Update services/analyzer.py

services/analyzer.py

@@ -1,174 +1,174 @@
-# services/analyzer.py
-import numpy as np
-import pandas as pd
-from typing import Dict, Any, Optional
-from services.economic_data import EconomicDataFetcher
-from models.analysis import StockAnalysisResponse, InvestmentRecommendationResponse
-from models.market_data import StockDataResponse
-
-class IndianFinancialAnalyzer:
-    """Analyze Indian market financial data"""
-
-    def __init__(self):
-        self.economic_data_fetcher = EconomicDataFetcher()
-        self.rbi_repo_rate = self.economic_data_fetcher.get_rbi_repo_rate()
-        self.indian_inflation_rate = self.economic_data_fetcher.get_indian_inflation_rate()
-
-    def analyze_indian_stock(self, stock_data: StockDataResponse, company_name: str) -> StockAnalysisResponse:
-        """Comprehensive analysis of Indian stock"""
-        # Convert history dict back to DataFrame for analysis (basic reconstruction)
-        # This assumes the dict keys are column names and values are lists of values
-        try:
-            hist_df = pd.DataFrame(stock_data.history)
-            if 'Date' in hist_df.columns:
-                 hist_df['Date'] = pd.to_datetime(hist_df['Date'])
-                 hist_df.set_index('Date', inplace=True)
-        except Exception as e:
-             print(f"Error reconstructing DataFrame for analysis: {e}")
-             hist_df = pd.DataFrame() # Return empty DataFrame on error
-
-        if hist_df.empty:
-            analysis_text = "No data available for analysis"
-        else:
-            # Current metrics
-            current_price = hist_df['Close'].iloc[-1]
-            year_high = hist_df['High'].max()
-            year_low = hist_df['Low'].min()
-
-            # Calculate returns
-            returns_1m = ((current_price - hist_df['Close'].iloc[-21]) / hist_df['Close'].iloc[-21]) * 100 if len(hist_df) >= 21 else 0
-            returns_3m = ((current_price - hist_df['Close'].iloc[-63]) / hist_df['Close'].iloc[-63]) * 100 if len(hist_df) >= 63 else 0
-            returns_1y = ((current_price - hist_df['Close'].iloc[0]) / hist_df['Close'].iloc[0]) * 100
-
-            # Volatility (standard deviation of daily returns)
-            daily_returns = hist_df['Close'].pct_change().dropna()
-            volatility = daily_returns.std() * np.sqrt(252) * 100  # Annualized volatility
-
-            # Technical indicators
-            sma_20 = hist_df['Close'].rolling(20).mean().iloc[-1] if len(hist_df) >= 20 else np.nan
-            sma_50 = hist_df['Close'].rolling(50).mean().iloc[-1] if len(hist_df) >= 50 else np.nan
-            ema_20 = hist_df['Close'].ewm(span=20, adjust=False).mean().iloc[-1] if len(hist_df) >= 20 else np.nan
-            ema_50 = hist_df['Close'].ewm(span=50, adjust=False).mean().iloc[-1] if len(hist_df) >= 50 else np.nan
-            rsi = hist_df['Close'].diff().ewm(span=14, adjust=False).mean() / hist_df['Close'].diff().ewm(span=14, adjust=False).std()
-            macd = hist_df['Close'].ewm(span=12, adjust=False).mean() - hist_df['Close'].ewm(span=26, adjust=False).mean()
-            macd_signal = macd.ewm(span=9, adjust=False).mean()
-            macd_hist = macd - macd_signal
-            macd_cross = 'Bullish' if macd.iloc[-1] > macd_signal.iloc[-1] else 'Bearish'
-            stochastic_k = ((hist_df['Close'].iloc[-1] - hist_df['Low'].rolling(14).min().iloc[-1]) /
-                            (hist_df['High'].rolling(14).max().iloc[-1] - hist_df['Low'].rolling(14).min().iloc[-1]) * 100) if len(hist_df) >= 14 else np.nan
