app.py

import requests
from bs4 import BeautifulSoup
import pandas as pd
from io import StringIO
from datetime import datetime, timedelta

from langchain.tools import tool
from langchain.agents import initialize_agent, Tool
from langchain_openai import ChatOpenAI

# === Configuration ===
OPENROUTER_API_KEY = "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"

HEADERS = {
    'User-Agent': (
        'Mozilla/5.0 (Windows NT 10.0; Win64; x64) '
        'AppleWebKit/537.36 (KHTML, like Gecko) '
        'Chrome/124.0.0.0 Safari/537.36'
    ),
    'Accept': (
        'text/html,application/xhtml+xml,application/xml;'
        'q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8'
    ),
    'Accept-Language': 'en-US,en;q=0.9',
    'Referer': 'https://www.google.com/',
    'Connection': 'keep-alive',
    'Cache-Control': 'max-age=0',
    'Upgrade-Insecure-Requests': '1'
}

@tool("GetCurrentPrice")
def get_current_price(symbol: str) -> str:
    """Get the current price for a stock symbol (e.g., 'AAPL')."""
    url = f'https://www.marketwatch.com/investing/stock/{symbol}'
    response = requests.get(url, headers=HEADERS)
    if response.status_code == 200:
        soup = BeautifulSoup(response.text, 'html.parser')
        price_tag = soup.find('bg-quote', class_='value')
        if price_tag:
            price = price_tag.get_text(strip=True)
            return f"{symbol.upper()} current price: ${price} (from MarketWatch)"
        else:
            return "Stock price not found."
    else:
        return f"Failed to retrieve stock page. Status code: {response.status_code}"

@tool("GetHistoricalData")
def get_historical_data(symbol: str, days: int = 5000) -> str:
    """Get the last N days of historical OHLCV data for a stock symbol as CSV."""
    end_date = datetime.now()
    start_date = end_date - timedelta(days=days)
    start_date_str = start_date.strftime('%m/%d/%Y 00:00:00')
    end_date_str = end_date.strftime('%m/%d/%Y 23:59:59')
    csv_url = (
        f'https://www.marketwatch.com/investing/stock/{symbol}/downloaddatapartial'
        f'?csvdownload=true&downloadpartial=false'
        f'&startdate={start_date_str}&enddate={end_date_str}'
        f'&frequency=p1d&newdates=false'
    )
    response = requests.get(csv_url, headers=HEADERS)
    if response.status_code == 200:
        try:
            df = pd.read_csv(StringIO(response.text))
            return f"Historical data for {symbol.upper()} (from MarketWatch):\n" + df.head(10).to_csv(index=False)
        except Exception as e:
            return f"Failed to parse CSV data: {e}"
    else:
        return f"Failed to download historical data. Status code: {response.status_code}"

@tool("GetTechnicalAnalysisDocs")
def get_technical_analysis_docs(_: str = "") -> str:
    """Get documentation for technical analysis indicators."""
    return """
Technical Analysis: Core Concepts & Formulas
-------------------------------------------
- Market Action Discounts Everything: All known information is reflected in price.
- Prices Move in Trends: Uptrend, downtrend, or sideways movement.
- History Repeats Itself: Psychological patterns repeat.

Key Technical Indicators and Their Formulas:
-------------------------------------------
1. Simple Moving Average (SMA): SMA(time_period) = Sum(Price_t ... Price_{t-n}) / n
2. Exponential Moving Average (EMA): EMA_t = (Price_t * α) + EMA_{t-1} * (1 - α), where α = 2/(n+1)
3. Relative Strength Index (RSI): RSI = 100 - [100 / (1 + Avg Gain / Avg Loss)]
4. MACD (Moving Average Convergence Divergence): MACD Line = 12-period EMA - 26-period EMA; Signal Line = 9-period EMA of MACD Line; Histogram = MACD Line - Signal Line
5. Stochastic Oscillator (STOCH): Fast K = (Current Close - Lowest Low) / (Highest High - Lowest Low) * 100 over N periods; Slow K = 3-day SMA of Fast K; Slow D = 3-day SMA of Slow K
6. Momentum (MOM): MOM = Current Close - Close_N_days_ago
7. Rate of Change (ROC): ROC = [(Current Close - Prior Close) / Prior Close] * 100
8. Volume Weighted Average Price (VWAP): VWAP = Sum(Price * Volume) / Sum(Volume) over intraday period
9. Bollinger Bands: Middle Band = 20-day SMA; Upper Band = 20-day SMA + 2 * 20-day Standard Deviation; Lower Band = 20-day SMA - 2 * 20-day Standard Deviation
10. Ichimoku Cloud: Tenkan-sen = (9-period high + low)/2; Kijun-sen = (26-period high + low)/2; Senkou Span A = (Tenkan-sen + Kijun-sen)/2 shifted forward by 26; Senkou Span B = (52-period high + low)/2 shifted forward by 26; Chikou Span = Current close shifted back by 26 days

