Update app.py

app.py

@@ -14,7 +14,7 @@ def verify_api_key():
 def fetch_stock_data(symbol):
     try:
         end_date = datetime.now()
-        start_date = end_date - timedelta(days=10)  # Changed to 10 days
+        start_date = end_date - timedelta(days=10)
         
         # Handle different market indexes
         index_mapping = {
@@ -24,15 +24,34 @@ def fetch_stock_data(symbol):
             'FINNIFTY': 'NIFTY_FIN_SERVICE.NS'
         }
         
+        # NSE option chain URLs
+        nse_option_urls = {
+            'NIFTY50': 'https://www.nseindia.com/api/option-chain-indices?symbol=NIFTY',
+            'BANKNIFTY': 'https://www.nseindia.com/api/option-chain-indices?symbol=BANKNIFTY',
+            'FINNIFTY': 'https://www.nseindia.com/api/option-chain-indices?symbol=FINNIFTY'
+        }
+        
+        # Historical options data URLs
+        historical_options_urls = {
+            'NIFTY50': 'https://www.nseindia.com/api/historical/fo/derivatives?',
+            'BANKNIFTY': 'https://www.nseindia.com/api/historical/fo/derivatives?',
+            'FINNIFTY': 'https://www.nseindia.com/api/historical/fo/derivatives?'
+        }
+        
         if symbol.upper() in index_mapping:
             symbol = index_mapping[symbol.upper()]
         elif not symbol.endswith('.NS') and not symbol.startswith('^'):
             symbol = f"{symbol}.NS"
         
+        # Fetch price data
         url = f"https://query1.finance.yahoo.com/v8/finance/chart/{symbol}?period1={int(start_date.timestamp())}&period2={int(end_date.timestamp())}&interval=1d"
         
         headers = {
-            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
+            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36',
+            'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',
+            'Accept-Language': 'en-US,en;q=0.5',
+            'Accept-Encoding': 'gzip, deflate, br',
+            'Connection': 'keep-alive',
         }
         
         response = requests.get(url, headers=headers)
@@ -51,9 +70,67 @@ def fetch_stock_data(symbol):
         numeric_columns = ['Open', 'High', 'Low', 'Close']
         df[numeric_columns] = df[numeric_columns].round(2)
         df = df.reset_index()
-        df.index = df.index + 1  # Start index from 1
+        df.index = df.index + 1
         df = df.drop('index', axis=1)
         
+        # Try to fetch option chain and historical data
+        options_data = {}
+        orig_symbol = next((k for k, v in index_mapping.items() if v == symbol), None)
+        if orig_symbol in nse_option_urls:
+            try:
+                # First get the cookie
+                session = requests.Session()
+                session.get("https://www.nseindia.com/get-quotes/derivatives?symbol=" + orig_symbol, headers=headers)
+                cookies = session.cookies.get_dict()
+                
+                # Then fetch option chain data
+                option_headers = headers.copy()
+                option_headers['Cookie'] = '; '.join([f"{k}={v}" for k, v in cookies.items()])
+                
+                option_response = session.get(nse_option_urls[orig_symbol], headers=option_headers)
+                if option_response.status_code == 200:
+                    option_data = option_response.json()
+                    current_price = df.iloc[-1]['Close']
+                    atm_strike = round(current_price / 50) * 50  # Round to nearest 50 for NIFTY
+                    if orig_symbol == 'BANKNIFTY':
+                        atm_strike = round(current_price / 100) * 100  # Round to nearest 100 for BANKNIFTY
+                    
+                    # Get strikes near current price
+                    strikes = [strike for strike in option_data['records']['data'] 
+                             if abs(strike['strikePrice'] - current_price) <= 1000]
+                    
+                    # Store strike prices and their details
+                    for strike in strikes:
+                        strike_price = strike['strikePrice']
+                        if 'CE' in strike and 'PE' in strike:
+                            options_data[strike_price] = {
+                                'CE': {
+                                    'OI': strike['CE'].get('openInterest', 0),
+                                    'Volume': strike['CE'].get('totalTradedVolume', 0),
+                                    'LTP': strike['CE'].get('lastPrice', 0)
+                                },
+                                'PE': {
+                                    'OI': strike['PE'].get('openInterest', 0),
+                                    'Volume': strike['PE'].get('totalTradedVolume', 0),
+                                    'LTP': strike['PE'].get('lastPrice', 0)
+                                }
+                            }
+                    
+                    # Sort strikes by total OI (CE + PE) to find strikes with highest interest
+                    sorted_strikes = sorted(options_data.items(), 
+                                         key=lambda x: (x[1]['CE']['OI'] + x[1]['PE']['OI']), 
+                                         reverse=True)
+                    
+                    # Store top 5 strikes by OI
+                    top_strikes = sorted_strikes[:5]
+                    
+                    df.attrs['options_data'] = options_data
+                    df.attrs['top_strikes'] = top_strikes
+                    df.attrs['atm_strike'] = atm_strike
+                    
+            except Exception as e:
+                print(f"Error fetching option data: {str(e)}")
+        
         return df
     except Exception as e:
         print(f"Error fetching data: {str(e)}")
@@ -66,16 +143,37 @@ def get_swing_trade_analysis(df, symbol):
             return "Error: Mistral API key not found. Please add it to the environment variables."
 
