Update ml_engine/indicators.py

ml_engine/indicators.py

@@ -1,4 +1,4 @@
-# ml_engine/indicators.py (V10.1 - SyntaxError FIX & Super-Brain)
+# ml_engine/indicators.py (V10.2 - Bug FIX)
 import pandas as pd
 import pandas_ta as ta
 import numpy as np
@@ -8,7 +8,6 @@ try:
     from hurst import compute_Hc
     HURST_AVAILABLE = True
 except ImportError:
-    # 🔴 --- (V10.1 - تم إصلاح الخطأ النحوي هنا) --- 🔴
     print("⚠️ مكتبة 'hurst' غير موجودة. ميزة 'مفتاح النظام' ستكون معطلة.")
     print("   -> قم بتثبيتها: pip install hurst")
     HURST_AVAILABLE = False
@@ -25,11 +24,10 @@ class AdvancedTechnicalAnalyzer:
             'cycle': ['hull_ma', 'supertrend', 'zigzag', 'fisher_transform']
         }
     
-    # 🔴 --- (V10.0 - العقل الفائق) --- 🔴
+    # 🔴 --- (V10.2 - تم إصلاح الأخطاء هنا) --- 🔴
     def calculate_v9_smart_features(self, dataframe: pd.DataFrame) -> Dict[str, float]:
         """
-        (محدث V10.0) - (العقل الحسابي لنموذج V9.8)
-        حساب جميع الميزات المتقدمة (بما في ذلك Hurst, CMF, PPO, VROC)
+        (محدث V10.2) - (إصلاح أخطاء 'hurst' و 'atr_normalized_return')
         """
         if dataframe.empty or dataframe is None or len(dataframe) < 100: 
             return {}
@@ -62,10 +60,11 @@ class AdvancedTechnicalAnalyzer:
             # --- 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:
+            if adx_data is not None and not adx_data.empty:
                 adx_series = adx_data['ADX_14']
-                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
+                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
 
             # --- 4. ميزات "الحجم" (Volume) و "السيولة" ---
             vol_ma_50 = volume.tail(50).mean(); vol_std_50 = volume.tail(50).std()
@@ -73,66 +72,69 @@ class AdvancedTechnicalAnalyzer:
             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: features['cmf_20'] = cmf.iloc[-1]
+            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: features['vroc_12'] = vroc.iloc[-1]
-            if obv_series is not None:
+            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
 
             # --- 5. ميزات "تجميعية" (Aggregative) ---
-            if rsi_series is not None:
+            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:
+            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: features['adx_14'] = adx_data['ADX_14'].iloc[-1]
+            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
 
-            # --- 6. ميزات "التقلب" (Volatility) ---
-            if atr_series is not None:
+            # 🔴 --- (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: features['vol_of_vol'] = vol_of_vol_series.iloc[-1]
+                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
-            
-            # --- 7. (جديد V9.8) ميزات النظام (Regime Features) ---
+            else:
+                features['atr_normalized_return'] = 0.0 # (قيمة افتراضية إذا فشل ATR)
+                
+            # 🔴 --- (V10.2 - إصلاح 'hurst') --- 🔴
             if HURST_AVAILABLE:
-                hurst_series = close.tail(100).to_numpy()
-                H, c, data = compute_Hc(hurst_series, kind='price', simplified=True)
-                features['hurst'] = H
+                try:
+                    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)
 
             ppo_data = ta.ppo(close, fast=12, slow=26, signal=9)
-            if ppo_data is not None:
+            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 as e:
             # print(f"⚠️ خطأ في حساب ميزات V9.8 الذكية: {e}"); 
-            pass # (نتجاهل الأخطاء في التداول الحي ونعيد ميزات جزئية)
+            pass 
             
         final_features = {};
         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
-    # 🔴 --- END OF UPDATED FUNCTION (V10.0) --- 🔴
-
 
