Create indicators.py

ml_engine/indicators.py

@@ -0,0 +1,290 @@
+# ml_engine/indicators.py
+import pandas as pd
+import pandas_ta as ta
+import numpy as np
+
+class AdvancedTechnicalAnalyzer:
+    def __init__(self):
+        self.indicators_config = {
+            'trend': ['ema_9', 'ema_21', 'ema_50', 'ema_200', 'ichimoku', 'adx', 'parabolic_sar', 'dmi'],
+            'momentum': ['rsi', 'stoch_rsi', 'macd', 'williams_r', 'cci', 'awesome_oscillator', 'momentum'],
+            'volatility': ['bbands', 'atr', 'keltner', 'donchian', 'rvi'],
+            'volume': ['vwap', 'obv', 'mfi', 'volume_profile', 'ad', 'volume_oscillator'],
+            'cycle': ['hull_ma', 'supertrend', 'zigzag', 'fisher_transform']
+        }
+    
+    def calculate_all_indicators(self, dataframe, timeframe):
+        """حساب جميع المؤشرات الفنية للإطار الزمني المحدد"""
+        if dataframe.empty or dataframe is None: 
+            return {}
+        
+        indicators = {}
+        
+        try:
+            indicators.update(self._calculate_trend_indicators(dataframe))
+            indicators.update(self._calculate_momentum_indicators(dataframe))
+            indicators.update(self._calculate_volatility_indicators(dataframe))
+            indicators.update(self._calculate_volume_indicators(dataframe, timeframe))
+            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 = {}
+        
+        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 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])
+            
+            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])
+            
+            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])
+            
+            # إيشيموكو
+            if len(dataframe) >= 26:
+                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:
+                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}")
+        
+        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 = {}
+        
+        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 and not pd.isna(rsi.iloc[-1]): 
+                    momentum['rsi'] = float(rsi.iloc[-1])
+            
+            # MACD
+            if len(dataframe) >= 26:
+                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])
+            
+            # ستوكاستك RSI
+            if len(dataframe) >= 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])
+            
+            # 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 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}")
+        
+        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 = {}
+        
+        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)
+                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])
+            
+            # 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 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_close) * 100
+        
+        except Exception as e:
+            print(f"⚠️ خطأ في حساب مؤشرات التقلب: {e}")
+        
+        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 = {}
+        
+        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:
+                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):
+                        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)
+                    
+                    # حساب VWAP
+                    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:
+                    print(f"⚠️ خطأ في حساب VWAP لـ {timeframe}: {vwap_error}")
+                    # استخدام بديل لـ VWAP في حالة الخطأ
+                    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:
+                            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 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}")
+            
+            # MFI
+            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:
+                    print(f"⚠️ خطأ في حساب MFI: {mfi_error}")
+            
+            # نسبة الحجم
+            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
+                    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}")
+        
+        except Exception as e:
+            print(f"⚠️ خطأ في حساب مؤشرات الحجم: {e}")
+        
+        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 = {}
+        
+        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 and not pd.isna(hull_moving_average.iloc[-1]): 
+                    cycle['hull_ma'] = float(hull_moving_average.iloc[-1])
+            
+            # سوبرتريند
+            if len(dataframe) >= 10:
+                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}")
+        
+        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")