added stock analysis tool

tools/stock_analysis_tool.py

@@ -0,0 +1,90 @@
+tool_name = "stock_analysis_tool"
+
+import os
+import asyncio
+from datetime import datetime, timedelta
+import matplotlib
+matplotlib.use("Agg")  # Use non-GUI backend
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import yfinance as yf
+from pytz import timezone  # type: ignore
+
+
+async def execute(ticker: str, save_dir: str = "coding") -> dict:  # type: ignore[type-arg]
+    """
+    Analyze stock data for a given ticker and generate metrics and a plot.
+
+    Args:
+        ticker (str): Stock ticker symbol (e.g., "AAPL").
+        save_dir (str): Directory to save the plot image.
+
+    Returns:
+        dict: Dictionary containing stock metrics and plot file path.
+    """
+    try:
+        # Fetch historical data (1 year)
+        stock = yf.Ticker(ticker)
+        end_date = datetime.now(timezone("UTC"))
+        start_date = end_date - timedelta(days=365)
+        hist = await asyncio.to_thread(stock.history, start=start_date, end=end_date)
+
+        if hist.empty:
+            return {"error": f"No historical data available for ticker {ticker}."}
+
+        # Metrics calculation
+        current_price = stock.info.get("currentPrice", hist["Close"].iloc[-1])
+        year_high = stock.info.get("fiftyTwoWeekHigh", hist["High"].max())
+        year_low = stock.info.get("fiftyTwoWeekLow", hist["Low"].min())
+        ma_50 = hist["Close"].rolling(window=50).mean().iloc[-1]
+        ma_200 = hist["Close"].rolling(window=200).mean().iloc[-1]
+        ytd_start = datetime(end_date.year, 1, 1, tzinfo=timezone("UTC"))
+        ytd_data = hist.loc[ytd_start:]
+        price_change = ytd_data["Close"].iloc[-1] - ytd_data["Close"].iloc[0] if not ytd_data.empty else np.nan
+        percent_change = (price_change / ytd_data["Close"].iloc[0]) * 100 if not ytd_data.empty else np.nan
+
+        trend = (
+            "Upward" if ma_50 > ma_200 else
+            "Downward" if ma_50 < ma_200 else
+            "Neutral"
+        ) if pd.notna(ma_50) and pd.notna(ma_200) else "Insufficient data for trend analysis"
+
+        daily_returns = hist["Close"].pct_change().dropna()
+        volatility = daily_returns.std() * np.sqrt(252)  # Annualized
+
+        # Convert numpy types for JSON serialization
+        result = {
+            "ticker": ticker,
+            "current_price": current_price,
+            "52_week_high": year_high,
+            "52_week_low": year_low,
+            "50_day_ma": ma_50,
+            "200_day_ma": ma_200,
+            "ytd_price_change": price_change,
+            "ytd_percent_change": percent_change,
+            "trend": trend,
+            "volatility": volatility.item() if isinstance(volatility, np.generic) else volatility,
+        }
+
+        # Generate and save plot
+        os.makedirs(save_dir, exist_ok=True)
+        plot_file_path = os.path.join(save_dir, f"{ticker}_stockprice.png")
+
+        plt.figure(figsize=(12, 6))
+        plt.plot(hist.index, hist["Close"], label="Close Price", color="blue")
+        plt.plot(hist.index, hist["Close"].rolling(window=50).mean(), label="50-day MA", color="orange")
+        plt.plot(hist.index, hist["Close"].rolling(window=200).mean(), label="200-day MA", color="green")
+        plt.title(f"{ticker} Stock Price Analysis (Past Year)")
+        plt.xlabel("Date")
+        plt.ylabel("Price ($)")
+        plt.legend()
+        plt.grid(True)
+        plt.savefig(plot_file_path)
+        plt.close()
+
+        result["plot_file_path"] = plot_file_path
+        return result
+
+    except Exception as e:
+        return {"error": f"An unexpected error occurred: {str(e)}"}