     # -----------------------------------------------------------------
     # --- (الدوال القديمة تبقى كما هي للاستخدامات الأخرى مثل Sentry 1m) ---
     # -----------------------------------------------------------------
 
     def calculate_all_indicators(self, dataframe, timeframe):
-        """حساب جميع المؤشرات الفنية للإطار الزمني المحدد"""
-        if dataframe.empty or dataframe is None: 
-            return {}
-        
+        if dataframe.empty or dataframe is None: return {}
         indicators = {}
-        
         try:
             indicators.update(self._calculate_trend_indicators(dataframe))
             indicators.update(self._calculate_momentum_indicators(dataframe))
@@ -141,250 +143,146 @@ class AdvancedTechnicalAnalyzer:
             indicators.update(self._calculate_cycle_indicators(dataframe))
         except Exception as e:
             print(f"⚠️ خطأ في حساب المؤشرات لـ {timeframe}: {e}")
-        
         return indicators
     
     def _calculate_trend_indicators(self, dataframe):
-        """حساب مؤشرات الاتجاه"""
-        trend = {}
-        
+        trend = {};
         try:
-            if dataframe is None or dataframe.empty or 'close' not in dataframe.columns:
-                return {}
-            
+            if dataframe is None or dataframe.empty or 'close' not in dataframe.columns: return {};
             if len(dataframe) >= 9: 
-                ema_9 = ta.ema(dataframe['close'], length=9)
-                if ema_9 is not None and not ema_9.empty and not pd.isna(ema_9.iloc[-1]): 
-                    trend['ema_9'] = float(ema_9.iloc[-1])
-            
+                ema_9 = ta.ema(dataframe['close'], length=9);
+                if ema_9 is not None and not ema_9.empty and not pd.isna(ema_9.iloc[-1]): trend['ema_9'] = float(ema_9.iloc[-1]);
             if len(dataframe) >= 21: 
-                ema_21 = ta.ema(dataframe['close'], length=21)
-                if ema_21 is not None and not ema_21.empty and not pd.isna(ema_21.iloc[-1]): 
-                    trend['ema_21'] = float(ema_21.iloc[-1])
-            
+                ema_21 = ta.ema(dataframe['close'], length=21);
+                if ema_21 is not None and not ema_21.empty and not pd.isna(ema_21.iloc[-1]): trend['ema_21'] = float(ema_21.iloc[-1]);
             if len(dataframe) >= 50: 
-                ema_50 = ta.ema(dataframe['close'], length=50)
-                if ema_50 is not None and not ema_50.empty and not pd.isna(ema_50.iloc[-1]): 
-                    trend['ema_50'] = float(ema_50.iloc[-1])
-            
+                ema_50 = ta.ema(dataframe['close'], length=50);
+                if ema_50 is not None and not ema_50.empty and not pd.isna(ema_50.iloc[-1]): trend['ema_50'] = float(ema_50.iloc[-1]);
             if len(dataframe) >= 200: 
-                ema_200 = ta.ema(dataframe['close'], length=200)
-                if ema_200 is not None and not ema_200.empty and not pd.isna(ema_200.iloc[-1]): 
-                    trend['ema_200'] = float(ema_200.iloc[-1])
-            
+                ema_200 = ta.ema(dataframe['close'], length=200);
+                if ema_200 is not None and not ema_200.empty and not pd.isna(ema_200.iloc[-1]): trend['ema_200'] = float(ema_200.iloc[-1]);
             if len(dataframe) >= 26:
                 try:
-                    ichimoku = ta.ichimoku(dataframe['high'], dataframe['low'], dataframe['close'])
+                    ichimoku = ta.ichimoku(dataframe['high'], dataframe['low'], dataframe['close']);
                     if ichimoku is not None and len(ichimoku) > 0:
-                        conversion_line = ichimoku[0].get('ITS_9') if ichimoku[0] is not None else None
-                        base_line = ichimoku[0].get('IKS_26') if ichimoku[0] is not None else None
-                        
-                        if conversion_line is not None and not conversion_line.empty and not pd.isna(conversion_line.iloc[-1]): 
-                            trend['ichimoku_conversion'] = float(conversion_line.iloc[-1])
-                        if base_line is not None and not base_line.empty and not pd.isna(base_line.iloc[-1]): 
-                            trend['ichimoku_base'] = float(base_line.iloc[-1])
-                except Exception as ichimoku_error:
-                    pass 
-            
+                        conversion_line = ichimoku[0].get('ITS_9') if ichimoku[0] is not None else None;
+                        base_line = ichimoku[0].get('IKS_26') if ichimoku[0] is not None else None;
+                        if conversion_line is not None and not conversion_line.empty and not pd.isna(conversion_line.iloc[-1]): trend['ichimoku_conversion'] = float(conversion_line.iloc[-1]);
+                        if base_line is not None and not base_line.empty and not pd.isna(base_line.iloc[-1]): trend['ichimoku_base'] = float(base_line.iloc[-1]);
+                except Exception as ichimoku_error: pass;
             if len(dataframe) >= 14:
                 try:
-                    adx_result = ta.adx(dataframe['high'], dataframe['low'], dataframe['close'], length=14)
+                    adx_result = ta.adx(dataframe['high'], dataframe['low'], dataframe['close'], length=14);
                     if adx_result is not None and not adx_result.empty:
-                        adx_value = adx_result.get('ADX_14')
-                        if adx_value is not None and not adx_value.empty and not pd.isna(adx_value.iloc[-1]): 
-                            trend['adx'] = float(adx_value.iloc[-1])
-                except Exception as adx_error:
-                    pass 
-        
-        except Exception as e:
-            # print(f"⚠️ خطأ في حساب مؤشرات الاتجاه: {e}")
-            pass
-        
-        return {key: value for key, value in trend.items() if value is not None and not np.isnan(value)}
+                        adx_value = adx_result.get('ADX_14');
+                        if adx_value is not None and not adx_value.empty and not pd.isna(adx_value.iloc[-1]): trend['adx'] = float(adx_value.iloc[-1]);
+                except Exception as adx_error: pass;
+        except Exception as e: pass;
+        return {key: value for key, value in trend.items() if value is not None and not np.isnan(value)};
     
     def _calculate_momentum_indicators(self, dataframe):
-        """حساب مؤشرات الزخم"""
-        momentum = {}
-        
+        momentum = {};
         try:
-            if dataframe is None or dataframe.empty or 'close' not in dataframe.columns:
-                return {}
-            
+            if dataframe is None or dataframe.empty or 'close' not in dataframe.columns: return {};
             if len(dataframe) >= 14:
-                rsi = ta.rsi(dataframe['close'], length=14)
-                if rsi is not None and not rsi.empty and not pd.isna(rsi.iloc[-1]): 
-                    momentum['rsi'] = float(rsi.iloc[-1])
-            
+                rsi = ta.rsi(dataframe['close'], length=14);
+                if rsi is not None and not rsi.empty and not pd.isna(rsi.iloc[-1]): momentum['rsi'] = float(rsi.iloc[-1]);
             if len(dataframe) >= 26:
-                macd = ta.macd(dataframe['close'])
+                macd = ta.macd(dataframe['close']);
                 if macd is not None and not macd.empty:
-                    macd_hist = macd.get('MACDh_12_26_9')
-                    macd_line = macd.get('MACD_12_26_9')
-                    
-                    if macd_hist is not None and not macd_hist.empty and not pd.isna(macd_hist.iloc[-1]): 
-                        momentum['macd_hist'] = float(macd_hist.iloc[-1])
-                    if macd_line is not None and not macd_line.empty and not pd.isna(macd_line.iloc[-1]): 
-                        momentum['macd_line'] = float(macd_line.iloc[-1])
-            
+                    macd_hist = macd.get('MACDh_12_26_9');
+                    macd_line = macd.get('MACD_12_26_9');
+                    if macd_hist is not None and not macd_hist.empty and not pd.isna(macd_hist.iloc[-1]): momentum['macd_hist'] = float(macd_hist.iloc[-1]);
+                    if macd_line is not None and not macd_line.empty and not pd.isna(macd_line.iloc[-1]): momentum['macd_line'] = float(macd_line.iloc[-1]);
             if len(dataframe) >= 14:
-                stoch_rsi = ta.stochrsi(dataframe['close'], length=14)
+                stoch_rsi = ta.stochrsi(dataframe['close'], length=14);
                 if stoch_rsi is not None and not stoch_rsi.empty:
-                    stoch_k = stoch_rsi.get('STOCHRSIk_14_14_3_3')
-                    if stoch_k is not None and not stoch_k.empty and not pd.isna(stoch_k.iloc[-1]): 
-                        momentum['stoch_rsi_k'] = float(stoch_k.iloc[-1])
-            
+                    stoch_k = stoch_rsi.get('STOCHRSIk_14_14_3_3');
+                    if stoch_k is not None and not stoch_k.empty and not pd.isna(stoch_k.iloc[-1]): momentum['stoch_rsi_k'] = float(stoch_k.iloc[-1]);
             if len(dataframe) >= 14:
-                williams = ta.willr(dataframe['high'], dataframe['low'], dataframe['close'], length=14)
-                if williams is not None and not williams.empty and not pd.isna(williams.iloc[-1]): 
-                    momentum['williams_r'] = float(williams.iloc[-1])
-        
-        except Exception as e:
-            # print(f"⚠️ خطأ في حساب مؤشرات الزخم: {e}")
-            pass
-        
-        return {key: value for key, value in momentum.items() if value is not None and not np.isnan(value)}
+                williams = ta.willr(dataframe['high'], dataframe['low'], dataframe['close'], length=14);
+                if williams is not None and not williams.empty and not pd.isna(williams.iloc[-1]): momentum['williams_r'] = float(williams.iloc[-1]);
+        except Exception as e: pass;
+        return {key: value for key, value in momentum.items() if value is not None and not np.isnan(value)};
     