-            stochastic_d = pd.Series(stochastic_k).rolling(3).mean().iloc[-1] if not pd.isna(stochastic_k) else np.nan
-            stochastic_cross = 'Bullish' if stochastic_k > stochastic_d else 'Bearish'
-            atr = hist_df['High'].combine(hist_df['Low'], max) - hist_df['Low'].combine(hist_df['Close'].shift(), min)
-            atr = atr.rolling(14).mean().iloc[-1] if len(hist_df) >= 14 else np.nan
-            obv = (np.sign(hist_df['Close'].diff()) * hist_df['Volume']).fillna(0).cumsum().iloc[-1]
-            mfi = 100 - (100 / (1 + (hist_df['Close'].diff().fillna(0) * hist_df['Volume']).rolling(14).sum() /
-                                (-hist_df['Close'].diff().fillna(0) * hist_df['Volume']).rolling(14).sum())).iloc[-1] if len(hist_df) >= 14 else np.nan
-            adx = pd.Series(np.abs(hist_df['High'].diff()) - np.abs(hist_df['Low'].diff())).rolling(14).mean().iloc[-1] if len(hist_df) >= 14 else np.nan
-            
-
-            # Determine trend
-            if pd.notna(current_price) and pd.notna(sma_20) and pd.notna(sma_50):
-                if current_price > sma_20 > sma_50:
-                    trend = 'Bullish'
-                elif current_price < sma_20 < sma_50:
-                    trend = 'Bearish'
-                else:
-                    trend = 'Neutral'
-            else:
-                trend = 'Insufficient Data'
-
-            # Indian market specific analysis
-            analysis = f"""
-## 🇮🇳 Indian Market Analysis for {company_name}
-
-### Current Market Position
-- **Current Price**: ₹{current_price:.2f}
-- **52-Week High**: ₹{year_high:.2f}
-- **52-Week Low**: ₹{year_low:.2f}
-- **Distance from High**: {((current_price - year_high) / year_high * 100):.1f}%
-
-### Returns Performance
-- **1 Month Return**: {returns_1m:.2f}%
-- **3 Month Return**: {returns_3m:.2f}%
-- **1 Year Return**: {returns_1y:.2f}%
-- **Annualized Volatility**: {volatility:.2f}%
-
-### Technical Analysis
-- **20-Day SMA**: ₹{sma_20:.2f}
-- **50-Day SMA**: ₹{sma_50:.2f}
-- **20-Day EMA**: ₹{ema_20:.2f}
-- **50-Day EMA**: ₹{ema_50:.2f}
-- **RSI (14-day)**: {rsi.iloc[-1]:.2f}'
-- **MACD**: {macd.iloc[-1]:.2f} ({macd_cross})
-- **MACD HIST**: {macd_hist.iloc[-1]:.2f}
-- **Stochastic %K**: {stochastic_k:.2f} ({stochastic_cross})
-- **ATR (14-day)**: ₹{atr:.2f}'
-- **OBV**: {obv:.2f}
-- **MFI (14-day)**: {mfi:.2f}'
-- **ADX (14-day)**: {adx:.2f}'
-- **Trend**: {trend}
-
-### Indian Market Context
-- **Relative to RBI Repo Rate ({self.rbi_repo_rate}%)**: {'Attractive' if returns_1y > self.rbi_repo_rate else 'Underperforming'}
-- **Inflation Adjusted Return**: {returns_1y - self.indian_inflation_rate:.2f}%
-
-### Key Company Metrics
-            """
-
-            # Add company-specific info if available
-            info = stock_data.info
-            if info:
-                market_cap = info.get('marketCap', 'N/A')
-                pe_ratio = info.get('forwardPE', info.get('trailingPE', 'N/A'))
-                dividend_yield = info.get('dividendYield', 0) * 100 if info.get('dividendYield') else 'N/A'
-
-                market_cap_str = f"₹{market_cap/10000000:.0f} Cr." if isinstance(market_cap, (int, float)) else market_cap
-                pe_str = f"{pe_ratio:.2f}" if isinstance(pe_ratio, (int, float)) else 'N/A'
-                div_yield_str = f"{dividend_yield:.2f}%" if isinstance(dividend_yield, (int, float)) else 'N/A'
-
-                analysis += f"""
-- **Market Cap**: {market_cap_str}
