pd.DataFrame(test_data)
-df['high'] = df[['open', 'high', 'close']].max(axis=1)
-df['low'] = df[['open', 'low', 'close']].min(axis=1)
-
-# Test indicator analysis
-analyzer = IndicatorAnalyzer()
-indicators = analyzer.analyze_indicators(df)
-print("Generated Indicators:", indicators)

test_model.py

@@ -1,34 +0,0 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import numpy as np
-import pandas as pd
-
-# Load model
-model = AutoModelForCausalLM.from_pretrained("codellama/CodeLlama-7b-hf")
-tokenizer = AutoTokenizer.from_pretrained("codellama/CodeLlama-7b-hf")
-
-# Create test data
-days = 150
-base_price = 100
-price_changes = np.random.normal(0.001, 0.02, days).cumsum()
-prices = base_price * (1 + price_changes)
-
-test_data = pd.DataFrame({
-    'open': prices * (1 + np.random.normal(0, 0.005, days)),
-    'high': prices * (1 + np.random.normal(0.01, 0.008, days)),
-    'low': prices * (1 + np.random.normal(-0.01, 0.008, days)),
-    'close': prices * (1 + np.random.normal(0, 0.005, days)),
-    'volume': np.random.normal(1000000, 200000, days)
-})
-
-# Test pattern detection
-prompt = f"""
-Analyze this OHLCV data and detect patterns:
-{test_data.head().to_string()}
-Return: Pattern type and coordinates
-"""
-
-inputs = tokenizer(prompt, return_tensors="pt")
-outputs = model.generate(**inputs, max_length=500)
-result = tokenizer.decode(outputs[0])
-
-print("Model Output:", result)

train.py

@@ -1,29 +0,0 @@
-from transformers import Trainer, TrainingArguments
-from datasets import Dataset
-
-def prepare_training_data():
-    # Training data structure
-    return {
-        'pattern_type': ['channel', 'triangle'],
-        'chart_code': ['// Channel code', '// Triangle code']
-    }
-
-def train_model():
-    # Create dataset
-    data = prepare_training_data()
-    dataset = Dataset.from_dict(data)
-    
-    training_args = TrainingArguments(
-        output_dir="./results",
-        num_train_epochs=3,
-        per_device_train_batch_size=8,
-        save_steps=500,
-    )
-    
-    trainer = Trainer(
-        model=model,
-        args=training_args,
-        train_dataset=dataset,
-    )
-    
-    trainer.train()

trigger_handler.py

@@ -1,21 +0,0 @@
-from chart_maker import ChartMaker
-from fastapi import FastAPI
-
-
-class TriggerHandler:
-    def __init__(self):
-        self.chart_maker = ChartMaker()
-    
-    async def handle_chart_trigger(self, trigger_data):
-        # Extract chart image and OHLCV data
-        chart_image = trigger_data['image']
-        ohlcv_data = trigger_data['ohlcv']
-        
-        # Generate pattern charts
-        pattern_charts = self.chart_maker.generate_all_variations(ohlcv_data)
-        
-        # Return generated charts through API
-        return {
-            'pattern_charts': pattern_charts,
-            'timestamp': trigger_data['timestamp']
-        }