Update backtest_engine.py

backtest_engine.py

@@ -285,68 +285,42 @@ class HeavyDutyBacktester:
         l = df["low"].astype(np.float64)
         v = df["volume"].astype(np.float64) if "volume" in df.columns else pd.Series(np.zeros(len(df)), index=df.index)
 
-        # ✅ [GEM-FIX] Calculate Slope for Oracle (prevents missing feature warning)
-        try:
-            df['slope'] = ta.slope(c, length=7).fillna(0)
-        except:
-            df['slope'] = 0.0
-
-        # ✅ [GEM-FIX] Calculate MFI for Titan (prevents missing feature warning)
-        try:
-            df['MFI'] = ta.mfi(h, l, c, v, length=14).fillna(50)
-        except:
-            df['MFI'] = 50.0
-
-        # EMAs
-        for span in [9, 20, 21, 50, 200]:
-            df[f"ema{span}"] = c.ewm(span=span, adjust=False).mean()
-
-        # BBANDS
-        if len(df) < 30:
-            df["lower_bb"] = c
-            df["upper_bb"] = c
-            df["bb_width"] = 0.0
-            df["bb_pct"] = 0.5
-        else:
-            bb = ta.bbands(c, length=20, std=2.0)
-            if bb is None or (isinstance(bb, pd.DataFrame) and bb.shape[1] < 3):
-                lower, upper, width, pct = self._safe_bbands(c, 20, 2.0)
-                df["lower_bb"], df["upper_bb"], df["bb_width"], df["bb_pct"] = lower, upper, width, pct
-            else:
-                if isinstance(bb, pd.DataFrame):
-                    col_lower = [x for x in bb.columns if "BBL" in x]
-                    col_upper = [x for x in bb.columns if "BBU" in x]
-                    col_width = [x for x in bb.columns if "BBB" in x]
-                    col_pct = [x for x in bb.columns if "BBP" in x]
-                    if col_lower and col_upper:
-                        df["lower_bb"] = bb[col_lower[0]]
-                        df["upper_bb"] = bb[col_upper[0]]
-                    else:
-                        lower, upper, width, pct = self._safe_bbands(c, 20, 2.0)
-                        df["lower_bb"], df["upper_bb"] = lower, upper
-                    df["bb_width"] = bb[col_width[0]] if col_width else (df["upper_bb"] - df["lower_bb"]) / (c.abs() + 1e-12)
-                    df["bb_pct"] = bb[col_pct[0]] if col_pct else (c - df["lower_bb"]) / ((df["upper_bb"] - df["lower_bb"]) + 1e-12)
-                else:
-                    lower, upper, width, pct = self._safe_bbands(c, 20, 2.0)
-                    df["lower_bb"], df["upper_bb"], df["bb_width"], df["bb_pct"] = lower, upper, width, pct
+        # ----------------------------------------------------
+        # ✅ [GEM-FIX] جسر التوافق مع Oracle & Titan
+        # ----------------------------------------------------
+        
+        # 1. Oracle: Slope
+        try: df['slope'] = ta.slope(c, length=7).fillna(0)
+        except: df['slope'] = 0.0
 
-        # MACD
-        macd = ta.macd(c)
-        if macd is not None and isinstance(macd, pd.DataFrame) and macd.shape[1] >= 3:
-            df["MACD"] = macd.iloc[:, 0]
-            df["MACD_h"] = macd.iloc[:, 1]
-            df["MACD_s"] = macd.iloc[:, 2]
-        else:
-            df["MACD"] = 0.0
-            df["MACD_h"] = 0.0
-            df["MACD_s"] = 0.0
+        # 2. Titan: MFI
+        try: df['MFI'] = ta.mfi(h, l, c, v, length=14).fillna(50)
+        except: df['MFI'] = 50.0
 
