Update app.py

app.py

@@ -4,19 +4,72 @@ import requests
 import pytz
 import yaml
 from tools.final_answer import FinalAnswerTool
+import pandas as pd
 
 from Gradio_UI import GradioUI
 
 # Below is an example of a tool that does nothing. Amaze us with your creativity !
 @tool
-def my_custom_tool(arg1:str, arg2:int)-> str: #it's import to specify the return type
-    #Keep this format for the description / args / args description but feel free to modify the tool
-    """A tool that does nothing yet 
+def get_stock_signal(symbol: str, interval: str) -> str:
+    """
+    Retrieves intraday stock data for the given symbol using the Alpha Vantage API, computes exponential moving averages (EMA),
+    and generates a trading signal based on an EMA crossover strategy.
     Args:
-        arg1: the first argument
-        arg2: the second argument
+        symbol (str): Stock symbol to analyze (e.g., "AAPL", "GOOG", "MSFT", "TSLA").
+        interval (str): Time interval between data points (e.g., "1min", "5min", "15min", "60min").
     """
-    return "What magic will you build ?"
+    API_KEY = 'RG9XKRIYBL2EV3V3'
+    
+    url = (
+        f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY'
+        f'&symbol={symbol}&interval={interval}&outputsize=full&apikey={API_KEY}'
+    )
+    
+    response = requests.get(url)
+    data = response.json()
+    
+    time_series_key = f"Time Series ({interval})"
+    time_series = data.get(time_series_key, {})
+    if not time_series:
+        raise ValueError("Failed to retrieve data. Check your API key, symbol, and interval.")
+    
+    # Create a DataFrame from the time series data
+    df = pd.DataFrame.from_dict(time_series, orient='index')
+    df = df.rename(columns={
+        '1. open': 'open',
+        '2. high': 'high',
+        '3. low': 'low',
+        '4. close': 'close',
+        '5. volume': 'volume'
+    })
+    df.index = pd.to_datetime(df.index)
+    df = df.sort_index()
+    df['close'] = pd.to_numeric(df['close'])
+    
+    # Ensure there is enough data to calculate the EMAs
+    if len(df) < 26:
+        return f"Insufficient data to calculate the required EMAs for {symbol} at a {interval} interval."
+    
+    # Improved Strategy: Using Exponential Moving Averages (EMA) for a more responsive indicator.
+    # Short-term EMA (e.g., 12 periods) and Long-term EMA (e.g., 26 periods)
+    df['EMA_short'] = df['close'].ewm(span=12, adjust=False).mean()
+    df['EMA_long'] = df['close'].ewm(span=26, adjust=False).mean()
+    
+    # Get the latest two data points to check for a crossover
+    latest = df.iloc[-1]
+    prev = df.iloc[-2]
+    
+    # Determine buy/sell signal based on EMA crossover
+    signal = "Hold"
+    if prev['EMA_short'] < prev['EMA_long'] and latest['EMA_short'] > latest['EMA_long']:
+        signal = "Buy"
+    elif prev['EMA_short'] > prev['EMA_long'] and latest['EMA_short'] < latest['EMA_long']:
+        signal = "Sell"
+    
+    # Return a complete sentence with the decision
+    decision = (f"The latest closing price for {symbol.upper()} at a {interval} interval is "
+                f"${latest['close']:.2f}, and the recommended action is to {signal}.")
+    return decision
 
 @tool
 def get_current_time_in_timezone(timezone: str) -> str:
@@ -57,7 +110,7 @@ with open("prompts.yaml", 'r') as stream:
     
 agent = CodeAgent(
     model=model,
-    tools=[final_answer, image_generation_tool, get_current_time_in_timezone, search_tool], ## add your tools here (don't remove final answer)
+    tools=[final_answer, get_stock_signal, image_generation_tool, get_current_time_in_timezone, search_tool], ## add your tools here (don't remove final answer)
     max_steps=6,
     verbosity_level=1,
     grammar=None,