feat: Add Signal Cache for ML training data collection
- Add signal_cache.py module to persist signal data between trade open/close - Modify execute_trade_v2_adaptive to cache signal info when trade opens - Update sync_closed_trades_to_tracker to retrieve cached ML features - Update scheduled_demo_tracker_sync with same ML feature retrieval This enables proper ML training by capturing: - base_confidence, enhanced_score, hybrid_score - signal_quality, market_regime, regime_strength - session and lot_multiplier Previously all trades were logged with 0 values for ML features. After ~50-100 new trades, the ML model can be properly trained. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Claude Opus 4.5
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#!/usr/bin/env python3
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"""
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SIGNAL CACHE - Speichert Signal-Daten für ML Training
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Wenn ein Trade geöffnet wird, werden die Signal-Informationen hier gespeichert.
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Wenn der Trade geschlossen wird, können die Daten abgerufen und zum demo_tracker geloggt werden.
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VERWENDUNG:
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from signal_cache import signal_cache
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# Beim Trade-Open:
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signal_cache.store(ticket_id, signal_info, enhanced_score, hybrid_score)
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# Beim Trade-Close:
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cached = signal_cache.get(ticket_id)
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if cached:
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demo_tracker.log_trade(...,
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base_confidence=cached['base_confidence'],
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enhanced_score=cached['enhanced_score'],
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hybrid_score=cached['hybrid_score'],
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signal_quality=cached['signal_quality']
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)
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"""
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import json
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import os
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import logging
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from datetime import datetime
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from typing import Dict, Optional, Any
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logger = logging.getLogger(__name__)
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# ==========================================
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# CONFIGURATION
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# ==========================================
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CACHE_FILE = "signal_cache.json"
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MAX_CACHE_AGE_HOURS = 48 # Alte Einträge nach 48h löschen
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# ==========================================
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# SIGNAL CACHE CLASS
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# ==========================================
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class SignalCache:
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"""
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Cache für Trade-Signal-Daten
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Persistiert auf Disk für Restart-Sicherheit
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"""
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def __init__(self, cache_file: str = CACHE_FILE):
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self.cache_file = cache_file
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self.cache: Dict[str, Dict] = {}
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self._load_cache()
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logger.info(f"SignalCache initialized with {len(self.cache)} entries")
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def _load_cache(self):
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"""Lädt Cache von Disk"""
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if os.path.exists(self.cache_file):
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try:
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with open(self.cache_file, 'r') as f:
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self.cache = json.load(f)
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self._cleanup_old_entries()
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except Exception as e:
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logger.warning(f"Could not load signal cache: {e}")
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self.cache = {}
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def _save_cache(self):
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"""Speichert Cache auf Disk"""
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try:
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with open(self.cache_file, 'w') as f:
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json.dump(self.cache, f, indent=2, default=str)
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except Exception as e:
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logger.error(f"Could not save signal cache: {e}")
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def _cleanup_old_entries(self):
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"""Entfernt Einträge älter als MAX_CACHE_AGE_HOURS"""
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now = datetime.now()
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to_remove = []
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for ticket, data in self.cache.items():
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try:
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timestamp = datetime.fromisoformat(data.get('timestamp', ''))
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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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to_remove.append(ticket)
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for ticket in to_remove:
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del self.cache[ticket]
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if to_remove:
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logger.info(f"Cleaned up {len(to_remove)} old cache entries")
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self._save_cache()
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def store(self,
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ticket: int,
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signal_info: Dict[str, Any],
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enhanced_score: float = 0.0,
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hybrid_score: float = 0.0,
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lot_multiplier: float = 1.0) -> bool:
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"""
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Speichert Signal-Daten für einen Trade
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Args:
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ticket: MT5 Order Ticket ID
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signal_info: Dictionary mit Signal-Analyse Daten
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enhanced_score: Enhanced Signal Score (0-100)
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hybrid_score: Hybrid Score (0-100)
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lot_multiplier: Equity Curve Lot Multiplier
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Returns:
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True wenn erfolgreich gespeichert
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"""
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try:
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# Extrahiere relevante Daten aus signal_info
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cache_entry = {
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'ticket': ticket,
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'timestamp': datetime.now().isoformat(),
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# Signal Analysis
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'base_confidence': signal_info.get('confidence', 0),
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'adaptive_threshold': signal_info.get('adaptive_threshold', 0),
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'signal_quality': signal_info.get('signal_quality', 'unknown'),
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'entry_signal': signal_info.get('entry_signal', 0),
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# Market Regime
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'market_regime': signal_info.get('market_regime', 'unknown'),
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'regime_strength': signal_info.get('regime_strength', 0),
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'risk_adjusted_strength': signal_info.get('risk_adjusted_strength', 0),
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# Enhanced Scoring
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'enhanced_score': enhanced_score,