-        # Core
+        # 3. Oracle Mapping (LowerCase Aliases)
+        # يحسب المكتبة RSI، ونحن ننسخها إلى rsi لأن النموذج يطلبها صغيرة
         df["RSI"] = ta.rsi(c, length=14).fillna(50)
+        df["rsi"] = df["RSI"]  # Alias for Oracle
+
         df["ATR"] = ta.atr(h, l, c, length=14).fillna(0)
+        df["ATR_pct"] = (df["ATR"] / (c + 1e-12)) * 100
+        df["atr_pct"] = df["ATR_pct"] # Alias for Oracle
+
+        # 4. Oracle: Volume Z-Score (vol_z)
+        vol_mean = v.rolling(20).mean()
+        vol_std = v.rolling(20).std()
+        df["vol_z"] = ((v - vol_mean) / (vol_std + 1e-9)).fillna(0) # For Oracle
 
+        # 5. Titan: Trend Strong (Approx for lower TFs, perfect for 1d)
         adx_df = ta.adx(h, l, c, length=14)
-        df["ADX"] = adx_df.iloc[:, 0].fillna(0) if (adx_df is not None and isinstance(adx_df, pd.DataFrame) and adx_df.shape[1] >= 1) else 0.0
+        if adx_df is not None and not adx_df.empty:
+            df["ADX"] = adx_df.iloc[:, 0].fillna(0)
+            df["Trend_Strong"] = np.where(df["ADX"] > 25, 1, 0) # For Titan 1d
+        else:
+            df["ADX"] = 0.0
+            df["Trend_Strong"] = 0
+
+        # ----------------------------------------------------
 
         try:
             df["CHOP"] = ta.chop(h, l, c, length=14).fillna(50)
@@ -358,19 +332,53 @@ class HeavyDutyBacktester:
         except:
             df["vwap"] = c
 
-        # ✅ FIX: Add CCI (Titan expects features like 5m_CCI)
         try:
             df["CCI"] = ta.cci(h, l, c, length=20).fillna(0)
         except:
             df["CCI"] = 0.0
 
-        # Derived
+        # EMAs
+        for span in [9, 20, 21, 50, 200]:
+            df[f"ema{span}"] = c.ewm(span=span, adjust=False).mean()
+
+        # Derived Features
         df["EMA_9_dist"] = (c / (df["ema9"] + 1e-12)) - 1
         df["EMA_21_dist"] = (c / (df["ema21"] + 1e-12)) - 1
         df["EMA_50_dist"] = (c / (df["ema50"] + 1e-12)) - 1
         df["EMA_200_dist"] = (c / (df["ema200"] + 1e-12)) - 1
         df["VWAP_dist"] = (c / (df["vwap"] + 1e-12)) - 1
-        df["ATR_pct"] = (df["ATR"] / (c + 1e-12)) * 100
+        
+        # BBANDS
+        if len(df) < 30:
+            df["lower_bb"] = c; df["upper_bb"] = c; df["bb_width"] = 0.0; df["bb_pct"] = 0.5
+        else:
+            bb = ta.bbands(c, length=20, std=2.0)
+            if bb is not None and isinstance(bb, pd.DataFrame):
+                col_w = [x for x in bb.columns if "BBB" in x]
+                col_p = [x for x in bb.columns if "BBP" in x]
+                col_l = [x for x in bb.columns if "BBL" in x]
+                col_u = [x for x in bb.columns if "BBU" in x]
+                
+                df["bb_width"] = bb[col_w[0]] if col_w else 0.0
+                df["bb_pct"] = bb[col_p[0]] if col_p else 0.5
+                df["lower_bb"] = bb[col_l[0]] if col_l else c
+                df["upper_bb"] = bb[col_u[0]] if col_u else c
+                
+                # Aliases for Titan
+                df["BB_w"] = df["bb_width"]
+                df["BB_p"] = df["bb_pct"]
+            else:
+                 df["lower_bb"] = c; df["upper_bb"] = c; df["bb_width"] = 0.0; df["bb_pct"] = 0.5
+                 df["BB_w"] = 0.0; df["BB_p"] = 0.5
+
+        # MACD
+        macd = ta.macd(c)
+        if macd is not None and not macd.empty:
+            df["MACD"] = macd.iloc[:, 0]
+            df["MACD_h"] = macd.iloc[:, 1]
+            df["MACD_s"] = macd.iloc[:, 2]
+        else:
+            df["MACD"] = 0.0; df["MACD_h"] = 0.0; df["MACD_s"] = 0.0
 
         mean_vol = v.rolling(50).mean() + 1e-9
         df["rel_vol"] = v / mean_vol