fix: enhanced_trailing_stop, dynamic_threshold_optimizer, signal_cache

enhanced_trailing_stop.py:
- mt -> mt5 (all occurrences)
- Add pandas + timezone imports at file top
- Fix UTC bug: datetime.fromtimestamp(..., tz=timezone.utc).replace(tzinfo=None)
- Fetch symbol_info once per call, reuse for point (was called twice)
- cleanup_closed_positions: handle None from positions_get()
- Remove pandas import from inside function body

dynamic_threshold_optimizer.py:
- Fix SQL injection: replace f-string session filter with parameterized query (?)
- Add logging module, replace all print() with logger calls
- Use context manager (with sqlite3.connect()) to prevent connection leak on exception
- save_thresholds_to_config: add try/except with logger.error

signal_cache.py:
- Fix bare except -> except Exception in _cleanup_old_entries

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

dynamic_threshold_optimizer.py

@@ -12,10 +12,13 @@ FEATURES:
 
 import sqlite3
 import pandas as pd
+import logging
 from datetime import datetime, timedelta
 from typing import Dict, Optional, Tuple
 import json
 
+logger = logging.getLogger(__name__)
+
 
 class DynamicThresholdOptimizer:
     """
@@ -58,10 +61,9 @@ class DynamicThresholdOptimizer:
             'overlap': 70
         }
 
-        print(f"✅ Dynamic Threshold Optimizer initialized")
+        logger.info(f"Dynamic Threshold Optimizer initialized — "
-        print(f"   Lookback: {lookback_trades} trades")
+                    f"lookback={lookback_trades}, target_wr={target_win_rate*100:.0f}%, "
-        print(f"   Target Win Rate: {target_win_rate*100:.1f}%")
+                    f"range={min_threshold}%-{max_threshold}%")
-        print(f"   Range: {min_threshold}% - {max_threshold}%")
 
     def get_recent_performance(self, session: Optional[str] = None) -> Dict:
         """
@@ -74,10 +76,7 @@ class DynamicThresholdOptimizer:
             Dict mit Performance-Metriken
         """
         try:
-            conn = sqlite3.connect(self.db_path)
+            base_query = """
-
-            # Query für letzte N Trades
-            query = f"""
                 SELECT
                     confidence,
                     session,
@@ -86,14 +85,15 @@ class DynamicThresholdOptimizer:
                 FROM trades
                 WHERE status = 'closed'
             """
-
+            params: list = []
             if session:
-                query += f" AND session = '{session}'"
+                base_query += " AND session = ?"
+                params.append(session)
+            base_query += " ORDER BY exit_time DESC LIMIT ?"
+            params.append(self.lookback_trades)
 
-            query += f" ORDER BY exit_time DESC LIMIT {self.lookback_trades}"
+            with sqlite3.connect(self.db_path) as conn:
-
+                df = pd.read_sql_query(base_query, conn, params=params)
-            df = pd.read_sql_query(query, conn)
-            conn.close()
 
             if df.empty:
                 return {
@@ -105,10 +105,10 @@ class DynamicThresholdOptimizer:
                 }
 
             trades = len(df)
-            wins = df['win'].sum()
+            wins = int(df['win'].sum())
             win_rate = wins / trades if trades > 0 else 0.0
-            avg_confidence = df['confidence'].mean()
+            avg_confidence = float(df['confidence'].mean())
-            total_profit = df['net_profit'].sum()
+            total_profit = float(df['net_profit'].sum())
 
             return {
                 'trades': trades,
@@ -121,7 +121,7 @@ class DynamicThresholdOptimizer:
             }
 
         except Exception as e:
-            print(f"❌ Error getting performance: {e}")
+            logger.error(f"Error getting performance: {e}")
             return {
                 'trades': 0,
                 'win_rate': 0.0,
@@ -322,10 +322,12 @@ class DynamicThresholdOptimizer:
             }
         }
 
-        with open(config_file, 'w') as f:
+        try:
-            json.dump(config, f, indent=2)
+            with open(config_file, 'w') as f:
-
+                json.dump(config, f, indent=2)
-        print(f"✅ Thresholds saved to: {config_file}")
+            logger.info(f"Thresholds saved to: {config_file}")
+        except Exception as e:
+            logger.error(f"Failed to save thresholds: {e}")
 
 
 # ==========================================
@@ -344,9 +346,9 @@ def auto_optimize_thresholds(optimizer: DynamicThresholdOptimizer,
     Returns:
         Optimization Results
     """
-    print(f"\n{'='*70}")
+    logger.info(f"\n{'='*70}")
-    print(f"🔄 AUTO-OPTIMIZATION STARTED - {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+    logger.info(f"🔄 AUTO-OPTIMIZATION STARTED - {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
-    print(f"{'='*70}\n")
+    logger.info(f"{'='*70}\n")
 
     results = optimizer.optimize_all_sessions()
 
@@ -357,16 +359,16 @@ def auto_optimize_thresholds(optimizer: DynamicThresholdOptimizer,
 
         change_emoji = "🔽" if info['change'] < 0 else ("🔼" if info['change'] > 0 else "➡️")
 