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'hybrid_score': hybrid_score,
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# Equity Curve
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'lot_multiplier': lot_multiplier,
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# Session
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'session': signal_info.get('session', 'unknown'),
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}
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self.cache[str(ticket)] = cache_entry
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self._save_cache()
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logger.info(f"Cached signal for ticket {ticket}: conf={cache_entry['base_confidence']:.1f}%, quality={cache_entry['signal_quality']}")
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return True
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except Exception as e:
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logger.error(f"Failed to cache signal for ticket {ticket}: {e}")
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return False
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def get(self, ticket: int) -> Optional[Dict]:
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"""
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Holt gecachte Signal-Daten für einen Trade
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Args:
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ticket: MT5 Order Ticket ID
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Returns:
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Dict mit Signal-Daten oder None
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"""
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return self.cache.get(str(ticket))
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def remove(self, ticket: int) -> bool:
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"""
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Entfernt einen Eintrag nach Verwendung
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Args:
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ticket: MT5 Order Ticket ID
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Returns:
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True wenn erfolgreich entfernt
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"""
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ticket_str = str(ticket)
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if ticket_str in self.cache:
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del self.cache[ticket_str]
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self._save_cache()
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return True
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return False
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def get_and_remove(self, ticket: int) -> Optional[Dict]:
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"""
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Holt und entfernt Signal-Daten (für Trade-Close)
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Args:
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ticket: MT5 Order Ticket ID
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Returns:
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Dict mit Signal-Daten oder None
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"""
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data = self.get(ticket)
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if data:
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self.remove(ticket)
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return data
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def get_stats(self) -> Dict:
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"""Gibt Cache-Statistiken zurück"""
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return {
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'total_entries': len(self.cache),
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'cache_file': self.cache_file,
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'oldest_entry': min(
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(datetime.fromisoformat(d.get('timestamp', datetime.now().isoformat()))
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for d in self.cache.values()),
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default=None
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),
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'tickets': list(self.cache.keys())
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}
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def print_status(self):
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"""Zeigt Cache-Status an"""
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stats = self.get_stats()
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print("\n" + "=" * 50)
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print("SIGNAL CACHE STATUS")
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print("=" * 50)
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print(f" Cached Trades: {stats['total_entries']}")
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print(f" Cache File: {stats['cache_file']}")
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if stats['tickets']:
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print(f" Tickets: {', '.join(stats['tickets'][:5])}")
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if len(stats['tickets']) > 5:
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print(f" ... and {len(stats['tickets']) - 5} more")
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print("=" * 50)
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# ==========================================
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# GLOBAL INSTANCE
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# ==========================================
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signal_cache = SignalCache()
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# ==========================================
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# HELPER FUNCTIONS
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# ==========================================
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def store_trade_signal(ticket: int,
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signal_info: Dict,
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enhanced_score: float = 0.0,
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hybrid_score: float = 0.0,
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lot_multiplier: float = 1.0) -> bool:
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"""Convenience function to store signal data"""
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return signal_cache.store(ticket, signal_info, enhanced_score, hybrid_score, lot_multiplier)
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def get_trade_signal(ticket: int) -> Optional[Dict]:
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"""Convenience function to get and remove signal data"""
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return signal_cache.get_and_remove(ticket)
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def get_cache_status() -> str:
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"""Returns formatted cache status"""
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stats = signal_cache.get_stats()
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return f"SignalCache: {stats['total_entries']} entries cached"
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# ==========================================
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# STANDALONE TEST
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# ==========================================
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if __name__ == "__main__":
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print("Testing Signal Cache...")
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# Test data
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test_signal = {
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'confidence': 85.5,
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'adaptive_threshold': 70,
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'signal_quality': 'excellent',
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'entry_signal': 1,
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'market_regime': 'trending',
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'regime_strength': 65.0,
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'risk_adjusted_strength': 12500.0,
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'session': 'asian'
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}
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# Store
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signal_cache.store(12345, test_signal, enhanced_score=78.5, hybrid_score=82.0)
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# Retrieve
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cached = signal_cache.get(12345)
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print(f"\nCached data: {json.dumps(cached, indent=2)}")
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# Status
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signal_cache.print_status()
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# Cleanup
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signal_cache.remove(12345)
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print("\nTest completed!")
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