11. **Williams %R**:
    %R = (Highest High - Close) / (Highest High - Lowest Low) * -100 over N periods

12. **Commodity Channel Index (CCI)**:
    CCI = (Typical Price - 20-day SMA of TP) / (0.015 * Mean Deviation)  
    where Typical Price = (High + Low + Close) / 3

13. **Average Directional Index (ADX)**:
    ADX = Smoothed average of DX values, which measure directional strength

14. **On-Balance Volume (OBV)**:
    OBV = previous OBV + volume if close > previous close, else -volume

15. **Moving Average Convergence Divergence (MACD)**:
    MACD Line = 12-day EMA - 26-day EMA  
    Signal Line = 9-day EMA of MACD Line  
    MACD Histogram = MACD Line - Signal Line

16. **Absolute Price Oscillator (APO)**:
    APO = Fast EMA - Slow EMA

17. **Balance of Power (BOP)**:
    BOP = (Close - Open) / (High - Low)

18. **Triple Exponential Moving Average (TEMA)**:
    TEMA = (3 * EMA1) - (3 * EMA2) + EMA3  
    where EMA1 = fast EMA, EMA2 = slower EMA, etc.

19. **Double Exponential Moving Average (DEMA)**:
    DEMA = 2*EMA1 - EMA2

20. **Kaufman Adaptive Moving Average (KAMA)**:
    KAMA = prior KAMA + SC * (price - prior KAMA)  
    where SC = smoothing constant based on efficiency ratio

21. **Chaikin Money Flow (CMF)**:
    MF Multiplier = [(Close - Low) - (High - Close)] / (High - Low)  
    MF Volume = MF Multiplier * Volume  
    CMF = Sum(MF Volume) / Sum(Volume) over N days

22. **Aroon Indicator**:
    Aroon Up = ((N - Periods Since Highest Close) / N) * 100  
    Aroon Down = ((N - Periods Since Lowest Close) / N) * 100

23. **Parabolic SAR**:
    SAR_t = SAR_{t-1} + AF * (EP - SAR_{t-1})

24. **Standard Deviation (Volatility)**:
    σ = sqrt[1/N * Σ(Close_i - μ)^2]

25. **Candlestick Patterns**:
   - Hammer, Shooting Star, Engulfing, Doji, Morning/Evening Star, etc.
"""

tools = [
    Tool.from_function(
        get_current_price,
        name="GetCurrentPrice",
        description="Get the current price for a stock symbol (e.g., AAPL)"
    ),
    Tool.from_function(
        get_historical_data,
        name="GetHistoricalData",
        description="Get the last N days of historical OHLCV data for a stock symbol as CSV"
    ),
    Tool.from_function(
        get_technical_analysis_docs,
        name="GetTechnicalAnalysisDocs",
        description="Provide documentation for technical analysis indicators"
    )
]

llm = ChatOpenAI(
    openai_api_key=OPENROUTER_API_KEY,
    openai_api_base="https://openrouter.ai/api/v1",
    model_name="google/gemma-3n-e4b-it:free"
)

agent = initialize_agent(
    tools,
    llm,
    agent="zero-shot-react-description",
    verbose=True  # This will show all reasoning and tool usage!
)

if __name__ == "__main__":
    print("Welcome to the Stock Analysis Agent!")
    print("Ask anything about stocks, technical analysis, or predictions.")
    print("Type 'exit' to quit.")
    while True:
        user_input = input("\nYour question: ")
        if user_input.lower() in ["exit", "quit"]:
            print("Goodbye!")
            break
        result = agent.run(user_input)
        print("\nAgent's answer:\n", result)