-        print(f"{session.upper():8s}: {info['old_threshold']}% → {info['new_threshold']}% "
+        logger.info(f"{session.upper():8s}: {info['old_threshold']}% → {info['new_threshold']}% "
               f"{change_emoji} | WR: {info['win_rate']*100:.1f}% ({info['recent_trades']} trades)")
 
     if apply_changes:
         optimizer.save_thresholds_to_config()
-        print("\n✅ Changes applied and saved!")
+        logger.info("\n✅ Changes applied and saved!")
     else:
-        print("\n⚠️ Dry-run mode - changes NOT applied")
+        logger.info("\n⚠️ Dry-run mode - changes NOT applied")
 
-    print(f"\n{'='*70}\n")
+    logger.info(f"\n{'='*70}\n")
 
     return results
 
@@ -434,4 +436,4 @@ print("✅ Auto-optimization scheduled (daily at midnight)")
 if __name__ == "__main__":
     # Test
     optimizer = DynamicThresholdOptimizer()
-    print(optimizer.generate_report())
+    logger.info(optimizer.generate_report())

enhanced_trailing_stop.py

@@ -11,8 +11,9 @@ IMPROVEMENTS:
 5. Multi-tier Profit Locking
 """
 
-import MetaTrader5 as mt
+import MetaTrader5 as mt5
-from datetime import datetime, timedelta
+import pandas as pd
+from datetime import datetime, timedelta, timezone
 from typing import Tuple, Optional, Dict
 import logging
 
@@ -132,15 +133,18 @@ class EnhancedTrailingStopManager:
             entry_price = position.price_open
             current_sl = position.sl
             tp = position.tp
-            entry_time = datetime.fromtimestamp(position.time)
+            entry_time = datetime.fromtimestamp(position.time, tz=timezone.utc).replace(tzinfo=None)
 
-            # Current Price
+            # Current Price — fetch tick and symbol info once each
-            symbol_info = mt.symbol_info_tick(position.symbol)
+            tick = mt5.symbol_info_tick(position.symbol)
-            if not symbol_info:
+            if not tick:
                 return False, None, "No symbol info"
 
-            current_price = symbol_info.bid if position_type == 0 else symbol_info.ask
+            current_price = tick.bid if position_type == 0 else tick.ask
-            point = mt.symbol_info(position.symbol).point
+            sym_info = mt5.symbol_info(position.symbol)
+            if not sym_info:
+                return False, None, "No symbol info"
+            point = sym_info.point
 
             # Calculate progress
             if position_type == 0:  # BUY
@@ -318,7 +322,7 @@ class EnhancedTrailingStopManager:
         """
         try:
             request = {
-                "action": mt.TRADE_ACTION_SLTP,
+                "action": mt5.TRADE_ACTION_SLTP,
                 "position": position.ticket,
                 "symbol": position.symbol,
                 "sl": new_sl,
@@ -327,9 +331,9 @@ class EnhancedTrailingStopManager:
                 "comment": "Enhanced Trailing"
             }
 
-            result = mt.order_send(request)
+            result = mt5.order_send(request)
 
-            if result.retcode == mt.TRADE_RETCODE_DONE:
+            if result.retcode == mt5.TRADE_RETCODE_DONE:
                 logger.info(f"✅ Enhanced Trailing Stop updated for #{position.ticket}")
                 logger.info(f"   Old SL: {position.sl:.5f}")
                 logger.info(f"   New SL: {new_sl:.5f}")
@@ -345,7 +349,7 @@ class EnhancedTrailingStopManager:
 
     def cleanup_closed_positions(self):
         """Entfernt geschlossene Positions aus Tier-Tracking"""
-        open_tickets = {pos.ticket for pos in mt.positions_get()}
+        open_tickets = {pos.ticket for pos in (mt5.positions_get() or [])}
         closed_tickets = set(self.position_tiers.keys()) - open_tickets
 
         for ticket in closed_tickets:
@@ -387,7 +391,7 @@ def create_enhanced_position_monitor(
         - Time-based Breakeven
         """
         try:
-            positions = mt.positions_get(symbol=symbol)
+            positions = mt5.positions_get(symbol=symbol)
 
             if not positions:
                 return
@@ -398,9 +402,8 @@ def create_enhanced_position_monitor(
             # Get current ATR
             atr_value = None
             try:
-                rates = mt.copy_rates_from_pos(symbol, mt.TIMEFRAME_M5, 0, 20)
+                rates = mt5.copy_rates_from_pos(symbol, mt5.TIMEFRAME_M5, 0, 20)
                 if rates is not None:
-                    import pandas as pd
                     df = pd.DataFrame(rates)
                     df['tr'] = df[['high', 'low', 'close']].apply(
                         lambda x: max(x['high'] - x['low'],

signal_cache.py

@@ -84,7 +84,7 @@ class SignalCache:
                 age_hours = (now - timestamp).total_seconds() / 3600
                 if age_hours > MAX_CACHE_AGE_HOURS:
                     to_remove.append(ticket)
-            except:
+            except Exception:
                 to_remove.append(ticket)
 
         for ticket in to_remove: