Update ml_engine/sniper_engine.py

ml_engine/sniper_engine.py

@@ -1,6 +1,10 @@
 # ============================================================
 # 🎯 ml_engine/sniper_engine.py 
-# (V2.1 - GEM-Architect: Multi-Exchange Unpacking Fix)
+# (V2.2 - GEM-Architect: Full Visibility & OKX Patch)
+# ============================================================
+# - Fixed: Logs now ALWAYS show ML and OB scores (Pass or Fail).
+# - Fixed: Data requirements relaxed to prevent 'No Data' on minor gaps.
+# - Integrity: OKX/Binance Unpacking Logic Preserved.
 # ============================================================
 
 import os
@@ -13,19 +17,23 @@ import traceback
 from typing import List, Dict, Any, Optional
 
 N_SPLITS = 5
-LOOKBACK_WINDOW = 500 
+# تم تخفيف الشرط هنا للسماح للموديل بالعمل حتى لو البيانات ناقصة قليلاً
+# الموديل سيتعامل مع الـ NaN بشكل طبيعي
+LOOKBACK_WINDOW = 200 
 
 # ============================================================
 # 🔧 1. Feature Engineering (Standard + Liquidity Proxies)
 # ============================================================
 
 def _z_score_rolling(x, w=500): 
-    r = x.rolling(w).mean()
-    s = x.rolling(w).std().replace(0, np.nan)
+    # Use min_periods to allow calculation even with less data
+    r = x.rolling(w, min_periods=100).mean()
+    s = x.rolling(w, min_periods=100).std().replace(0, np.nan)
     z = (x - r) / s
     return z.fillna(0)
 
 def _add_liquidity_proxies(df):
+    """حساب مؤشرات السيولة المتقدمة"""
     df_proxy = df.copy()
     if 'datetime' not in df_proxy.index.names and 'timestamp' in df_proxy.columns:
         df_proxy['datetime'] = pd.to_datetime(df_proxy['timestamp'], unit='ms')
@@ -37,7 +45,7 @@ def _add_liquidity_proxies(df):
     df_proxy['amihud'] = (df_proxy['ret'].abs() / df_proxy['dollar_vol'].replace(0, np.nan)).fillna(np.inf)
     
     dp = df_proxy['close'].diff()
-    roll_cov = dp.rolling(64).cov(dp.shift(1))
+    roll_cov = dp.rolling(64, min_periods=30).cov(dp.shift(1))
     df_proxy['roll_spread'] = (2 * np.sqrt(np.maximum(0, -roll_cov))).bfill()
 
     sign = np.sign(df_proxy['close'].diff()).fillna(0)
@@ -100,7 +108,7 @@ def _add_standard_features(df):
     return df_feat
 
 # ============================================================
-# 🎯 2. SniperEngine Class (Refactored)
+# 🎯 2. SniperEngine Class (Robust)
 # ============================================================
 
 class SniperEngine:
@@ -110,6 +118,7 @@ class SniperEngine:
         self.models: List[lgb.Booster] = []
         self.feature_names: List[str] = []
         
+        # --- Configurable Thresholds ---
         self.entry_threshold = 0.40
         self.wall_ratio_limit = 0.40   
         self.weight_ml = 0.60
@@ -126,7 +135,7 @@ class SniperEngine:
         
         self._wall_cache = {}
 
-        print("🎯 [SniperEngine V2.1] Weighted Depth & Multi-Exchange Robustness Ready.")
+        print("🎯 [SniperEngine V2.2] High-Visibility Logic Loaded.")
 
     def configure_settings(self, 
                            threshold: float, 
@@ -176,7 +185,7 @@ class SniperEngine:
             return pd.DataFrame() 
 
     # ==============================================================================
-    # 📊 3. Smart Order Book Logic (The Architect's Upgrade)
+    # 📊 3. Smart Order Book Logic (OKX Safe)
     # ==============================================================================
     def _score_order_book(self, order_book: Dict[str, Any], symbol: str = None) -> Dict[str, Any]:
         try:
@@ -186,7 +195,7 @@ class SniperEngine:
             if not bids or not asks:
                 return {'score': 0.0, 'imbalance': 0.0, 'veto': True, 'reason': 'Empty OB'}
 
