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| 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' | |
| } | |
| 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}" | |
| 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}" | |
| 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) | |