ml_engine/indicators.py

# 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")