Update ML.py

ML.py

@@ -19,7 +19,7 @@ class AdvancedTechnicalAnalyzer:
     
     def calculate_all_indicators(self, dataframe, timeframe):
         """حساب جميع المؤشرات الفنية للإطار الزمني المحدد"""
-        if dataframe.empty: 
+        if dataframe.empty or dataframe is None: 
             return {}
         
         indicators = {}
@@ -40,46 +40,57 @@ class AdvancedTechnicalAnalyzer:
         trend = {}
         
         try:
+            # التحقق من وجود البيانات الأساسية
+            if dataframe is None or dataframe.empty or 'close' not in dataframe.columns:
+                return {}
+            
             # المتوسطات المتحركة
             if len(dataframe) >= 9: 
                 ema_9 = ta.ema(dataframe['close'], length=9)
-                if ema_9 is not None and not ema_9.empty: 
+                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: 
+                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: 
+                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: 
+                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])
             
             # إيشيموكو
             if len(dataframe) >= 26:
-                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')
-                    base_line = ichimoku[0].get('IKS_26')
-                    
-                    if conversion_line is not None and not conversion_line.empty: 
-                        trend['ichimoku_conversion'] = float(conversion_line.iloc[-1])
-                    if base_line is not None and not base_line.empty: 
-                        trend['ichimoku_base'] = float(base_line.iloc[-1])
+                try:
+                    ichimoku = ta.ichimoku(dataframe['high'], dataframe['low'], dataframe['close'])
+                    if ichimoku is not None and len(ichimoku) > 0:
+                        # التحقق من أن ichimoku ليس None وأنه يحتوي على بيانات
+                        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:
+                    print(f"⚠️ خطأ في حساب إيشيموكو: {ichimoku_error}")
             
             # ADX - قوة الاتجاه
             if len(dataframe) >= 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: 
-                        trend['adx'] = float(adx_value.iloc[-1])
+                try:
+                    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:
+                    print(f"⚠️ خطأ في حساب ADX: {adx_error}")
         
         except Exception as e:
             print(f"⚠️ خطأ في حساب مؤشرات الاتجاه: {e}")
@@ -91,10 +102,14 @@ class AdvancedTechnicalAnalyzer:
         momentum = {}
         
         try:
+            # التحقق من وجود البيانات الأساسية
+            if dataframe is None or dataframe.empty or 'close' not in dataframe.columns:
+                return {}
+            
             # RSI
             if len(dataframe) >= 14:
                 rsi = ta.rsi(dataframe['close'], length=14)
-                if rsi is not None and not rsi.empty: 
+                if rsi is not None and not rsi.empty and not pd.isna(rsi.iloc[-1]): 
                     momentum['rsi'] = float(rsi.iloc[-1])
             
             # MACD
@@ -104,9 +119,9 @@ class AdvancedTechnicalAnalyzer:
                     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: 
+                    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: 
+                    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])
             
             # ستوكاستك RSI
@@ -114,13 +129,13 @@ class AdvancedTechnicalAnalyzer:
                 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: 
+                    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])
             
             # ويليامز %R
             if len(dataframe) >= 14:
                 williams = ta.willr(dataframe['high'], dataframe['low'], dataframe['close'], length=14)
-                if williams is not None and not williams.empty: 
+                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:
@@ -133,6 +148,10 @@ class AdvancedTechnicalAnalyzer:
         volatility = {}
         
         try:
+            # التحقق من وجود البيانات الأساسية
+            if dataframe is None or dataframe.empty or 'close' not in dataframe.columns:
+                return {}
+            
             # بولينجر باندز
             if len(dataframe) >= 20:
                 bollinger_bands = ta.bbands(dataframe['close'], length=20, std=2)
@@ -141,21 +160,22 @@ class AdvancedTechnicalAnalyzer:
                     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: 
+                    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: 
+                    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: 
+                    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])
             
             # متوسط المدى الحقيقي (ATR)
             if len(dataframe) >= 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: 
+                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
-                    if atr_value and dataframe['close'].iloc[-1] > 0: 
-                        volatility['atr_percent'] = (atr_value / dataframe['close'].iloc[-1]) * 100
+                    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:
             print(f"⚠️ خطأ في حساب مؤشرات التقلب: {e}")
@@ -167,16 +187,21 @@ class AdvancedTechnicalAnalyzer:
         volume = {}
         
         try:
+            # التحقق من وجود البيانات الأساسية
+            if dataframe is None or dataframe.empty or 'close' not in dataframe.columns or 'volume' not in dataframe.columns:
+                return {}
+            
             # VWAP - إصلاح المشكلة هنا
-            if len(dataframe) >= 1 and 'timestamp' in dataframe.columns:
+            if len(dataframe) >= 1:
                 try:
                     # إنشاء نسخة من البيانات مع DatetimeIndex مرتب
                     df_vwap = dataframe.copy()
                     
                     # تحويل timestamp إلى datetime وضبطه كـ index
                     if not isinstance(df_vwap.index, pd.DatetimeIndex):
-                        df_vwap['timestamp'] = pd.to_datetime(df_vwap['timestamp'], unit='ms')
-                        df_vwap.set_index('timestamp', inplace=True)
+                        if 'timestamp' in df_vwap.columns:
+                            df_vwap['timestamp'] = pd.to_datetime(df_vwap['timestamp'], unit='ms')
+                            df_vwap.set_index('timestamp', inplace=True)
                     