-- **P/E Ratio**: {pe_str}
-- **Dividend Yield**: {div_yield_str}
-                """
-            analysis_text = analysis
-
-        return StockAnalysisResponse(basic_analysis=analysis_text)
-
-    def generate_investment_recommendation(self) -> InvestmentRecommendationResponse:
-        """Generate investment recommendation based on Indian market conditions"""
-
-        recommendation = f"""
-## 📊 Investment Recommendation (Indian Market Context)
-
-### Risk Assessment
-- **Market Risk**: Indian equity markets are subject to high volatility
-- **Currency Risk**: INR fluctuations affect returns for foreign investors
-- **Regulatory Risk**: SEBI regulations and policy changes impact
-
-### Recommendation Framework
-Based on current Indian market conditions:
-
-1. **Conservative Investors**: Consider Large Cap stocks with dividend yield
-2. **Moderate Risk**: Mid Cap stocks with strong fundamentals
-3. **Aggressive**: Small Cap and sector-specific opportunities
-
-### Indian Market Specific Factors
-- **Monsoon Impact**: Agricultural and rural demand dependency
-- **Festival Season**: Seasonal consumption patterns
-- **Government Policy**: Budget announcements and reforms
-- **FII/DII Flows**: Foreign and domestic institutional investor sentiment
-
-### Economic Context
-- **RBI Repo Rate**: {self.rbi_repo_rate}%
-- **Inflation Rate**: {self.indian_inflation_rate}%
-
-### Tax Implications (Indian Investors)
-- **Short Term Capital Gains**: 15% (< 1 year)
-- **Long Term Capital Gains**: 10% on gains > ₹1 Lakh (> 1 year)
-- **Dividend Tax**: TDS as per income tax slab
-
-**Disclaimer**: This is for educational purposes only. Please consult a SEBI registered investment advisor.
-        """
-
-        return InvestmentRecommendationResponse(recommendation=recommendation)
+# services/analyzer.py
+import numpy as np
+import pandas as pd
+from typing import Dict, Any, Optional
+from services.economic_data import EconomicDataFetcher
+from models.analysis import StockAnalysisResponse, InvestmentRecommendationResponse
+from models.market_data import StockDataResponse
+
+class IndianFinancialAnalyzer:
+    """Analyze Indian market financial data"""
+
+    def __init__(self):
+        self.economic_data_fetcher = EconomicDataFetcher()
+        self.rbi_repo_rate = self.economic_data_fetcher.get_rbi_repo_rate()
+        self.indian_inflation_rate = self.economic_data_fetcher.get_indian_inflation_rate()
+
+    def analyze_indian_stock(self, stock_data: StockDataResponse, company_name: str) -> StockAnalysisResponse:
+        """Comprehensive analysis of Indian stock"""
+        # Convert history dict back to DataFrame for analysis (basic reconstruction)
+        # This assumes the dict keys are column names and values are lists of values
+        try:
+            hist_df = pd.DataFrame(stock_data.history)
+            if 'Date' in hist_df.columns:
+                 hist_df['Date'] = pd.to_datetime(hist_df['Date'])
+                 hist_df.set_index('Date', inplace=True)
+        except Exception as e:
+             print(f"Error reconstructing DataFrame for analysis: {e}")
+             hist_df = pd.DataFrame() # Return empty DataFrame on error
+
+        if hist_df.empty:
+            analysis_text = "No data available for analysis"
+        else:
+            # Current metrics
+            current_price = hist_df['Close'].iloc[-1]
+            year_high = hist_df['High'].max()
+            year_low = hist_df['Low'].min()
+
+            # Calculate returns
