Update ml_engine/titan_engine.py

ml_engine/titan_engine.py

@@ -1,5 +1,5 @@
 # ml_engine/titan_engine.py
-# (V1.0 - Titan Inference Engine)
+# (V1.1 - Titan Inference Engine - Robust Fix)
 
 import os
 import joblib
@@ -8,6 +8,7 @@ import pandas as pd
 import pandas_ta as ta
 import xgboost as xgb
 import json
+import traceback
 
 class TitanEngine:
     def __init__(self, model_dir="ml_models/layer2"):
@@ -37,45 +38,85 @@ class TitanEngine:
             print(f"❌ [Titan] خطأ فادح أثناء التهيئة: {e}")
 
     def apply_inverted_pyramid(self, df, tf):
-        """نفس منطق هندسة الميزات المستخدم في التدريب تماماً"""
+        """نفس منطق هندسة الميزات المستخدم في التدريب تماماً مع تحصين ضد الأخطاء"""
         df = df.copy().sort_values('timestamp').reset_index(drop=True)
         # تعيين الفهرس للسهولة في pandas_ta
         df = df.set_index(pd.DatetimeIndex(pd.to_datetime(df['timestamp'], unit='ms')))
         
-        # --- المستوى 1: دقيق (5m, 15m) ---
-        if tf in ['5m', '15m']:
-            df['RSI'] = ta.rsi(df['close'], length=14)
-            df['MACD'] = ta.macd(df['close']).iloc[:, 0]
-            df['MACD_h'] = ta.macd(df['close']).iloc[:, 1]
-            df['CCI'] = ta.cci(df['high'], df['low'], df['close'], length=20)
-            df['ADX'] = ta.adx(df['high'], df['low'], df['close'], length=14).iloc[:, 0]
-            for p in [9, 21, 50, 200]:
-                ema = ta.ema(df['close'], length=p)
-                df[f'EMA_{p}_dist'] = (df['close'] / ema) - 1
-            bb = ta.bbands(df['close'], length=20, std=2.0)
-            df['BB_w'] = (bb.iloc[:, 2] - bb.iloc[:, 0]) / bb.iloc[:, 1]
-            df['BB_p'] = (df['close'] - bb.iloc[:, 0]) / (bb.iloc[:, 2] - bb.iloc[:, 0])
-            df['MFI'] = ta.mfi(df['high'], df['low'], df['close'], df['volume'], length=14)
-            vwap = ta.vwap(df['high'], df['low'], df['close'], df['volume'])
-            df['VWAP_dist'] = (df['close'] / vwap) - 1
-
-        # --- المستوى 2: تكتيكي (1h, 4h) ---
-        elif tf in ['1h', '4h']:
-            df['RSI'] = ta.rsi(df['close'], length=14)
-            df['MACD_h'] = ta.macd(df['close']).iloc[:, 1]
-            df['EMA_50_dist'] = (df['close'] / ta.ema(df['close'], length=50)) - 1
-            df['EMA_200_dist'] = (df['close'] / ta.ema(df['close'], length=200)) - 1
-            df['ATR_pct'] = ta.atr(df['high'], df['low'], df['close'], length=14) / df['close']
-
-        # --- المستوى 3: استراتيجي (1d) ---
-        elif tf == '1d':
-            df['RSI'] = ta.rsi(df['close'], length=14)
-            df['EMA_200_dist'] = (df['close'] / ta.ema(df['close'], length=200)) - 1
-            adx = ta.adx(df['high'], df['low'], df['close'])
-            if adx is not None and not adx.empty:
-                 df['Trend_Strong'] = np.where(adx.iloc[:, 0] > 25, 1, 0)
-            else:
-                 df['Trend_Strong'] = 0
+        try:
+            # --- المستوى 1: دقيق (5m, 15m) ---
+            if tf in ['5m', '15m']:
+                df['RSI'] = ta.rsi(df['close'], length=14)
+                
+                macd = ta.macd(df['close'])
+                if macd is not None and not macd.empty:
+                    df['MACD'] = macd.iloc[:, 0]
+                    df['MACD_h'] = macd.iloc[:, 1]
+                else:
+                    df['MACD'] = np.nan
+                    df['MACD_h'] = np.nan
+
+                df['CCI'] = ta.cci(df['high'], df['low'], df['close'], length=20)
+                
+                adx = ta.adx(df['high'], df['low'], df['close'], length=14)
+                df['ADX'] = adx.iloc[:, 0] if adx is not None and not adx.empty else np.nan
+
+                for p in [9, 21, 50, 200]:
+                    ema = ta.ema(df['close'], length=p)
+                    # تحصين ضد القسمة على None
+                    if ema is not None and not ema.empty:
+                         df[f'EMA_{p}_dist'] = (df['close'] / ema) - 1
+                    else:
+                         df[f'EMA_{p}_dist'] = np.nan
+
+                bb = ta.bbands(df['close'], length=20, std=2.0)
+                if bb is not None and not bb.empty:
+                    df['BB_w'] = (bb.iloc[:, 2] - bb.iloc[:, 0]) / bb.iloc[:, 1]
+                    df['BB_p'] = (df['close'] - bb.iloc[:, 0]) / (bb.iloc[:, 2] - bb.iloc[:, 0])
+                else:
+                    df['BB_w'] = np.nan
+                    df['BB_p'] = np.nan
+
+                df['MFI'] = ta.mfi(df['high'], df['low'], df['close'], df['volume'], length=14)
+                
+                vwap = ta.vwap(df['high'], df['low'], df['close'], df['volume'])
+                if vwap is not None and not vwap.empty:
+                     df['VWAP_dist'] = (df['close'] / vwap) - 1
+                else:
+                     df['VWAP_dist'] = np.nan
+
+            # --- المستوى 2: تكتيكي (1h, 4h) ---
+            elif tf in ['1h', '4h']:
+                df['RSI'] = ta.rsi(df['close'], length=14)
+                
+                macd = ta.macd(df['close'])
+                df['MACD_h'] = macd.iloc[:, 1] if macd is not None and not macd.empty else np.nan
+
+                ema50 = ta.ema(df['close'], length=50)
+                df['EMA_50_dist'] = (df['close'] / ema50) - 1 if ema50 is not None and not ema50.empty else np.nan
+                
+                ema200 = ta.ema(df['close'], length=200)
+                df['EMA_200_dist'] = (df['close'] / ema200) - 1 if ema200 is not None and not ema200.empty else np.nan
+
+                atr = ta.atr(df['high'], df['low'], df['close'], length=14)
+                df['ATR_pct'] = (atr / df['close']) if atr is not None and not atr.empty else np.nan
+
+            # --- المستوى 3: استراتيجي (1d) ---
+            elif tf == '1d':
+                df['RSI'] = ta.rsi(df['close'], length=14)
+                
+                ema200 = ta.ema(df['close'], length=200)
+                df['EMA_200_dist'] = (df['close'] / ema200) - 1 if ema200 is not None and not ema200.empty else np.nan
+                
+                adx = ta.adx(df['high'], df['low'], df['close'])
+                if adx is not None and not adx.empty:
+                     df['Trend_Strong'] = np.where(adx.iloc[:, 0] > 25, 1, 0)
+                else:
+                     df['Trend_Strong'] = 0
+
+        except Exception as e:
+            print(f"⚠️ [Titan Warning] Error calculating indicators for {tf}: {e}")
+            # في حال حدوث خطأ، نترك الأعمدة كما هي (ستكون NaN إذا لم يتم إنشاؤها)
 