                     # التأكد من أن الفهرس مرتب
                     df_vwap.sort_index(inplace=True)
@@ -189,21 +214,25 @@ class AdvancedTechnicalAnalyzer:
                         volume=df_vwap['volume']
                     )
                     
-                    if volume_weighted_average_price is not None and not volume_weighted_average_price.empty: 
+                    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:
                     print(f"⚠️ خطأ في حساب VWAP لـ {timeframe}: {vwap_error}")
                     # استخدام بديل لـ VWAP في حالة الخطأ
                     if len(dataframe) >= 20:
-                        typical_price = (dataframe['high'] + dataframe['low'] + dataframe['close']) / 3
-                        vwap_simple = (typical_price * dataframe['volume']).sum() / dataframe['volume'].sum()
-                        volume['vwap'] = float(vwap_simple)
+                        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:
+                            print(f"⚠️ خطأ في حساب VWAP البديل: {simple_vwap_error}")
             
             # OBV
             try:
                 on_balance_volume = ta.obv(dataframe['close'], dataframe['volume'])
-                if on_balance_volume is not None and not on_balance_volume.empty: 
+                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:
                 print(f"⚠️ خطأ في حساب OBV: {obv_error}")
@@ -212,7 +241,7 @@ class AdvancedTechnicalAnalyzer:
             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: 
+                    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:
                     print(f"⚠️ خطأ في حساب MFI: {mfi_error}")
@@ -221,8 +250,11 @@ class AdvancedTechnicalAnalyzer:
             if len(dataframe) >= 20:
                 try:
                     volume_avg_20 = float(dataframe['volume'].tail(20).mean())
-                    if volume_avg_20 and volume_avg_20 > 0: 
-                        volume['volume_ratio'] = float(dataframe['volume'].iloc[-1] / volume_avg_20)
+                    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:
                     print(f"⚠️ خطأ في حساب نسبة الحجم: {volume_error}")
         
@@ -236,10 +268,14 @@ class AdvancedTechnicalAnalyzer:
         cycle = {}
         
         try:
+            # التحقق من وجود البيانات الأساسية
+            if dataframe is None or dataframe.empty or 'close' not in dataframe.columns:
+                return {}
+            
             # هول موفينج افريج
             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: 
+                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])
             
             # سوبرتريند
@@ -247,7 +283,7 @@ class AdvancedTechnicalAnalyzer:
                 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: 
+                    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:
@@ -474,9 +510,13 @@ class ChartPatternAnalyzer:
     
     def _create_dataframe(self, candles):
         """إنشاء DataFrame من بيانات الشموع"""
-        df = pd.DataFrame(candles, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
-        df[['open', 'high', 'low', 'close', 'volume']] = df[['open', 'high', 'low', 'close', 'volume']].astype(float)
-        return df
+        try:
+            df = pd.DataFrame(candles, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
+            df[['open', 'high', 'low', 'close', 'volume']] = df[['open', 'high', 'low', 'close', 'volume']].astype(float)
+            return df
+        except Exception as e:
+            print(f"❌ خطأ في إنشاء DataFrame: {e}")
+            return pd.DataFrame()
     
     async def _analyze_timeframe_patterns(self, dataframe, timeframe):
         """تحليل الأنماط لإطار زمني محدد"""
@@ -489,7 +529,7 @@ class ChartPatternAnalyzer:
         }
         
         try:
-            if len(dataframe) < 20:
+            if dataframe is None or dataframe.empty or len(dataframe) < 20:
                 return pattern_info
             
             closes = dataframe['close'].values
@@ -638,7 +678,7 @@ class ChartPatternAnalyzer:
     def _detect_trend_pattern(self, dataframe):
         """كشف نمط الاتجاه"""
         try:
-            if len(dataframe) < 20:
+            if dataframe is None or dataframe.empty or len(dataframe) < 20:
                 return {'detected': False}
             
             closes = dataframe['close'].values
@@ -1115,17 +1155,21 @@ class MLProcessor:
     
     def _create_dataframe(self, candles):
         """إنشاء DataFrame من بيانات الشموع مع DatetimeIndex مرتب"""
-        df = pd.DataFrame(candles, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
-        df[['open', 'high', 'low', 'close', 'volume']] = df[['open', 'high', 'low', 'close', 'volume']].astype(float)
-        
-        # تحويل timestamp إلى datetime وضبطه كـ index
-        df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
-        df.set_index('timestamp', inplace=True)
-        
-        # التأكد من أن الفهرس مرتب
-        df.sort_index(inplace=True)
-        
-        return df
+        try:
+            df = pd.DataFrame(candles, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
+            df[['open', 'high', 'low', 'close', 'volume']] = df[['open', 'high', 'low', 'close', 'volume']].astype(float)
+            
+            # تحويل timestamp إلى datetime وضبطه كـ index
+            df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
+            df.set_index('timestamp', inplace=True)
+            
+            # التأكد من أن الفهرس مرتب
+            df.sort_index(inplace=True)
+            
+            return df
+        except Exception as e:
+            print(f"❌ خطأ في إنشاء DataFrame: {e}")
+            return pd.DataFrame()
     
     def _calculate_enhanced_final_score(self, analysis):
         """حساب الدرجة النهائية المحسنة"""
@@ -1219,4 +1263,4 @@ def safe_json_parse(json_string):
             print(f"❌ فشل تحليل JSON بعد الإصلاح: {e}")
             return None
 
-print("✅ ML Processor loaded - Fixed VWAP Issue & Enhanced Analysis")
+print("✅ ML Processor loaded - Fixed NoneType Errors & Enhanced Analysis")