feat: current dividend trend calculation implementation

ETF_Portal/services/nav_erosion_service/service.py

@@ -143,6 +143,63 @@ class NavErosionService:
         payout_ratio = min(yield_ratio / 0.20, 1.0)
         return payout_ratio
 
+    def _calculate_dividend_trend(self, etf_data: Dict) -> Tuple[float, str]:
+        """
+        Calculate dividend trend score and direction.
+        Returns a tuple of (trend_score, trend_direction)
+        trend_score: float between -1 and 1
+        trend_direction: str ('Increasing', 'Decreasing', 'Stable', 'Unknown')
+        """
+        try:
+            if not etf_data.get('dividends'):
+                logger.warning("No dividend data available for trend calculation")
+                return 0.0, "Unknown"
+
+            # Convert dividends to DataFrame
+            dividends = pd.DataFrame.from_dict(etf_data['dividends'], orient='index', columns=['Dividends'])
+            dividends.index = pd.to_datetime(dividends.index)
+            
+            # Resample to monthly and calculate rolling averages
+            monthly_divs = dividends.resample('M')['Dividends'].sum()
+            if len(monthly_divs) < 6:  # Need at least 6 months of data
+                return 0.0, "Unknown"
+                
+            # Calculate 3-month and 6-month moving averages
+            ma3 = monthly_divs.rolling(window=3).mean()
+            ma6 = monthly_divs.rolling(window=6).mean()
+            
+            # Calculate trend metrics
+            recent_ma3 = ma3.iloc[-3:].mean()
+            recent_ma6 = ma6.iloc[-6:].mean()
+            
+            # Calculate year-over-year growth
+            yearly_divs = dividends.resample('Y')['Dividends'].sum()
+            if len(yearly_divs) >= 2:
+                yoy_growth = (yearly_divs.iloc[-1] / yearly_divs.iloc[-2]) - 1
+            else:
+                yoy_growth = 0
+            
+            # Calculate trend score (-1 to 1)
+            ma_trend = (recent_ma3 / recent_ma6) - 1 if recent_ma6 > 0 else 0
+            trend_score = (ma_trend * 0.7 + yoy_growth * 0.3)  # Weighted combination
+            
+            # Normalize trend score to -1 to 1 range
+            trend_score = max(min(trend_score, 1), -1)
+            
+            # Determine trend direction
+            if abs(trend_score) < 0.05:
+                direction = "Stable"
+            elif trend_score > 0:
+                direction = "Increasing"
+            else:
+                direction = "Decreasing"
+                
+            return trend_score, direction
+            
+        except Exception as e:
+            logger.error(f"Error calculating dividend trend: {str(e)}")
+            return 0.0, "Unknown"
+
     def analyze_etf_erosion_risk(self, tickers: List[str]) -> NavErosionAnalysis:
         """Analyze erosion risk for a list of ETFs."""
         results = []
@@ -164,6 +221,9 @@ class NavErosionService:
                 yield_risk, yield_components = self._calculate_yield_risk(etf_data, etf_type)
                 structural_risk, structural_components = self._calculate_structural_risk(etf_data)
                 
+                # Calculate dividend trend
+                trend_score, trend_direction = self._calculate_dividend_trend(etf_data)
+                
                 # Calculate final risk scores
                 final_nav_risk = round(
                     nav_risk * self.NAV_RISK_WEIGHT +
@@ -186,14 +246,15 @@ class NavErosionService:
                     yield_risk_explanation=(
                         f"Dividend stability: {yield_components['stability']:.1%}, "
                         f"Growth: {yield_components['growth']:.1%}, "
-                        f"Payout ratio: {yield_components['payout']:.1%}"
+                        f"Payout ratio: {yield_components['payout']:.1%}, "
+                        f"Trend: {trend_direction}"
                     ),
                     etf_age_years=etf_data.get('age_years', 3),
                     max_drawdown=round(etf_data.get('max_drawdown', 0.0), 3),
                     volatility=round(etf_data.get('volatility', 0.0), 3),
                     sharpe_ratio=round(etf_data.get('sharpe_ratio', 0.0), 2),
                     sortino_ratio=round(etf_data.get('sortino_ratio', 0.0), 2),
-                    dividend_trend=round(etf_data.get('dividend_trend', 0.0), 3),
+                    dividend_trend=trend_score,
                     component_risks={
                         'nav': nav_components,
                         'yield': yield_components,