     def _calculate_volatility_indicators(self, dataframe):
-        """حساب مؤشرات التقلب"""
-        volatility = {}
-        
+        volatility = {};
         try:
-            if dataframe is None or dataframe.empty or 'close' not in dataframe.columns:
-                return {}
-            
+            if dataframe is None or dataframe.empty or 'close' not in dataframe.columns: return {};
             if len(dataframe) >= 20:
-                bollinger_bands = ta.bbands(dataframe['close'], length=20, std=2)
+                bollinger_bands = ta.bbands(dataframe['close'], length=20, std=2);
                 if bollinger_bands is not None and not bollinger_bands.empty:
-                    bb_lower = bollinger_bands.get('BBL_20_2.0')
-                    bb_upper = bollinger_bands.get('BBU_20_2.0') 
-                    bb_middle = bollinger_bands.get('BBM_20_2.0')
-                    
-                    if bb_lower is not None and not bb_lower.empty and not pd.isna(bb_lower.iloc[-1]): 
-                        volatility['bb_lower'] = float(bb_lower.iloc[-1])
-                    if bb_upper is not None and not bb_upper.empty and not pd.isna(bb_upper.iloc[-1]): 
-                        volatility['bb_upper'] = float(bb_upper.iloc[-1])
-                    if bb_middle is not None and not bb_middle.empty and not pd.isna(bb_middle.iloc[-1]): 
-                        volatility['bb_middle'] = float(bb_middle.iloc[-1])
-            
+                    bb_lower = bollinger_bands.get('BBL_20_2.0'); bb_upper = bollinger_bands.get('BBU_20_2.0'); bb_middle = bollinger_bands.get('BBM_20_2.0');
+                    if bb_lower is not None and not bb_lower.empty and not pd.isna(bb_lower.iloc[-1]): volatility['bb_lower'] = float(bb_lower.iloc[-1]);
+                    if bb_upper is not None and not bb_upper.empty and not pd.isna(bb_upper.iloc[-1]): volatility['bb_upper'] = float(bb_upper.iloc[-1]);
+                    if bb_middle is not None and not bb_middle.empty and not pd.isna(bb_middle.iloc[-1]): volatility['bb_middle'] = float(bb_middle.iloc[-1]);
             if len(dataframe) >= 14:
-                average_true_range = ta.atr(dataframe['high'], dataframe['low'], dataframe['close'], length=14)
+                average_true_range = ta.atr(dataframe['high'], dataframe['low'], dataframe['close'], length=14);
                 if average_true_range is not None and not average_true_range.empty and not pd.isna(average_true_range.iloc[-1]): 
-                    atr_value = float(average_true_range.iloc[-1])
-                    volatility['atr'] = atr_value
-                    current_close = dataframe['close'].iloc[-1] if not dataframe['close'].empty else 0
-                    if atr_value and current_close > 0: 
-                        volatility['atr_percent'] = (atr_value / current_price) * 100
-        
-        except Exception as e:
-            # print(f"⚠️ خطأ في حساب مؤشرات التقلب: {e}")
-            pass
-        
-        return {key: value for key, value in volatility.items() if value is not None and not np.isnan(value)}
+                    atr_value = float(average_true_range.iloc[-1]); volatility['atr'] = atr_value;
+                    current_close = dataframe['close'].iloc[-1] if not dataframe['close'].empty else 0;
+                    if atr_value and current_close > 0: volatility['atr_percent'] = (atr_value / current_close) * 100;
+        except Exception as e: pass;
+        return {key: value for key, value in volatility.items() if value is not None and not np.isnan(value)};
     
