Update ml_engine/indicators.py

ml_engine/indicators.py

@@ -1,9 +1,18 @@
-# ml_engine/indicators.py (V9.1 - Smart Feature Engineering)
+# ml_engine/indicators.py (V10.0 - Super-Brain Features)
 import pandas as pd
 import pandas_ta as ta
 import numpy as np
 from typing import Dict
 
+try:
+    from hurst import compute_Hc
+    HURST_AVAILABLE = True
+except ImportError:
+    print("⚠️ مكتبة 'hurst' غير موجودة. ميزة "مفتاح النظام" ستكون معطلة.")
+    print("   -> قم بتثبيتها: pip install hurst")
+    HURST_AVAILABLE = False
+
+
 class AdvancedTechnicalAnalyzer:
     def __init__(self):
         # (هذا الكونفيغ سيبقى للاستخدامات القديمة مثل الحارس 1m)
@@ -15,16 +24,13 @@ class AdvancedTechnicalAnalyzer:
             'cycle': ['hull_ma', 'supertrend', 'zigzag', 'fisher_transform']
         }
     
-    # 🔴 --- START OF NEW FUNCTION (V9.1) --- 🔴
+    # 🔴 --- START OF UPDATED FUNCTION (V10.0 - Super-Brain) --- 🔴
     def calculate_v9_smart_features(self, dataframe: pd.DataFrame) -> Dict[str, float]:
         """
-        (جديد V9.1) - (العقل الحسابي لنموذج الرانكر V9.1)
-        حساب "الميزات الذكية" المتقدمة المستوحاة من خطة GPT (للكاشف المصغر V9.1).
-        هذه الدالة مصممة لتغذية نموذج ML (مثل LightGBM) ببيانات غنية.
+        (محدث V10.0) - (العقل الحسابي لنموذج V9.8)
+        حساب جميع الميزات المتقدمة (بما في ذلك Hurst, CMF, PPO, VROC)
         """
         if dataframe.empty or dataframe is None or len(dataframe) < 100: 
-            # (نحتاج 100 شمعة على الأقل لحساب الميزات الطويلة المدى مثل min(100))
-            # (ملاحظة: data_manager V9.1 سيطلب 200 شمعة لضمان عمل ema_200)
             return {}
             
         features = {}
@@ -41,84 +47,77 @@ class AdvancedTechnicalAnalyzer:
             mfi_series = ta.mfi(high, low, close, volume, length=14)
             atr_series = ta.atr(high, low, close, length=14)
             adx_data = ta.adx(high, low, close, length=14)
+            obv_series = ta.obv(close, volume)
 
-            # --- 2. ميزات "نسب السعر" (Price Ratios) - (لتحديد "القاع") ---
-            # (نسبة السعر إلى المتوسطات المتحركة)
+            # --- 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
-
-            # (نسبة السعر إلى أدنى/أعلى سعر)
-            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
+            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
 
-            # --- 3. ميزات "الميل" (Slope) - (لتحديد "تراكم الزخم") ---
+            # --- 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 ema_14 and ema_50: features['slope_14_50'] = (ema_14 - ema_50) / 14
+            if adx_data is not None:
+                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
 
             # --- 4. ميزات "الحجم" (Volume) و "السيولة" ---
-            # (Z-Score للحجم)
-            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
-            
-            # (فجوة VWAP)
+            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
+            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]
+            vroc = ta.roc(volume, length=12)
+            if vroc is not None: features['vroc_12'] = vroc.iloc[-1]
+            if obv_series is not None:
+                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) - (لفهم السياق) ---
-            # (إحصائيات RSI)
+            # --- 5. ميزات "تجميعية" (Aggregative) ---
             if rsi_series is not None:
-                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()
-
-            # (إحصائيات MFI)
+                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:
-                features['mfi_14'] = mfi_series.iloc[-1]
-                features['mfi_mean_10'] = mfi_series.tail(10).mean()
-
-            # (مؤشر ADX)
-            if adx_data is not None:
-                features['adx_14'] = adx_data['ADX_14'].iloc[-1]
+                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]
 
             # --- 6. ميزات "التقلب" (Volatility) ---
-            # (ATR كنسبة مئوية)
             if atr_series is not None:
                 atr_val = atr_series.iloc[-1]
-                if atr_val and current_price > 0:
-                    features['atr_percent'] = (atr_val / current_price) * 100
-            
-            # (تطبيع العائد بالتقلب)
+                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]
             last_return = close.pct_change().iloc[-1]
-            if atr_val and atr_val > 0:
-                features['atr_normalized_return'] = last_return / atr_val
+            if atr_val and atr_val > 0: features['atr_normalized_return'] = last_return / atr_val
                 
+            # --- 7. (جديد V9.8) ميزات النظام (Regime Features) ---
+            if HURST_AVAILABLE:
+                hurst_series = close.tail(100).to_numpy()
+                H, c, data = compute_Hc(hurst_series, kind='price', simplified=True)
+                features['hurst'] = H
+            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:
+                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:
-            # (في التداول الحي، من الأفضل تسجيل الخطأ بدلاً من طباعته فقط)
-            # print(f"⚠️ خطأ في حساب ميزات V9.1 الذكية: {e}")
-            return {}
-        
-        # (تنظيف: إزالة NaN أو Inf وضمان أن القيم أرقام عشرية)
-        final_features = {}
+            # print(f"⚠️ خطأ في حساب ميزات V9.8 الذكية: {e}"); 
+            pass # (نتجاهل الأخطاء في التداول الحي ونعيد ميزات جزئية)
+            
+        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 # (استبدال القيم غير الصالحة بـ 0.0)
-                
+            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 NEW FUNCTION (V9.1) --- 🔴
+    # 🔴 --- END OF UPDATED FUNCTION (V10.0) --- 🔴
 
 
     # -----------------------------------------------------------------
@@ -386,4 +385,4 @@ class AdvancedTechnicalAnalyzer:
         
         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 (V9.1 - Smart Features Enabled)")
+print("✅ ML Module: Technical Indicators loaded (V10.0 - Super-Brain Features)")