         return df.reset_index(drop=True)
 
@@ -98,12 +139,14 @@ class TitanEngine:
                 else:
                     df = data.copy()
                 
-                # تطبيق المؤشرات حسب الإطار
+                if df.empty: continue
+
+                # تطبيق المؤشرات حسب الإطار (النسخة المحصنة)
                 df = self.apply_inverted_pyramid(df, tf)
                 processed_tfs[tf] = df
 
             # 2. الدمج (Alignment) للحصول على آخر لقطة (Latest Snapshot)
-            if '5m' not in processed_tfs:
+            if '5m' not in processed_tfs or processed_tfs['5m'].empty:
                 return {'score': 0.0, 'error': 'Missing 5m base timeframe'}
 
             # نأخذ آخر صف فقط من الـ 5m كأساس
@@ -114,7 +157,7 @@ class TitanEngine:
 
             # دمج باقي الأطر (نأخذ آخر شمعة أغلقت قبل أو مع شمعة الـ 5m الحالية)
             for tf, df in processed_tfs.items():
-                if tf == '5m': continue
+                if tf == '5m' or df.empty: continue
                 # العثور على الشمعة المناسبة زمنياً
                 relevant_row = df[df['timestamp'] <= latest_ts].iloc[-1:].copy()
                 if relevant_row.empty: continue
@@ -136,7 +179,8 @@ class TitanEngine:
 
             # 4. التنبؤ
             # تحويل إلى DMatrix (تنسيق XGBoost السريع)
-            dtest = xgb.DMatrix([input_data], feature_names=self.feature_names)
+            # نستخدم np.nan القيم المفقودة ليقوم XGBoost بمعالجتها تلقائياً
+            dtest = xgb.DMatrix([input_data], feature_names=self.feature_names, missing=np.nan)
             prediction = self.model.predict(dtest)[0] # إرجاع الاحتمالية الأولى
 
             return {
@@ -147,6 +191,5 @@ class TitanEngine:
 
         except Exception as e:
             # print(f"⚠️ [Titan Error] {e}")
-            import traceback
             traceback.print_exc()
             return {'score': 0.0, 'error': str(e)}