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>
This commit is contained in:
Claude Sonnet 4.6
@@ -12,10 +12,13 @@ FEATURES:
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import sqlite3
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import pandas as pd
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import logging
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from datetime import datetime, timedelta
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from typing import Dict, Optional, Tuple
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import json
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logger = logging.getLogger(__name__)
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class DynamicThresholdOptimizer:
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"""
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@@ -58,10 +61,9 @@ class DynamicThresholdOptimizer:
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'overlap': 70
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}
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print(f"✅ Dynamic Threshold Optimizer initialized")
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print(f" Lookback: {lookback_trades} trades")
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print(f" Target Win Rate: {target_win_rate*100:.1f}%")
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print(f" Range: {min_threshold}% - {max_threshold}%")
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logger.info(f"Dynamic Threshold Optimizer initialized — "
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f"lookback={lookback_trades}, target_wr={target_win_rate*100:.0f}%, "
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f"range={min_threshold}%-{max_threshold}%")
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def get_recent_performance(self, session: Optional[str] = None) -> Dict:
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"""
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@@ -74,10 +76,7 @@ class DynamicThresholdOptimizer:
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Dict mit Performance-Metriken
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"""
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try:
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conn = sqlite3.connect(self.db_path)
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# Query für letzte N Trades
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query = f"""
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base_query = """
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SELECT
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confidence,
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session,
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@@ -86,14 +85,15 @@ class DynamicThresholdOptimizer:
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FROM trades
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WHERE status = 'closed'
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"""
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params: list = []
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if session:
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query += f" AND session = '{session}'"
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base_query += " AND session = ?"
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params.append(session)
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base_query += " ORDER BY exit_time DESC LIMIT ?"
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params.append(self.lookback_trades)
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query += f" ORDER BY exit_time DESC LIMIT {self.lookback_trades}"
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df = pd.read_sql_query(query, conn)
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conn.close()
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with sqlite3.connect(self.db_path) as conn:
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df = pd.read_sql_query(base_query, conn, params=params)
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if df.empty:
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return {
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@@ -105,10 +105,10 @@ class DynamicThresholdOptimizer:
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}
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trades = len(df)
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wins = df['win'].sum()
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wins = int(df['win'].sum())
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win_rate = wins / trades if trades > 0 else 0.0
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avg_confidence = df['confidence'].mean()
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total_profit = df['net_profit'].sum()
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avg_confidence = float(df['confidence'].mean())
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total_profit = float(df['net_profit'].sum())
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return {
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'trades': trades,
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@@ -121,7 +121,7 @@ class DynamicThresholdOptimizer:
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}
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except Exception as e:
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print(f"❌ Error getting performance: {e}")
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logger.error(f"Error getting performance: {e}")
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return {
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'trades': 0,
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'win_rate': 0.0,
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@@ -322,10 +322,12 @@ class DynamicThresholdOptimizer:
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}
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}
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with open(config_file, 'w') as f:
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json.dump(config, f, indent=2)
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print(f"✅ Thresholds saved to: {config_file}")
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try:
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with open(config_file, 'w') as f:
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json.dump(config, f, indent=2)
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logger.info(f"Thresholds saved to: {config_file}")
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except Exception as e:
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logger.error(f"Failed to save thresholds: {e}")
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# ==========================================
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@@ -344,9 +346,9 @@ def auto_optimize_thresholds(optimizer: DynamicThresholdOptimizer,
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Returns:
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Optimization Results
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"""
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print(f"\n{'='*70}")
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print(f"🔄 AUTO-OPTIMIZATION STARTED - {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
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print(f"{'='*70}\n")
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logger.info(f"\n{'='*70}")
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logger.info(f"🔄 AUTO-OPTIMIZATION STARTED - {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
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logger.info(f"{'='*70}\n")
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results = optimizer.optimize_all_sessions()
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@@ -357,16 +359,16 @@ def auto_optimize_thresholds(optimizer: DynamicThresholdOptimizer,
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change_emoji = "🔽" if info['change'] < 0 else ("🔼" if info['change'] > 0 else "➡️")
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print(f"{session.upper():8s}: {info['old_threshold']}% → {info['new_threshold']}% "
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logger.info(f"{session.upper():8s}: {info['old_threshold']}% → {info['new_threshold']}% "
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f"{change_emoji} | WR: {info['win_rate']*100:.1f}% ({info['recent_trades']} trades)")
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if apply_changes:
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optimizer.save_thresholds_to_config()
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print("\n✅ Changes applied and saved!")