         # Get last 10 days of data
-        last_10_days = df.tail(10)  # Changed to 10 days
+        last_10_days = df.tail(10)
+        
+        # Get options data if available
+        options_info = ""
+        if hasattr(df, 'attrs') and 'options_data' in df.attrs:
+            atm_strike = df.attrs['atm_strike']
+            top_strikes = df.attrs['top_strikes']
+            
+            options_info = "\n\nOptions Data:\n"
+            options_info += f"ATM Strike: {atm_strike}\n\n"
+            options_info += "Top 5 Strikes by Open Interest:\n"
+            
+            for strike_price, data in top_strikes:
+                ce_data = data['CE']
+                pe_data = data['PE']
+                options_info += f"\nStrike {strike_price}:\n"
+                options_info += f"Call - OI: {ce_data['OI']}, Volume: {ce_data['Volume']}, LTP: {ce_data['LTP']}\n"
+                options_info += f"Put  - OI: {pe_data['OI']}, Volume: {pe_data['Volume']}, LTP: {pe_data['LTP']}\n"
         
         # Format data for prompt
         data_str = f"""This is the last 10 days data for {symbol} index:
 Open    High    Low     Close
 """
-        for _, row in last_10_days.iterrows():  # Using last_10_days
+        for _, row in last_10_days.iterrows():
             data_str += f"{row['Open']}\t{row['High']}\t{row['Low']}\t{row['Close']}\n"
         
-        data_str += "\nso i need you to give me the setup for today by priceaction levels"
+        data_str += options_info
+        data_str += "\nBased on the index price action and options data, please provide:"
+        data_str += "\n1. Index price action analysis with key levels"
+        data_str += "\n2. Potential option trades with specific strikes"
+        data_str += "\n3. Entry, target, and stop loss for both index and suggested option strikes"
 
         url = "https://api.mistral.ai/v1/chat/completions"
         
@@ -87,24 +185,29 @@ Open    High    Low     Close
         data = {
             "model": "mistral-small",
             "messages": [
-                {"role": "system", "content": """You are a pure price action trader. Focus ONLY on price levels and patterns, NO technical indicators. When analyzing market data:
+                {"role": "system", "content": """You are a professional options trader specializing in index options and price action analysis. When analyzing market data:
+
+1. First analyze index price action:
+   - Key support and resistance levels
+   - Current market position
+   - Potential price targets and stop loss levels
 
-1. Key Price Levels:
-   - Strong Resistance levels (with recent highs)
-   - Strong Support levels (with recent lows)
-   - Current market position relative to these levels
+2. Then analyze options data:
+   - Focus on strikes with highest open interest
+   - Compare Call vs Put open interest
+   - Look for unusual volume patterns
 
-2. Trading Setups (based ONLY on price action):
-   - Entry levels (specific price points)
-   - Stop loss levels (based on recent swing highs/lows)
-   - Target levels (based on price structure)
+3. Provide specific trade setups:
+   - Index levels: Entry, Target, Stop Loss
+   - Option trades: Specific strikes to trade
+   - For each option trade suggested:
+     * Strike selection rationale
+     * Entry price range
+     * Target price
+     * Stop loss price
+     * Risk-reward ratio
 
-3. Keep it simple and focus on:
-   - Recent swing highs and lows
-   - Key price levels where market reversed
-   - Areas of price rejection
-   
-Do not mention any technical indicators, moving averages, or oscillators. Pure price action only."""},
+Keep analysis focused on price action and option chain data. No technical indicators."""},
                 {"role": "user", "content": data_str}
             ],
             "temperature": 0.7,