     def _calculate_volume_indicators(self, dataframe, timeframe):
-        """حساب مؤشرات الحجم"""
-        volume = {}
-        
+        volume = {};
         try:
-            if dataframe is None or dataframe.empty or 'close' not in dataframe.columns or 'volume' not in dataframe.columns:
-                return {}
-            
+            if dataframe is None or dataframe.empty or 'close' not in dataframe.columns or 'volume' not in dataframe.columns: return {};
             if len(dataframe) >= 1:
                 try:
-                    df_vwap = dataframe.copy()
-                    
+                    df_vwap = dataframe.copy();
                     if not isinstance(df_vwap.index, pd.DatetimeIndex):
                         if 'timestamp' in df_vwap.columns:
-                            df_vwap['timestamp'] = pd.to_datetime(df_vwap['timestamp'], unit='ms')
-                            df_vwap.set_index('timestamp', inplace=True)
+                            df_vwap['timestamp'] = pd.to_datetime(df_vwap['timestamp'], unit='ms'); df_vwap.set_index('timestamp', inplace=True);
                         elif not df_vwap.index.is_numeric():
-                             # (محاولة تحويل الفهرس إذا كان هو التايم ستامب)
-                             df_vwap.index = pd.to_datetime(df_vwap.index, unit='ms')
+                             df_vwap.index = pd.to_datetime(df_vwap.index, unit='ms');
                         else:
-                             raise ValueError("DataFrame needs 'timestamp' column or DatetimeIndex")
-                    
-                    df_vwap.sort_index(inplace=True)
-                    
-                    volume_weighted_average_price = ta.vwap(
-                        high=df_vwap['high'],    
-                        low=df_vwap['low'],     
-                        close=df_vwap['close'],   
-                        volume=df_vwap['volume']  
-                    )
-                    
-                    if volume_weighted_average_price is not None and not volume_weighted_average_price.empty and not pd.isna(volume_weighted_average_price.iloc[-1]): 
-                        volume['vwap'] = float(volume_weighted_average_price.iloc[-1])
-                        
+                             raise ValueError("DataFrame needs 'timestamp' column or DatetimeIndex");
+                    df_vwap.sort_index(inplace=True);
+                    volume_weighted_average_price = ta.vwap(high=df_vwap['high'], low=df_vwap['low'], close=df_vwap['close'], volume=df_vwap['volume']);
+                    if volume_weighted_average_price is not None and not volume_weighted_average_price.empty and not pd.isna(volume_weighted_average_price.iloc[-1]): volume['vwap'] = float(volume_weighted_average_price.iloc[-1]);
                 except Exception as vwap_error:
-                    if "VWAP requires an ordered DatetimeIndex" not in str(vwap_error) and "Index" not in str(vwap_error):
-                        # print(f"⚠️ خطأ في حساب VWAP لـ {timeframe}: {vwap_error}")
-                        pass
-                    
+                    if "VWAP requires an ordered DatetimeIndex" not in str(vwap_error) and "Index" not in str(vwap_error): pass;
                     if len(dataframe) >= 20:
                         try:
-                            typical_price = (dataframe['high'] + dataframe['low'] + dataframe['close']) / 3
-                            vwap_simple = (typical_price * dataframe['volume']).sum() / dataframe['volume'].sum()
-                            if not np.isnan(vwap_simple):
-                                volume['vwap'] = float(vwap_simple)
-                        except Exception as simple_vwap_error:
-                            pass
-            
+                            typical_price = (dataframe['high'] + dataframe['low'] + dataframe['close']) / 3;
+                            vwap_simple = (typical_price * dataframe['volume']).sum() / dataframe['volume'].sum();
+                            if not np.isnan(vwap_simple): volume['vwap'] = float(vwap_simple);
+                        except Exception as simple_vwap_error: pass;
             try:
-                on_balance_volume = ta.obv(dataframe['close'], dataframe['volume'])
-                if on_balance_volume is not None and not on_balance_volume.empty and not pd.isna(on_balance_volume.iloc[-1]): 
-                    volume['obv'] = float(on_balance_volume.iloc[-1])
-            except Exception as obv_error:
-                pass
-            
+                on_balance_volume = ta.obv(dataframe['close'], dataframe['volume']);
+                if on_balance_volume is not None and not on_balance_volume.empty and not pd.isna(on_balance_volume.iloc[-1]): volume['obv'] = float(on_balance_volume.iloc[-1]);
+            except Exception as obv_error: pass;
             if len(dataframe) >= 14:
                 try:
-                    money_flow_index = ta.mfi(dataframe['high'], dataframe['low'], dataframe['close'], dataframe['volume'], length=14)
-                    if money_flow_index is not None and not money_flow_index.empty and not pd.isna(money_flow_index.iloc[-1]): 
-                        volume['mfi'] = float(money_flow_index.iloc[-1])
-                except Exception as mfi_error:
-                    pass
-            
+                    money_flow_index = ta.mfi(dataframe['high'], dataframe['low'], dataframe['close'], dataframe['volume'], length=14);
+                    if money_flow_index is not None and not money_flow_index.empty and not pd.isna(money_flow_index.iloc[-1]): volume['mfi'] = float(money_flow_index.iloc[-1]);
+                except Exception as mfi_error: pass;
             if len(dataframe) >= 20:
                 try:
-                    volume_avg_20 = float(dataframe['volume'].tail(20).mean())
-                    current_volume = float(dataframe['volume'].iloc[-1]) if not dataframe['volume'].empty else 0
+                    volume_avg_20 = float(dataframe['volume'].tail(20).mean());
+                    current_volume = float(dataframe['volume'].iloc[-1]) if not dataframe['volume'].empty else 0;
                     if volume_avg_20 and volume_avg_20 > 0 and current_volume > 0: 
-                        volume_ratio = current_volume / volume_avg_20
-                        if not np.isnan(volume_ratio):
-                            volume['volume_ratio'] = volume_ratio
-                except Exception as volume_error:
-                    pass
-        
-        except Exception as e:
-            # print(f"⚠️ خطأ في حساب مؤشرات الحجم: {e}")
-            pass
-        
-        return {key: value for key, value in volume.items() if value is not None and not np.isnan(value)}
+                        volume_ratio = current_volume / volume_avg_20;
+                        if not np.isnan(volume_ratio): volume['volume_ratio'] = volume_ratio;
+                except Exception as volume_error: pass;
+        except Exception as e: pass;
+        return {key: value for key, value in volume.items() if value is not None and not np.isnan(value)};
     
