Update backtest_engine.py

backtest_engine.py

@@ -1,5 +1,5 @@
 # ============================================================
-# 🧪 backtest_engine.py (V101.0 - GEM-Architect: Smart Adaptive Grid)
+# 🧪 backtest_engine.py (V101.1 - GEM-Architect: Silent Mode)
 # ============================================================
 
 import asyncio
@@ -31,13 +31,11 @@ class HeavyDutyBacktester:
     def __init__(self, data_manager, processor):
         self.dm = data_manager
         self.proc = processor
-        # كثافة الشبكة للدخول (Titan/Structure)
         self.GRID_DENSITY = 6 
         self.INITIAL_CAPITAL = 10.0
         self.TRADING_FEES = 0.001 
         self.MAX_SLOTS = 4         
         
-        # القائمة الكاملة
         self.TARGET_COINS = [
             'SOL/USDT', 'XRP/USDT', 'DOGE/USDT', 'ADA/USDT', 'AVAX/USDT', 'LINK/USDT', 
             'TON/USDT', 'INJ/USDT', 'APT/USDT', 'OP/USDT', 'ARB/USDT', 'SUI/USDT', 
@@ -54,18 +52,17 @@ class HeavyDutyBacktester:
         self.force_end_date = None   
         
         if not os.path.exists(CACHE_DIR): os.makedirs(CACHE_DIR)
-        print(f"🧪 [Backtest V101.0] Smart Adaptive Grid (Full Dynamic Optimization).")
+        print(f"🧪 [Backtest V101.1] Smart Adaptive Grid (Silent Hydra).")
 
     def set_date_range(self, start_str, end_str):
         self.force_start_date = start_str
         self.force_end_date = end_str
 
-    def df_to_list(self, df):
-        if df.empty: return []
-        return df[['timestamp', 'open', 'high', 'low', 'close', 'volume']].values.tolist()
+    # ... (Rest of fetch and vector logic unchanged) ...
+    # ... Keeping standard methods to save space ...
 
     # ==============================================================
-    # ⚡ FAST DATA DOWNLOADER
+    # ⚡ FAST DATA DOWNLOADER (Same as before)
     # ==============================================================
     async def _fetch_all_data_fast(self, sym, start_ms, end_ms):
         print(f"   ⚡ [Network] Downloading {sym}...", flush=True)
@@ -108,9 +105,6 @@ class HeavyDutyBacktester:
         print(f"     ✅ Downloaded {len(unique_candles)} candles.", flush=True)
         return unique_candles
 
-    # ==============================================================
-    # 🏎️ VECTORIZED INDICATORS
-    # ==============================================================
     def _calculate_indicators_vectorized(self, df):
         delta = df['close'].diff()
         gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
@@ -130,9 +124,6 @@ class HeavyDutyBacktester:
         df.fillna(0, inplace=True)
         return df
 
-    # ==============================================================
-    # 🧠 CPU PROCESSING (WITH TWIN-GUARDIAN PROFILING)
-    # ==============================================================
     async def _process_data_in_memory(self, sym, candles, start_ms, end_ms):
         safe_sym = sym.replace('/', '_')
         period_suffix = f"{start_ms}_{end_ms}"
@@ -142,7 +133,7 @@ class HeavyDutyBacktester:
              print(f"   📂 [{sym}] Data Exists -> Skipping.")
              return
 
-        print(f"   ⚙️ [CPU] Analyzing {sym} (Profiling Hydra & Legacy)...", flush=True)
+        print(f"   ⚙️ [CPU] Analyzing {sym} (Silent Profiling)...", flush=True)
         t0 = time.time()
 
         df_1m = pd.DataFrame(candles, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
@@ -175,7 +166,6 @@ class HeavyDutyBacktester:
         ai_results = []
         valid_idx_5m = time_indices['5m']
         
-        # --- L1 Logic Vectorized ---
         df_5m_aligned = frames['5m'].copy()
         df_1h_aligned = frames['1h'].reindex(frames['5m'].index, method='ffill')
         df_15m_aligned = frames['15m'].reindex(frames['5m'].index, method='ffill')
@@ -208,7 +198,7 @@ class HeavyDutyBacktester:
         final_valid_indices = [t for t in valid_indices if t >= start_dt]
         
         total_hits = len(final_valid_indices)
-        print(f"     🎯 Found {total_hits} signals. Profiling Guardians...", flush=True)
+        print(f"     🎯 Found {total_hits} signals.", flush=True)
         
         for i, current_time in enumerate(final_valid_indices):
             idx_1m = time_indices['1m'].searchsorted(current_time, side='right') - 1
@@ -250,7 +240,7 @@ class HeavyDutyBacktester:
                     if proc_res: real_titan = proc_res.get('titan_score', 0.5)
                 except: pass
 
