Update backtest_engine.py

backtest_engine.py

@@ -1,5 +1,5 @@
 # ============================================================
-# 🧪 backtest_engine.py (V105.0 - GEM-Architect: ULTIMATE SPEED)
+# 🧪 backtest_engine.py (V106.0 - GEM-Architect: Crash Fix & Optimization)
 # ============================================================
 
 import asyncio
@@ -21,7 +21,7 @@ try:
     from learning_hub.adaptive_hub import StrategyDNA, AdaptiveHub
     from r2 import R2Service
     import ccxt.async_support as ccxt 
-    import xgboost as xgb # Required for Direct Injection
+    import xgboost as xgb 
 except ImportError:
     pass
 
@@ -53,7 +53,7 @@ class HeavyDutyBacktester:
         self.force_end_date = None   
         
         if not os.path.exists(CACHE_DIR): os.makedirs(CACHE_DIR)
-        print(f"🧪 [Backtest V105.0] Ultimate Speed (Hydra + Legacy V2/V3 Injection).")
+        print(f"🧪 [Backtest V106.0] Ultimate Speed (Hydra + Legacy V2/V3 Injection) - Crash Fixed.")
 
     def set_date_range(self, start_str, end_str):
         self.force_start_date = start_str
@@ -121,7 +121,7 @@ class HeavyDutyBacktester:
         df['rsi'] = 100 - (100 / (1 + rs))
         df['ema20'] = df['close'].ewm(span=20, adjust=False).mean()
         df['ema50'] = df['close'].ewm(span=50, adjust=False).mean()
-        df['ema200'] = df['close'].ewm(span=200, adjust=False).mean() # For V3
+        df['ema200'] = df['close'].ewm(span=200, adjust=False).mean() 
         
         # 3. ATR
         high_low = df['high'] - df['low']
@@ -218,8 +218,10 @@ class HeavyDutyBacktester:
         numpy_htf = {} 
         for tf_str, tf_code in [('5m', '5T'), ('15m', '15T'), ('1h', '1h'), ('4h', '4h'), ('1d', '1D')]:
             resampled = df_1m.resample(tf_code).agg(agg_dict).dropna()
-            if tf_str in ['5m', '15m', '1h']:
-                resampled = self._calculate_indicators_vectorized(resampled)
+            
+            # ✅ FIX: ALWAYS calculate indicators, even for 4h/1d, to prevent KeyError 'rsi'
+            resampled = self._calculate_indicators_vectorized(resampled)
+            
             resampled['timestamp'] = resampled.index.astype(np.int64) // 10**6
             frames[tf_str] = resampled