Update ml_engine/indicators.py

ml_engine/indicators.py

@@ -1,4 +1,4 @@
-# ml_engine/indicators.py (V10.2 - Bug FIX)
+# ml_engine/indicators.py (V10.2 - Anti-Fragile FIX)
 import pandas as pd
 import pandas_ta as ta
 import numpy as np
@@ -24,15 +24,28 @@ class AdvancedTechnicalAnalyzer:
             'cycle': ['hull_ma', 'supertrend', 'zigzag', 'fisher_transform']
         }
     
-    # 🔴 --- (V10.2 - تم إصلاح الأخطاء هنا) --- 🔴
+    # 🔴 --- START OF UPDATED FUNCTION (V10.2 - Anti-Fragile) --- 🔴
     def calculate_v9_smart_features(self, dataframe: pd.DataFrame) -> Dict[str, float]:
         """
-        (محدث V10.2) - (إصلاح أخطاء 'hurst' و 'atr_normalized_return')
+        (محدث V10.2) - (إصلاح خطأ "الميزات المفقودة" بشكل نهائي)
+        - استخدام "قالب الميزات" لضمان إرجاع جميع الميزات دائماً.
         """
         if dataframe.empty or dataframe is None or len(dataframe) < 100: 
             return {}
             
-        features = {}
+        # --- (V10.2) الخطوة 1: تعريف "قالب الميزات" بالقيم الافتراضية ---
+        # (هذه هي جميع الميزات التي تدرب عليها نموذج V9.8)
+        features = {
+            'price_to_ema_50': 0.0, 'price_to_ema_200': 0.0, 'price_to_min_100': 0.0,
+            'price_to_max_100': 0.0, 'slope_14_50': 0.0, 'adx_slope': 0.0,
+            'volume_zscore_50': 0.0, 'vwap_gap': 0.0, 'cmf_20': 0.0, 'vroc_12': 0.0,
+            'obv_slope': 0.0, 'rsi_14': 50.0, 'rsi_mean_10': 50.0, 'rsi_std_10': 0.0,
+            'mfi_14': 50.0, 'mfi_mean_10': 50.0, 'adx_14': 20.0, 'atr_percent': 0.0,
+            'vol_of_vol': 0.0, 'atr_normalized_return': 0.0, 'hurst': 0.5,
+            'ppo_hist': 0.0, 'ppo_line': 0.0
+            # (ميزات مونت كارلو ستضاف لاحقاً في data_manager)
+        }
+
         try:
             # --- جلب البيانات الأساسية (Series) ---
             close = dataframe['close']
@@ -49,84 +62,97 @@ class AdvancedTechnicalAnalyzer:
             obv_series = ta.obv(close, volume)
 
             # --- 2. ميزات "نسب السعر" (Price Ratios) ---
-            ema_50 = ta.ema(close, length=50).iloc[-1]
-            ema_200 = ta.ema(close, length=200).iloc[-1]
-            if ema_50 and ema_50 > 0: features['price_to_ema_50'] = (current_price / ema_50) - 1
-            if ema_200 and ema_200 > 0: features['price_to_ema_200'] = (current_price / ema_200) - 1
-            min_100 = low.tail(100).min(); max_100 = high.tail(100).max()
-            if min_100 and min_100 > 0: features['price_to_min_100'] = (current_price / min_100) - 1
-            if max_100 and max_100 > 0: features['price_to_max_100'] = (current_price / max_100) - 1
+            try:
+                ema_50 = ta.ema(close, length=50).iloc[-1]
+                ema_200 = ta.ema(close, length=200).iloc[-1]
+                if ema_50 and ema_50 > 0: features['price_to_ema_50'] = (current_price / ema_50) - 1
+                if ema_200 and ema_200 > 0: features['price_to_ema_200'] = (current_price / ema_200) - 1
+                min_100 = low.tail(100).min(); max_100 = high.tail(100).max()
+                if min_100 and min_100 > 0: features['price_to_min_100'] = (current_price / min_100) - 1
+                if max_100 and max_100 > 0: features['price_to_max_100'] = (current_price / max_100) - 1
+                ema_14 = ta.ema(close, length=14).iloc[-1]
+                if ema_14 and ema_50: features['slope_14_50'] = (ema_14 - ema_50) / 14
+            except Exception: pass # (فشل هذا الجزء، ستبقى القيم الافتراضية)
 