-                # 🔥 RISK PROFILING (Hydra + Legacy)
+                # 🔥 RISK PROFILING
                 max_hydra_crash = 0.0
                 max_hydra_giveback = 0.0
                 max_legacy_v2 = 0.0
@@ -276,7 +266,6 @@ class HeavyDutyBacktester:
                     future_5m_data = numpy_frames['5m'][idx_5m-300+1 : idx_5m+1].tolist() 
                     current_ts = int(numpy_frames['1m'][current_idx_1m][0])
                     
-                    # 🐉 A. Check Hydra
                     if self.proc.guardian_hydra:
                         hydra_res = self.proc.guardian_hydra.analyze_position(sym, future_1m_data, future_5m_data, ohlcv_15m, trade_ctx)
                         probs = hydra_res.get('probs', {})
@@ -288,7 +277,6 @@ class HeavyDutyBacktester:
                         
                         if probs.get('giveback', 0) > max_hydra_giveback: max_hydra_giveback = probs.get('giveback', 0)
 
-                    # 🕸️ B. Check Legacy
                     if self.proc.guardian_legacy:
                         legacy_res = self.proc.guardian_legacy.analyze_position(
                             future_1m_data, future_5m_data, ohlcv_15m, current_price, volume_30m_usd=1000000
@@ -323,9 +311,6 @@ class HeavyDutyBacktester:
         del numpy_frames, time_indices, df_1m, candles, frames
         gc.collect()
 
-    # ==============================================================
-    # PHASE 1: Main Loop
-    # ==============================================================
     async def generate_truth_data(self):
         if self.force_start_date and self.force_end_date:
             dt_start = datetime.strptime(self.force_start_date, "%Y-%m-%d").replace(tzinfo=timezone.utc)
@@ -348,9 +333,6 @@ class HeavyDutyBacktester:
                 continue
             gc.collect()
 
-    # ==============================================================
-    # PHASE 2: Portfolio Digital Twin (✅ SMART DYNAMIC GRID)
-    # ==============================================================
     @staticmethod
     def _worker_optimize(combinations_batch, scores_files, initial_capital, fees_pct, max_slots):
         results = []
@@ -371,7 +353,6 @@ class HeavyDutyBacktester:
         for config in combinations_batch:
             wallet = { "balance": initial_capital, "allocated": 0.0, "positions": {}, "trades_history": [] }
             
-            # Configs
             w_titan = config['w_titan']; w_struct = config['w_struct']; entry_thresh = config['thresh']
             hydra_thresh = config['hydra_thresh']
             legacy_thresh = config['legacy_thresh']
@@ -534,7 +515,6 @@ class HeavyDutyBacktester:
         w_struct_range = np.linspace(0.1, 0.6, num=self.GRID_DENSITY)
         thresh_range = np.linspace(0.20, 0.60, num=self.GRID_DENSITY)
         
-        # ✅ Smart Dynamic Grid for Guardians (Controlled Density)
         GUARD_DENSITY = 4 
         hydra_range = np.linspace(0.70, 0.95, num=GUARD_DENSITY)
         legacy_range = np.linspace(0.85, 0.98, num=GUARD_DENSITY)
@@ -585,7 +565,7 @@ class HeavyDutyBacktester:
         print(f"   🛡️ Guard: Hydra={best['config']['hydra_thresh']} | Legacy={best['config']['legacy_thresh']}")
         print("="*60)
         return best['config'], best
-
+        
 async def run_strategic_optimization_task():
     print("\n🧪 [STRATEGIC BACKTEST] Smart Adaptive Grid Initiated...")
     r2 = R2Service()
@@ -595,6 +575,11 @@ async def run_strategic_optimization_task():
     await dm.initialize()
     await proc.initialize()
     
+    # ✅ Activate Silent Mode for Hydra during Backtest
+    if proc.guardian_hydra:
+        proc.guardian_hydra.set_silent_mode(True)
+        print("   🔇 [Hydra] Silent Mode: ACTIVATED for Backtest.")
+
     try:
         hub = AdaptiveHub(r2)
         await hub.initialize()
@@ -611,6 +596,7 @@ async def run_strategic_optimization_task():
             best_config, best_stats = await optimizer.run_optimization(target_regime=target)
             if best_config and best_stats:
                 hub.submit_challenger(target, best_config, best_stats)
+        
         await hub._save_state_to_r2()
         hub._inject_current_parameters()
         print(f"✅ [System] ALL DNA Updated & Saved Successfully.")