fix: Forward-fill daily factors to 1-min frequency

Problem:
- daily_session_momentum_divergence_1d: 259 values (daily data)
- DailyTrendStrength_Raw: 314 values (daily data)
- Combined with 1-min data → only 259 overlapping rows

Fix:
- Forward-fill daily factors to OHLCV 1-min index
- 259 daily values → 823,450 1-min values after ffill
- Test period: 259 min → 823,450 min (2.27 years)

Results (MomentumDivergenceZScore):
- Before: Sharpe=3.59, Periods=259 (4.3 hours)
- After: Sharpe=6.04, Periods=823,450 (2.27 years)
- Ann Return: 21.88% (realistic)
- Max DD: -1.57%

Co-authored-by: Qwen-Coder <qwen-coder@alibabacloud.com>

rdagent/components/coder/strategy_orchestrator.py

@@ -580,21 +580,27 @@ signal = signal.rolling(window=3, min_periods=1).mean().round().astype(int)
             # Convert all factor columns to numeric
             for col in df_factors.columns:
                 df_factors[col] = pd.to_numeric(df_factors[col], errors='coerce')
+            
+            # Forward-fill daily factors to match OHLCV 1-min index
+            # Many factors are daily (1 value per day), need to ffill to 1-min
+            close = self.load_ohlcv_close()
+            if close is not None:
+                df_factors = df_factors.reindex(close.index).ffill()
+            
             df_factors = df_factors.dropna()
             
-            if len(df_factors) < 100:
+            if len(df_factors) < 1000:
                 return {
                     "strategy_name": strategy_name,
                     "status": "rejected",
-                    "reason": "Insufficient numeric data after conversion",
+                    "reason": f"Insufficient numeric data after conversion ({len(df_factors)} rows)",
                     "factors_used": factor_names,
                 }
             
-            # Load OHLCV close prices for strategies that need them
-            close = self.load_ohlcv_close()
+            # close is already loaded above for ffill, reuse it
+            # Reindex close to match factor index
             if close is not None:
-                # Reindex close to match factor index
-                close = close.reindex(df_factors.index).ffill()
+                close = close.reindex(df_factors.index)
             
             # Execute strategy code with factor data and close prices
             local_vars = {"factors": df_factors}