             # --- 3. ميزات "الميل" (Slope) ---
-            ema_14 = ta.ema(close, length=14).iloc[-1]
-            if ema_14 and ema_50: features['slope_14_50'] = (ema_14 - ema_50) / 14
-            if adx_data is not None and not adx_data.empty:
-                adx_series = adx_data['ADX_14']
-                if adx_series is not None and not adx_series.empty:
-                    adx_ema_5 = ta.ema(adx_series, length=5).iloc[-1]; adx_ema_15 = ta.ema(adx_series, length=15).iloc[-1]
-                    if adx_ema_5 and adx_ema_15: features['adx_slope'] = (adx_ema_5 - adx_ema_15) / 5
+            try:
+                if adx_data is not None and not adx_data.empty:
+                    adx_series = adx_data['ADX_14']
+                    if adx_series is not None and not adx_series.empty:
+                        adx_ema_5 = ta.ema(adx_series, length=5).iloc[-1]; adx_ema_15 = ta.ema(adx_series, length=15).iloc[-1]
+                        if adx_ema_5 and adx_ema_15: features['adx_slope'] = (adx_ema_5 - adx_ema_15) / 5
+            except Exception: pass # (فشل هذا الجزء، ستبقى القيم الافتراضية)
 
             # --- 4. ميزات "الحجم" (Volume) و "السيولة" ---
-            vol_ma_50 = volume.tail(50).mean(); vol_std_50 = volume.tail(50).std()
-            if vol_std_50 and vol_std_50 > 0: features['volume_zscore_50'] = (volume.iloc[-1] - vol_ma_50) / vol_std_50
-            vwap = ta.vwap(high, low, close, volume).iloc[-1]
-            if vwap and vwap > 0: features['vwap_gap'] = (current_price - vwap) / vwap
-            cmf = ta.cmf(high, low, close, volume, length=20)
-            if cmf is not None and not cmf.empty: features['cmf_20'] = cmf.iloc[-1]
-            vroc = ta.roc(volume, length=12)
-            if vroc is not None and not vroc.empty: features['vroc_12'] = vroc.iloc[-1]
-            if obv_series is not None and not obv_series.empty:
-                obv_ema_10 = ta.ema(obv_series, length=10).iloc[-1]; obv_ema_30 = ta.ema(obv_series, length=30).iloc[-1]
-                if obv_ema_10 and obv_ema_30: features['obv_slope'] = (obv_ema_10 - obv_ema_30) / 10
+            try:
+                vol_ma_50 = volume.tail(50).mean(); vol_std_50 = volume.tail(50).std()
+                if vol_std_50 and vol_std_50 > 0: features['volume_zscore_50'] = (volume.iloc[-1] - vol_ma_50) / vol_std_50
+                vwap = ta.vwap(high, low, close, volume).iloc[-1]
+                if vwap and vwap > 0: features['vwap_gap'] = (current_price - vwap) / vwap
+                cmf = ta.cmf(high, low, close, volume, length=20)
+                if cmf is not None and not cmf.empty: features['cmf_20'] = cmf.iloc[-1]
+                vroc = ta.roc(volume, length=12)
+                if vroc is not None and not vroc.empty: features['vroc_12'] = vroc.iloc[-1]
+                if obv_series is not None and not obv_series.empty:
+                    obv_ema_10 = ta.ema(obv_series, length=10).iloc[-1]; obv_ema_30 = ta.ema(obv_series, length=30).iloc[-1]
+                    if obv_ema_10 and obv_ema_30: features['obv_slope'] = (obv_ema_10 - obv_ema_30) / 10
+            except Exception: pass # (فشل هذا الجزء، ستبقى القيم الافتراضية)
 
             # --- 5. ميزات "تجميعية" (Aggregative) ---
-            if rsi_series is not None and not rsi_series.empty:
-                features['rsi_14'] = rsi_series.iloc[-1]; features['rsi_mean_10'] = rsi_series.tail(10).mean(); features['rsi_std_10'] = rsi_series.tail(10).std()
-            if mfi_series is not None and not mfi_series.empty:
-                features['mfi_14'] = mfi_series.iloc[-1]; features['mfi_mean_10'] = mfi_series.tail(10).mean()
-            if adx_data is not None and not adx_data.empty: 
-                adx_val = adx_data['ADX_14'].iloc[-1]
-                if adx_val is not None: features['adx_14'] = adx_val
+            try:
+                if rsi_series is not None and not rsi_series.empty:
+                    features['rsi_14'] = rsi_series.iloc[-1]; features['rsi_mean_10'] = rsi_series.tail(10).mean(); features['rsi_std_10'] = rsi_series.tail(10).std()
+                if mfi_series is not None and not mfi_series.empty:
+                    features['mfi_14'] = mfi_series.iloc[-1]; features['mfi_mean_10'] = mfi_series.tail(10).mean()
+                if adx_data is not None and not adx_data.empty: 
+                    adx_val = adx_data['ADX_14'].iloc[-1]
+                    if adx_val is not None: features['adx_14'] = adx_val
+            except Exception: pass # (فشل هذا الجزء، ستبقى القيم الافتراضية)
 