     def _calculate_cycle_indicators(self, dataframe):
-        """حساب مؤشرات الدورة"""
-        cycle = {}
-        
+        cycle = {};
         try:
-            if dataframe is None or dataframe.empty or 'close' not in dataframe.columns:
-                return {}
-            
+            if dataframe is None or dataframe.empty or 'close' not in dataframe.columns: return {};
             if len(dataframe) >= 9:
-                hull_moving_average = ta.hma(dataframe['close'], length=9)
-                if hull_moving_average is not None and not hull_moving_average.empty and not pd.isna(hull_moving_average.iloc[-1]): 
-                    cycle['hull_ma'] = float(hull_moving_average.iloc[-1])
-            
+                hull_moving_average = ta.hma(dataframe['close'], length=9);
+                if hull_moving_average is not None and not hull_moving_average.empty and not pd.isna(hull_moving_average.iloc[-1]): cycle['hull_ma'] = float(hull_moving_average.iloc[-1]);
             if len(dataframe) >= 10:
-                supertrend = ta.supertrend(dataframe['high'], dataframe['low'], dataframe['close'], length=10, multiplier=3)
+                supertrend = ta.supertrend(dataframe['high'], dataframe['low'], dataframe['close'], length=10, multiplier=3);
                 if supertrend is not None and not supertrend.empty:
-                    supertrend_value = supertrend.get('SUPERT_10_3.0')
-                    if supertrend_value is not None and not supertrend_value.empty and not pd.isna(supertrend_value.iloc[-1]): 
-                        cycle['supertrend'] = float(supertrend_value.iloc[-1])
-        
-        except Exception as e:
-            # print(f"⚠️ خطأ في حساب مؤشرات الدورة: {e}")
-            pass
-        
-        return {key: value for key, value in cycle.items() if value is not None and not np.isnan(value)}
+                    supertrend_value = supertrend.get('SUPERT_10_3.0');
+                    if supertrend_value is not None and not supertrend_value.empty and not pd.isna(supertrend_value.iloc[-1]): cycle['supertrend'] = float(supertrend_value.iloc[-1]);
+        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 (V10.1 - SyntaxError FIX)")
+print("✅ ML Module: Technical Indicators loaded (V10.2 - Bug FIX)")