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logger.info("\n✅ Changes applied and saved!")
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else:
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print("\n⚠️ Dry-run mode - changes NOT applied")
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logger.info("\n⚠️ Dry-run mode - changes NOT applied")
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print(f"\n{'='*70}\n")
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logger.info(f"\n{'='*70}\n")
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return results
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@@ -434,4 +436,4 @@ print("✅ Auto-optimization scheduled (daily at midnight)")
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if __name__ == "__main__":
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# Test
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optimizer = DynamicThresholdOptimizer()
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print(optimizer.generate_report())
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logger.info(optimizer.generate_report())
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+18
-15
@@ -11,8 +11,9 @@ IMPROVEMENTS:
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5. Multi-tier Profit Locking
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"""
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import MetaTrader5 as mt
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from datetime import datetime, timedelta
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import MetaTrader5 as mt5
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import pandas as pd
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from datetime import datetime, timedelta, timezone
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from typing import Tuple, Optional, Dict
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import logging
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@@ -132,15 +133,18 @@ class EnhancedTrailingStopManager:
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entry_price = position.price_open
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current_sl = position.sl
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tp = position.tp
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entry_time = datetime.fromtimestamp(position.time)
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entry_time = datetime.fromtimestamp(position.time, tz=timezone.utc).replace(tzinfo=None)
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# Current Price
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symbol_info = mt.symbol_info_tick(position.symbol)
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if not symbol_info:
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# Current Price — fetch tick and symbol info once each
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tick = mt5.symbol_info_tick(position.symbol)
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if not tick:
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return False, None, "No symbol info"
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current_price = symbol_info.bid if position_type == 0 else symbol_info.ask
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point = mt.symbol_info(position.symbol).point
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current_price = tick.bid if position_type == 0 else tick.ask
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sym_info = mt5.symbol_info(position.symbol)
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if not sym_info:
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return False, None, "No symbol info"
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point = sym_info.point
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# Calculate progress
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if position_type == 0: # BUY
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@@ -318,7 +322,7 @@ class EnhancedTrailingStopManager:
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"""
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try:
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request = {
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"action": mt.TRADE_ACTION_SLTP,
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"action": mt5.TRADE_ACTION_SLTP,
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"position": position.ticket,
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"symbol": position.symbol,
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"sl": new_sl,
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@@ -327,9 +331,9 @@ class EnhancedTrailingStopManager:
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"comment": "Enhanced Trailing"
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}
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result = mt.order_send(request)
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result = mt5.order_send(request)
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if result.retcode == mt.TRADE_RETCODE_DONE:
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if result.retcode == mt5.TRADE_RETCODE_DONE:
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logger.info(f"✅ Enhanced Trailing Stop updated for #{position.ticket}")
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logger.info(f" Old SL: {position.sl:.5f}")
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logger.info(f" New SL: {new_sl:.5f}")
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@@ -345,7 +349,7 @@ class EnhancedTrailingStopManager:
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def cleanup_closed_positions(self):
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"""Entfernt geschlossene Positions aus Tier-Tracking"""
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open_tickets = {pos.ticket for pos in mt.positions_get()}
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open_tickets = {pos.ticket for pos in (mt5.positions_get() or [])}
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closed_tickets = set(self.position_tiers.keys()) - open_tickets
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for ticket in closed_tickets:
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@@ -387,7 +391,7 @@ def create_enhanced_position_monitor(
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- Time-based Breakeven
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"""
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try:
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positions = mt.positions_get(symbol=symbol)
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positions = mt5.positions_get(symbol=symbol)
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if not positions:
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return
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@@ -398,9 +402,8 @@ def create_enhanced_position_monitor(
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# Get current ATR
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atr_value = None
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try:
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rates = mt.copy_rates_from_pos(symbol, mt.TIMEFRAME_M5, 0, 20)
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rates = mt5.copy_rates_from_pos(symbol, mt5.TIMEFRAME_M5, 0, 20)
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if rates is not None:
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import pandas as pd
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df = pd.DataFrame(rates)
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df['tr'] = df[['high', 'low', 'close']].apply(
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lambda x: max(x['high'] - x['low'],
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+1
-1
@@ -84,7 +84,7 @@ class SignalCache:
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age_hours = (now - timestamp).total_seconds() / 3600
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if age_hours > MAX_CACHE_AGE_HOURS:
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to_remove.append(ticket)
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except:
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except Exception:
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to_remove.append(ticket)
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for ticket in to_remove:
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