-            # 🔴 --- (V10.2 - إصلاح 'atr_val') --- 🔴
-            atr_val = None # (تعريف المتغير أولاً)
-            if atr_series is not None and not atr_series.empty:
-                atr_val = atr_series.iloc[-1]
-                if atr_val and current_price > 0: features['atr_percent'] = (atr_val / current_price) * 100
-                vol_of_vol_series = ta.atr(atr_series, length=10) # (Vol-of-Vol)
-                if vol_of_vol_series is not None and not vol_of_vol_series.empty: features['vol_of_vol'] = vol_of_vol_series.iloc[-1]
-            
-            last_return = close.pct_change().iloc[-1]
-            if atr_val and atr_val > 0: 
-                features['atr_normalized_return'] = last_return / atr_val
-            else:
-                features['atr_normalized_return'] = 0.0 # (قيمة افتراضية إذا فشل ATR)
+            # --- 6. ميزات "التقلب" (Volatility) ---
+            try:
+                atr_val = None
+                if atr_series is not None and not atr_series.empty:
+                    atr_val = atr_series.iloc[-1]
+                    if atr_val and current_price > 0: features['atr_percent'] = (atr_val / current_price) * 100
+                    vol_of_vol_series = ta.atr(atr_series, length=10) # (Vol-of-Vol)
+                    if vol_of_vol_series is not None and not vol_of_vol_series.empty: features['vol_of_vol'] = vol_of_vol_series.iloc[-1]
                 
-            # 🔴 --- (V10.2 - إصلاح 'hurst') --- 🔴
-            if HURST_AVAILABLE:
-                try:
+                last_return = close.pct_change().iloc[-1]
+                if atr_val and atr_val > 0: 
+                    features['atr_normalized_return'] = last_return / atr_val
+                # (لا نحتاج else، القيمة الافتراضية 0.0 موجودة)
+            except Exception: pass # (فشل هذا الجزء، ستبقى القيم الافتراضية)
+                
+            # --- 7. ميزات النظام (Regime Features) ---
+            try:
+                if HURST_AVAILABLE:
                     hurst_series = close.tail(100).to_numpy()
                     H, c, data = compute_Hc(hurst_series, kind='price', simplified=True)
                     features['hurst'] = H
-                except Exception:
-                     features['hurst'] = 0.5 # (محايد إذا فشلت المكتبة)
-            else:
-                features['hurst'] = 0.5 # (محايد إذا لم يتم تثبيت المكتبة)
+                # (لا نحتاج else، القيمة الافتراضية 0.5 موجودة)
+            except Exception: pass # (فشل هذا الجزء، ستبقى القيم الافتراضية)
 
-            ppo_data = ta.ppo(close, fast=12, slow=26, signal=9)
-            if ppo_data is not None and not ppo_data.empty:
-                features['ppo_hist'] = ppo_data['PPOh_12_26_9'].iloc[-1]
-                features['ppo_line'] = ppo_data['PPO_12_26_9'].iloc[-1]
+            try:
+                ppo_data = ta.ppo(close, fast=12, slow=26, signal=9)
+                if ppo_data is not None and not ppo_data.empty:
+                    features['ppo_hist'] = ppo_data['PPOh_12_26_9'].iloc[-1]
+                    features['ppo_line'] = ppo_data['PPO_12_26_9'].iloc[-1]
+            except Exception: pass # (فشل هذا الجزء، ستبقى القيم الافتراضية)
 
         except Exception as e:
-            # print(f"⚠️ خطأ في حساب ميزات V9.8 الذكية: {e}"); 
+            # (فشل كبير، سنعيد القالب الافتراضي)
+            # print(f"⚠️ خطأ كارثي في حساب ميزات V9.8: {e}"); 
             pass 
             
-        final_features = {};
+        # (تنظيف نهائي للتأكد من عدم وجود NaN/Inf)
         for key, value in features.items():
-            if value is not None and np.isfinite(value): final_features[key] = float(value)
-            else: final_features[key] = 0.0
-        return final_features
+            if not np.isfinite(value):
+                features[key] = 0.0 # (إعادة التعيين إلى 0.0 إذا كان الحساب NaN)
+        
+        return features
+    # 🔴 --- END OF UPDATED FUNCTION (V10.2) --- 🔴
+
 
     # -----------------------------------------------------------------
     # --- (الدوال القديمة تبقى كما هي للاستخدامات الأخرى مثل Sentry 1m) ---
@@ -285,4 +311,4 @@ class AdvancedTechnicalAnalyzer:
         except Exception as e: pass;
         return {key: value for key, value in cycle.items() if value is not None and not np.isnan(value)};
 
-print("✅ ML Module: Technical Indicators loaded (V10.2 - Bug FIX)")
+print("✅ ML Module: Technical Indicators loaded (V10.2 - Anti-Fragile FIX)")