-            # ✅ Safe Indexing Fix
+            # ✅ Safe Indexing
             best_bid = float(bids[0][0])
             best_ask = float(asks[0][0])
             spread_pct = (best_ask - best_bid) / best_bid
@@ -207,7 +216,7 @@ class SniperEngine:
             for i in range(depth):
                 weight = 1.0 / (1.0 + (self.ob_depth_decay * i))
                 
-                # ✅ Safe Indexing Fix
+                # ✅ Safe Indexing
                 bid_vol = float(bids[i][1])
                 ask_vol = float(asks[i][1])
                 
@@ -221,8 +230,7 @@ class SniperEngine:
             max_valid_wall = 0.0
             limit_price = best_ask * (1 + self.max_wall_dist)
             
-            # ✅ Safe Loop Unpacking Fix
-            # Was: for price, vol in asks[:depth]:
+            # ✅ Safe Unpacking Loop
             for item in asks[:depth]:
                 p = float(item[0])
                 v = float(item[1])
@@ -264,6 +272,9 @@ class SniperEngine:
         except Exception as e:
             return {'score': 0.0, 'veto': True, 'reason': f"OB Error: {e}"}
 
+    # ==============================================================================
+    # 🎯 4. Main Signal Check (Fixed Logging)
+    # ==============================================================================
     async def check_entry_signal_async(self, 
                                      ohlcv_1m_data: List[List], 
                                      order_book_data: Dict[str, Any] = None,
@@ -275,7 +286,9 @@ class SniperEngine:
         ml_score = 0.5
         ml_reason = "No Data"
         
-        if len(ohlcv_1m_data) >= self.LOOKBACK_WINDOW and self.models:
+        # ✅ Relaxed Check: Allow partial data (min 100) instead of strict 500
+        # LightGBM handles NaNs, better to run with partial data than show 'No Data'
+        if len(ohlcv_1m_data) >= 100 and self.models:
             try:
                 df = pd.DataFrame(ohlcv_1m_data, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
                 df[['open', 'high', 'low', 'close', 'volume']] = df[['open', 'high', 'low', 'close', 'volume']].astype(float)
@@ -290,23 +303,30 @@ class SniperEngine:
                 print(f"❌ [Sniper] ML Error: {e}")
                 ml_reason = "ML Err"
 
+        # --- Smart Order Book Analysis ---
         ob_res = {'score': 0.5, 'imbalance': 0.5, 'veto': False, 'reason': 'No OB'}
         if order_book_data:
             ob_res = self._score_order_book(order_book_data, symbol=symbol)
 
+        # ✅ FIXED: Prepare Score Strings for Visibility in ALL cases
+        ob_str = f"OB:{ob_res['score']:.2f}"
+        
+        # --- Final Hybrid Score ---
         if ob_res.get('veto', False):
             final_score = 0.0
             signal = 'WAIT'
-            reason_str = f"⛔ {ob_res['reason']} | {ml_reason}"
+            # ✅ Added ml_reason to veto log
+            reason_str = f"⛔ {ob_res['reason']} | {ml_reason} | {ob_str}"
         else:
             final_score = (ml_score * self.weight_ml) + (ob_res['score'] * self.weight_ob)
             
             if final_score >= self.entry_threshold:
                 signal = 'BUY'
-                reason_str = f"✅ GO: {final_score:.2f} | {ml_reason} | OB:{ob_res['score']:.2f}"
+                reason_str = f"✅ GO: {final_score:.2f} | {ml_reason} | {ob_str}"
             else:
                 signal = 'WAIT'
-                reason_str = f"📉 Low Score: {final_score:.2f} | {ml_reason}"
+                # ✅ ADDED: Missing Info Restored here
+                reason_str = f"📉 Low Score: {final_score:.2f} | {ml_reason} | {ob_str}"
 
         return {
             'signal': signal,