Update backtest_engine.py

backtest_engine.py

@@ -1,5 +1,5 @@
 # ============================================================
-# 🧪 backtest_engine.py (V135.0 - GEM-Architect: Feature Parity + Full Diagnostics)
+# 🧪 backtest_engine.py (V135.1 - GEM-Architect: EMA20 Fix)
 # ============================================================
 
 import asyncio
@@ -46,7 +46,6 @@ def _z_roll(x, w=500):
     return ((x - r) / s).fillna(0)
 
 def _revive_score_distribution(scores):
-    """Normalize flattened scores to 0-1 range if they are compressed"""
     scores = np.array(scores, dtype=np.float32)
     if len(scores) < 10: return scores
     std = np.std(scores)
@@ -84,7 +83,7 @@ class HeavyDutyBacktester:
         self.force_end_date = None   
         
         if not os.path.exists(CACHE_DIR): os.makedirs(CACHE_DIR)
-        print(f"🧪 [Backtest V135.0] Feature Parity + Full Diagnostics + Speed.")
+        print(f"🧪 [Backtest V135.1] Feature Parity + Full Diagnostics + Speed.")
 
     def set_date_range(self, start_str, end_str):
         self.force_start_date = start_str
@@ -155,11 +154,15 @@ class HeavyDutyBacktester:
         if adx is not None: df['ADX'] = adx.iloc[:, 0].fillna(0)
         else: df['ADX'] = 0.0
 
+        # Titan uses 9, 21, 50, 200
         for p in [9, 21, 50, 200]:
             ema = ta.ema(df['close'], length=p)
             df[f'EMA_{p}_dist'] = ((df['close'] / ema) - 1).fillna(0)
             df[f'ema{p}'] = ema 
 
+        # ✅ [GEM-FIX] Explicitly calculate EMA20 for Legacy models
+        df['ema20'] = ta.ema(df['close'], length=20).fillna(df['close'])
+
         bb = ta.bbands(df['close'], length=20, std=2.0)
         if bb is not None:
             df['BB_w'] = ((bb.iloc[:, 2] - bb.iloc[:, 0]) / bb.iloc[:, 1]).fillna(0)
@@ -228,7 +231,6 @@ class HeavyDutyBacktester:
             rv_gk = ((np.log(df['high'] / df['low'])**2) / 2) - ((2 * np.log(2) - 1) * (np.log(df['close'] / df['open'])**2))
             df['rv_gk'] = _z_roll(rv_gk)
             
-            # L_Score approximation
             df['L_score'] = (df['vol_zscore_50'] - df['amihud'] - df['roll_spread'] - df['rv_gk'].abs() - df['vwap_dev'].abs() + df['ofi']).fillna(0)
 
         # ---------------------------------------------------------
@@ -246,6 +248,8 @@ class HeavyDutyBacktester:
         roll_min = df['low'].rolling(50).min()
         diff = (roll_max - roll_min).replace(0, 1e-9)
         df['fib_pos'] = ((df['close'] - roll_min) / diff).fillna(0.5)
+        
+        # ✅ Now 'ema20' exists!
         df['trend_slope'] = ((df['ema20'] - df['ema20'].shift(5)) / df['ema20'].shift(5)).fillna(0)
         df['volatility'] = (df['atr'] / df['close']).fillna(0)
         
@@ -481,6 +485,7 @@ class HeavyDutyBacktester:
                 l2_arr = np.full(240, 0.7)
                 tgt_arr = np.full(240, 3.0)
                 
+                # [rsi1, rsi5, rsi15, bb, vol, dist_ema, atr_p, norm, max, dists, time, entry, oracle, l2, target]
                 X_H = np.column_stack([
                     sl_st[:,0], sl_st[:,1], sl_st[:,2], sl_st[:,3], sl_st[:,4],
                     zeros, atr_pct, norm_pnl, max_pnl_r,