Update ml_engine/processor.py

ml_engine/processor.py

@@ -1,4 +1,4 @@
-# ml_engine/processor.py (Updated to pass LearningHubManager)
+# ml_engine/processor.py (Updated to V6.2 - Normalized MC Score)
 import pandas as pd
 import numpy as np
 from datetime import datetime
@@ -13,7 +13,6 @@ from .patterns import ChartPatternAnalyzer
 from .strategies import MultiStrategyEngine
 
 class MLProcessor:
-    # 🔴 --- START OF CHANGE --- 🔴
     def __init__(self, market_context, data_manager, learning_hub): # (Changed from learning_engine)
         self.market_context = market_context
         self.data_manager = data_manager
@@ -25,7 +24,6 @@ class MLProcessor:
         self.monte_carlo_analyzer = MonteCarloAnalyzer()
         self.pattern_analyzer = ChartPatternAnalyzer()
         self.whale_data_semaphore = asyncio.Semaphore(2) 
-    # 🔴 --- END OF CHANGE --- 🔴
 
     async def process_and_score_symbol_enhanced(self, raw_data, preloaded_whale_data: dict = None):
         """
@@ -114,8 +112,9 @@ class MLProcessor:
             print(f"❌ Error creating DataFrame: {e}")
             return pd.DataFrame()
 
+    # 🔴 --- START OF CHANGE (V6.2 - NORMALIZE MC SCORE) --- 🔴
     def _calculate_enhanced_final_score(self, analysis):
-        # (This function remains unchanged)
+        """(محدث V6.2) تطبيع درجة مونت كارلو لمعاقبة النتائج العشوائية."""
         try:
             base_score = analysis.get('final_score', 0)
             pattern_confidence = analysis.get('pattern_analysis', {}).get('pattern_confidence', 0)
@@ -133,8 +132,12 @@ class MLProcessor:
                     normalized_var = var_95_value / current_price
                     risk_penalty = 1.0
                     if normalized_var > 0.05: risk_penalty = 0.5           
-                    elif normalized_var > 0.03: risk_penalty = 0.8           
-                    monte_carlo_score = prob_gain * risk_penalty
+                    elif normalized_var > 0.03: risk_penalty = 0.8
+                    
+                    # (تعديل: تطبيع الاحتمالية)
+                    # (0.5 -> 0), (0.75 -> 0.5), (1.0 -> 1.0)
+                    normalized_prob_score = max(0.0, (prob_gain - 0.5) * 2)
+                    monte_carlo_score = normalized_prob_score * risk_penalty
                 else:
                     monte_carlo_score = 0 
             
@@ -163,6 +166,7 @@ class MLProcessor:
         except Exception as e:
             print(f"❌ Error calculating enhanced score: {e}")
             return analysis.get('final_score', 0)
+    # 🔴 --- END OF CHANGE --- 🔴
 
     async def process_and_score_symbol(self, raw_data):
         # (This function remains unchanged)