+            returns_1m = ((current_price - hist_df['Close'].iloc[-21]) / hist_df['Close'].iloc[-21]) * 100 if len(hist_df) >= 21 else 0
+            returns_3m = ((current_price - hist_df['Close'].iloc[-63]) / hist_df['Close'].iloc[-63]) * 100 if len(hist_df) >= 63 else 0
+            returns_1y = ((current_price - hist_df['Close'].iloc[0]) / hist_df['Close'].iloc[0]) * 100
+
+            # Volatility (standard deviation of daily returns)
+            daily_returns = hist_df['Close'].pct_change().dropna()
+            volatility = daily_returns.std() * np.sqrt(252) * 100  # Annualized volatility
+
+            # Technical indicators
+            sma_20 = hist_df['Close'].rolling(20).mean().iloc[-1] if len(hist_df) >= 20 else np.nan
+            sma_50 = hist_df['Close'].rolling(50).mean().iloc[-1] if len(hist_df) >= 50 else np.nan
+            ema_20 = hist_df['Close'].ewm(span=20, adjust=False).mean().iloc[-1] if len(hist_df) >= 20 else np.nan
+            ema_50 = hist_df['Close'].ewm(span=50, adjust=False).mean().iloc[-1] if len(hist_df) >= 50 else np.nan
+            rsi = hist_df['Close'].diff().ewm(span=14, adjust=False).mean() / hist_df['Close'].diff().ewm(span=14, adjust=False).std()
+            macd = hist_df['Close'].ewm(span=12, adjust=False).mean() - hist_df['Close'].ewm(span=26, adjust=False).mean()
+            macd_signal = macd.ewm(span=9, adjust=False).mean()
+            macd_hist = macd - macd_signal
+            macd_cross = 'Bullish' if macd.iloc[-1] > macd_signal.iloc[-1] else 'Bearish'
+            stochastic_k = ((hist_df['Close'].iloc[-1] - hist_df['Low'].rolling(14).min().iloc[-1]) /
+                            (hist_df['High'].rolling(14).max().iloc[-1] - hist_df['Low'].rolling(14).min().iloc[-1]) * 100) if len(hist_df) >= 14 else np.nan
+            stochastic_d = pd.Series(stochastic_k).rolling(3).mean().iloc[-1] if not pd.isna(stochastic_k) else np.nan
+            stochastic_cross = 'Bullish' if stochastic_k > stochastic_d else 'Bearish'
+            atr = hist_df['High'].combine(hist_df['Low'], max) - hist_df['Low'].combine(hist_df['Close'].shift(), min)
+            atr = atr.rolling(14).mean().iloc[-1] if len(hist_df) >= 14 else np.nan
+            obv = (np.sign(hist_df['Close'].diff()) * hist_df['Volume']).fillna(0).cumsum().iloc[-1]
+            mfi = 100 - (100 / (1 + (hist_df['Close'].diff().fillna(0) * hist_df['Volume']).rolling(14).sum() /
+                                (-hist_df['Close'].diff().fillna(0) * hist_df['Volume']).rolling(14).sum())).iloc[-1] if len(hist_df) >= 14 else np.nan
+            adx = pd.Series(np.abs(hist_df['High'].diff()) - np.abs(hist_df['Low'].diff())).rolling(14).mean().iloc[-1] if len(hist_df) >= 14 else np.nan
+            
+
+            # Determine trend
+            if pd.notna(current_price) and pd.notna(sma_20) and pd.notna(sma_50):
+                if current_price > sma_20 > sma_50:
+                    trend = 'Bullish'
+                elif current_price < sma_20 < sma_50:
+                    trend = 'Bearish'
+                else:
+                    trend = 'Neutral'
+            else:
+                trend = 'Insufficient Data'
+
+            # Indian market specific analysis
+            analysis = f"""
+## Indian Market Analysis for {company_name}
+
+### Current Market Position
+- **Current Price**: ₹{current_price:.2f}
+- **52-Week High**: ₹{year_high:.2f}
+- **52-Week Low**: ₹{year_low:.2f}
+- **Distance from High**: {((current_price - year_high) / year_high * 100):.1f}%
+
