Update app.py

app.py

@@ -3,7 +3,9 @@ import datetime
 import requests
 import pytz
 import yaml
+import pandas as pd
 import os
+import json
 from tools.final_answer import FinalAnswerTool
 from Gradio_UI import GradioUI
 
@@ -11,52 +13,123 @@ api_key = os.getenv("ALPHAVANTAGE")
 if not api_key:
     raise ValueError("\u26a0\ufe0f Key is missing! Add it as a Secret in Hugging Face Spaces.")
 
-@tool
-def get_dcf_of_company(company_name: str) -> str:
-    """A tool that retrieves the DCF of a company based on its name.
+
+def calculate_dcf_valuation(
+    symbol: str,
+    api_key: str,
+    growth_rate: float = 0.15,
+    terminal_growth: float = 0.03,
+    discount_rate: float = 0.10,
+    years_to_project: int = 5
+) -> Dict[str, Union[float, str, List[float]]]:
+    """
+    Calculates the DCF valuation for a given stock symbol using Alpha Vantage data.
+    
     Args:
-        company_name: The name of the company.
+        symbol: Stock symbol (e.g., 'AAPL')
+        api_key: Alpha Vantage API key
+        growth_rate: Expected annual growth rate (default: 15%)
+        terminal_growth: Terminal growth rate (default: 3%)
+        discount_rate: Discount rate (default: 10%)
+        years_to_project: Number of years to project (default: 5)
+    
+    Returns:
+        Dictionary containing:
+        - intrinsic_value: Calculated intrinsic value per share
+        - current_price: Current market price
+        - recommendation: Buy/Sell/Hold recommendation
+        - projected_cash_flows: List of projected cash flows
+        - error: Error message if any
     """
     try:
-        # Step 1: Retrieve the stock ticker symbol
-        search_url = f'https://www.alphavantage.co/query?function=SYMBOL_SEARCH&keywords={company_name}&apikey={api_key}'
-        response = requests.get(search_url)
-        data = response.json()
+        # Get cash flow data
+        cash_flow_url = f"https://www.alphavantage.co/query?function=CASH_FLOW&symbol={symbol}&apikey={api_key}"
+        response = requests.get(cash_flow_url)
+        cash_flow_data = response.json()
         
-        if 'bestMatches' in data and len(data['bestMatches']) > 0:
-            symbol = data['bestMatches'][0]['1. symbol']
-        else:
-            return f"No stock symbol found for company '{company_name}'."
-            
-        # Step 2: Retrieve the company's financial data
-        overview_url = f'https://www.alphavantage.co/query?function=OVERVIEW&symbol={symbol}&apikey={api_key}'
+        if "annualReports" not in cash_flow_data:
+            return {"error": "Unable to fetch cash flow data"}
+        
+        # Get operating cash flow from most recent year
+        latest_cash_flow = float(cash_flow_data["annualReports"][0]["operatingCashflow"])
+        
+        # Get shares outstanding
+        overview_url = f"https://www.alphavantage.co/query?function=OVERVIEW&symbol={symbol}&apikey={api_key}"
         response = requests.get(overview_url)
-        overview = response.json()
+        overview_data = response.json()
+        shares_outstanding = float(overview_data.get("SharesOutstanding", 0))
+        
+        # Project future cash flows
+        projected_cash_flows = []
+        current_cash_flow = latest_cash_flow
         
-        if not overview:
-            return f"No financial data found for company with symbol '{symbol}'."
-            
-        # Extract necessary data
-        free_cash_flow = float(overview.get('FreeCashFlow', 0))
-        wacc = float(overview.get('WeightedAverageCostOfCapital', 0)) / 100
-        growth_rate = float(overview.get('GrowthRate', 0)) / 100
+        for year in range(years_to_project):
+            current_cash_flow *= (1 + growth_rate)
+            projected_cash_flows.append(current_cash_flow)
         
-        if free_cash_flow == 0 or wacc == 0:
-            return f"Insufficient data to calculate DCF for company '{company_name}'."
-            
-        # Step 3: Calculate DCF
-        dcf_value = 0
-        years = 5  # Number of years for forecasting
+        # Calculate terminal value
+        terminal_value = (current_cash_flow * (1 + terminal_growth)) / (discount_rate - terminal_growth)
+        projected_cash_flows.append(terminal_value)
         
-        for year in range(1, years + 1):
-            future_cash_flow = free_cash_flow * ((1 + growth_rate) ** year)
-            discounted_cash_flow = future_cash_flow / ((1 + wacc) ** year)
-            dcf_value += discounted_cash_flow
-            
-        return f"The Discounted Cash Flow (DCF) for company '{company_name}' is: ${dcf_value:,.2f}"
+        # Calculate present value of all cash flows
+        present_value = 0
+        for i, cash_flow in enumerate(projected_cash_flows):
+            present_value += cash_flow / ((1 + discount_rate) ** (i + 1))
         
+        # Calculate per share value
+        intrinsic_value = present_value / shares_outstanding
+        
+        # Get current price
+        price_url = f"https://www.alphavantage.co/query?function=GLOBAL_QUOTE&symbol={symbol}&apikey={api_key}"
+        response = requests.get(price_url)
+        price_data = response.json()
+        current_price = float(price_data["Global Quote"]["05. price"])
+        
+        # Generate recommendation
+        margin_of_safety = 0.2  # 20% margin of safety
+        if intrinsic_value * (1 - margin_of_safety) > current_price:
+            recommendation = "Buy"
+        elif intrinsic_value * (1 + margin_of_safety) < current_price:
+            recommendation = "Sell"
+        else:
+            recommendation = "Hold"
+        
+        return {
+            "intrinsic_value": round(intrinsic_value, 2),
+            "current_price": round(current_price, 2),
+            "recommendation": recommendation,
+            "projected_cash_flows": [round(cf, 2) for cf in projected_cash_flows],
+            "error": None
+        }
+    
     except Exception as e:
-        return f"Error calculating DCF for company '{company_name}': {str(e)}"
+        return {"error": f"Error calculating DCF: {str(e)}"}
+
+# Example of how to format it as an AI agent tool
+@tool
+def get_stock_dcf_valuation(symbol: str, api_key: str) -> str:
+    """
+    A tool that calculates the DCF valuation for a given stock symbol.
+    
+    Args:
+        symbol: Stock symbol (e.g., 'AAPL')
+        api_key: Alpha Vantage API key
+    
+    Returns:
+        A string containing the DCF analysis results or error message
+    """
+    result = calculate_dcf_valuation(symbol, api_key)
+    
+    if result.get("error"):
+        return result["error"]
+    
+    return (
+        f"DCF Analysis for {symbol}:\n"
+        f"Intrinsic Value: ${result['intrinsic_value']}\n"
+        f"Current Price: ${result['current_price']}\n"
+        f"Recommendation: {result['recommendation']}\n"
+        f"Projected Cash Flows: {result['projected_cash_flows']}"
+    )
 
 final_answer = FinalAnswerTool()
 
@@ -77,7 +150,7 @@ agent = CodeAgent(
     model=model,
     tools=[
         final_answer,
-        get_dcf_of_company  # Added the DCF tool here
+        get_stock_dcf_valuation  # Added the DCF tool here
     ],
     max_steps=6,
     verbosity_level=1,