Update app.py

app.py

@@ -32,6 +32,86 @@ def get_current_time_in_timezone(timezone: str) -> str:
         return f"The current local time in {timezone} is: {local_time}"
     except Exception as e:
         return f"Error fetching time for timezone '{timezone}': {str(e)}"
+@tool
+def analyze_stock(ticker: str) -> dict:  # type: ignore[type-arg]
+    """
+    A tool that analyze stock data.
+    Args:
+        ticker: A string representing ticker
+    """
+    import os
+    from datetime import datetime, timedelta
+
+    import matplotlib.pyplot as plt
+    import numpy as np
+    import pandas as pd
+    import yfinance as yf
+    from pytz import timezone  # type: ignore
+
+    stock = yf.Ticker(ticker)
+
+    # Get historical data (1 year of data to ensure we have enough for 200-day MA)
+    end_date = datetime.now(timezone("UTC"))
+    start_date = end_date - timedelta(days=365)
+    hist = stock.history(start=start_date, end=end_date)
+
+    # Ensure we have data
+    if hist.empty:
+        return {"error": "No historical data available for the specified ticker."}
+
+    # Compute basic statistics and additional metrics
+    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())
+
+    # Calculate 50-day and 200-day moving averages
+    ma_50 = hist["Close"].rolling(window=50).mean().iloc[-1]
+    ma_200 = hist["Close"].rolling(window=200).mean().iloc[-1]
+
+    # Calculate YTD price change and percent change
+    ytd_start = datetime(end_date.year, 1, 1, tzinfo=timezone("UTC"))
+    ytd_data = hist.loc[ytd_start:]  # type: ignore[misc]
+    if not ytd_data.empty:
+        price_change = ytd_data["Close"].iloc[-1] - ytd_data["Close"].iloc[0]
+        percent_change = (price_change / ytd_data["Close"].iloc[0]) * 100
+    else:
+        price_change = percent_change = np.nan
+
+    # Determine trend
+    if pd.notna(ma_50) and pd.notna(ma_200):
+        if ma_50 > ma_200:
+            trend = "Upward"
+        elif ma_50 < ma_200:
+            trend = "Downward"
+        else:
+            trend = "Neutral"
+    else:
+        trend = "Insufficient data for trend analysis"
+
+    # Calculate volatility (standard deviation of daily returns)
+    daily_returns = hist["Close"].pct_change().dropna()
+    volatility = daily_returns.std() * np.sqrt(252)  # Annualized volatility
+
+    # Create result dictionary
+    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,
+    }
+
+    # Convert numpy types to Python native types for better JSON serialization
+    for key, value in result.items():
+        if isinstance(value, np.generic):
+            result[key] = value.item()
+
+    return result
 
 
 final_answer = FinalAnswerTool()