+### Returns Performance
+- **1 Month Return**: {returns_1m:.2f}%
+- **3 Month Return**: {returns_3m:.2f}%
+- **1 Year Return**: {returns_1y:.2f}%
+- **Annualized Volatility**: {volatility:.2f}%
+
+### Technical Analysis
+- **20-Day SMA**: ₹{sma_20:.2f}
+- **50-Day SMA**: ₹{sma_50:.2f}
+- **20-Day EMA**: ₹{ema_20:.2f}
+- **50-Day EMA**: ₹{ema_50:.2f}
+- **RSI (14-day)**: {rsi.iloc[-1]:.2f}'
+- **MACD**: {macd.iloc[-1]:.2f} ({macd_cross})
+- **MACD HIST**: {macd_hist.iloc[-1]:.2f}
+- **Stochastic %K**: {stochastic_k:.2f} ({stochastic_cross})
+- **ATR (14-day)**: ₹{atr:.2f}'
+- **OBV**: {obv:.2f}
+- **MFI (14-day)**: {mfi:.2f}'
+- **ADX (14-day)**: {adx:.2f}'
+- **Trend**: {trend}
+
+### Indian Market Context
+- **Relative to RBI Repo Rate ({self.rbi_repo_rate}%)**: {'Attractive' if returns_1y > self.rbi_repo_rate else 'Underperforming'}
+- **Inflation Adjusted Return**: {returns_1y - self.indian_inflation_rate:.2f}%
+
+### Key Company Metrics
+            """
+
+            # Add company-specific info if available
+            info = stock_data.info
+            if info:
+                market_cap = info.get('marketCap', 'N/A')
+                pe_ratio = info.get('forwardPE', info.get('trailingPE', 'N/A'))
+                dividend_yield = info.get('dividendYield', 0) * 100 if info.get('dividendYield') else 'N/A'
+
+                market_cap_str = f"₹{market_cap/10000000:.0f} Cr." if isinstance(market_cap, (int, float)) else market_cap
+                pe_str = f"{pe_ratio:.2f}" if isinstance(pe_ratio, (int, float)) else 'N/A'
+                div_yield_str = f"{dividend_yield:.2f}%" if isinstance(dividend_yield, (int, float)) else 'N/A'
+
+                analysis += f"""
+- **Market Cap**: {market_cap_str}
+- **P/E Ratio**: {pe_str}
+- **Dividend Yield**: {div_yield_str}
+                """
+            analysis_text = analysis
+
+        return StockAnalysisResponse(basic_analysis=analysis_text)
+
+    def generate_investment_recommendation(self) -> InvestmentRecommendationResponse:
+        """Generate investment recommendation based on Indian market conditions"""
+
+        recommendation = f"""
+## 📊 Investment Recommendation (Indian Market Context)
+
+### Risk Assessment
+- **Market Risk**: Indian equity markets are subject to high volatility
+- **Currency Risk**: INR fluctuations affect returns for foreign investors
+- **Regulatory Risk**: SEBI regulations and policy changes impact
+
+### Recommendation Framework
+Based on current Indian market conditions:
+
+1. **Conservative Investors**: Consider Large Cap stocks with dividend yield
+2. **Moderate Risk**: Mid Cap stocks with strong fundamentals
+3. **Aggressive**: Small Cap and sector-specific opportunities
+
+### Indian Market Specific Factors
+- **Monsoon Impact**: Agricultural and rural demand dependency
+- **Festival Season**: Seasonal consumption patterns
+- **Government Policy**: Budget announcements and reforms
+- **FII/DII Flows**: Foreign and domestic institutional investor sentiment
+
+### Economic Context
+- **RBI Repo Rate**: {self.rbi_repo_rate}%
+- **Inflation Rate**: {self.indian_inflation_rate}%
+
+### Tax Implications (Indian Investors)
+- **Short Term Capital Gains**: 15% (< 1 year)
+- **Long Term Capital Gains**: 10% on gains > ₹1 Lakh (> 1 year)
+- **Dividend Tax**: TDS as per income tax slab
+
+**Disclaimer**: This is for educational purposes only. Please consult a SEBI registered investment advisor.
+        """
+
+        return InvestmentRecommendationResponse(recommendation=recommendation)