cbazza
7e978cf7c4
Fixed AttributeError caused by wrong attribute access: - Changed component_scores['trend'] → trend_score - Changed component_scores['volume'] → volume_score - Changed component_scores['momentum'] → momentum_score - Changed component_scores['support_resistance'] → support_resistance_score - Changed component_scores['fibonacci'] → fibonacci_score - Changed reasoning → reason Files fixed: - activate_enhanced_scoring.py - Notebook cells 84, 87 regenerated Error resolved: AttributeError: 'EnhancedSignal' object has no attribute 'component_scores'
338 lines
12 KiB
Python
338 lines
12 KiB
Python
#!/usr/bin/env python3
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"""
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Script to activate Enhanced Signal Scoring in Trading Logic
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Adds a new cell that wraps the trading check with enhanced scoring
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"""
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import nbformat
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from pathlib import Path
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import sys
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def activate_enhanced_scoring(notebook_path):
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"""Add enhanced scoring activation cell to notebook"""
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# Read notebook
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with open(notebook_path, 'r', encoding='utf-8') as f:
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nb = nbformat.read(f, as_version=4)
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print(f"📖 Loaded notebook: {Path(notebook_path).name}")
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print(f" Current cells: {len(nb.cells)}")
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# Define new cells
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new_cells = []
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# ==========================================
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# Cell 1: Markdown Header
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# ==========================================
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new_cells.append(nbformat.v4.new_markdown_cell("""# 🎯 ENHANCED SIGNAL SCORING ACTIVATION (V1.10)
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**Aktiviert Multi-Faktor-Analyse für Trading Signals**
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Erweitert das Trend-System um:
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- 📊 **Volume Analysis** (20%) - Hohes Volume = stärkerer Move
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- 📈 **Momentum Indicators** (20%) - RSI + MACD Confirmation
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- 🎯 **Support/Resistance** (15%) - Nähe zu Key Levels
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- 📐 **Fibonacci Levels** (15%) - Bounce-Zones
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- 📉 **Trend Alignment** (30%) - Bestehendes System
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**Status:** ✅ READY TO ACTIVATE
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"""))
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# ==========================================
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# Cell 2: Enhanced Trading Check Wrapper
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# ==========================================
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new_cells.append(nbformat.v4.new_code_cell("""# ==========================================
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# ENHANCED TRADING CHECK WITH SIGNAL SCORING
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# ==========================================
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def enhanced_trading_check_wrapper(symbol="XAUUSD", debug=False):
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\"\"\"
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Enhanced wrapper around execute_trade_v2_adaptive
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Adds multi-factor signal scoring before execution
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\"\"\"
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try:
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# SCHRITT 1: Position Check (wie vorher)
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max_positions = TRADING_CONFIG['risk']['max_positions']
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position_info = check_existing_position(symbol)
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if position_info['count'] >= max_positions:
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if debug:
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print(f"🛑 TRADE BLOCKIERT: {position_info['count']}/{max_positions} Positionen aktiv")
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for pos in position_info['details']:
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profit_emoji = "🟢" if pos['profit'] >= 0 else "🔴"
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print(f" {pos['type']} @ {pos['price_open']} | {profit_emoji} {pos['profit']:.2f}")
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return None
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print(f"✅ Position-Check OK: {position_info['count']}/{max_positions}")
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# SCHRITT 2: Signal Analysis (wie vorher)
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signal_info = extended_top_down_v2_adaptive(symbol)
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if signal_info is None:
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print("❌ Signal-Analyse fehlgeschlagen")
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return None
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entry_signal = signal_info["entry_signal"]
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base_confidence = signal_info["confidence"]
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adaptive_threshold = signal_info["adaptive_threshold"]
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print(f"\\n📊 Base Signal Analysis:")
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print(f" Direction: {entry_signal}")
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print(f" Base Confidence: {base_confidence:.1f}%")
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print(f" Adaptive Threshold: {adaptive_threshold:.1f}%")
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# ⭐ SCHRITT 3: ENHANCED SIGNAL SCORING (NEU!)
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print(f"\\n🎯 Calculating Enhanced Signal Score...")
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try:
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enhanced = signal_scorer.calculate_enhanced_score(
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symbol=symbol,
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base_confidence=base_confidence,
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trend_direction=entry_signal,
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current_price=signal_info['trend_info']['M5']['price']
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)
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# Verwende enhanced score statt base confidence
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final_confidence = enhanced.total_score
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print(f"\\n✅ Enhanced Signal Scoring:")
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print(f" Trend Score: {enhanced.trend_score:.1f}/100")
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print(f" Volume Score: {enhanced.volume_score:.1f}/100")
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print(f" Momentum Score: {enhanced.momentum_score:.1f}/100")
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print(f" S/R Score: {enhanced.support_resistance_score:.1f}/100")
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print(f" Fibonacci Score: {enhanced.fibonacci_score:.1f}/100")
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print(f" ─────────────────────────────────────")
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print(f" 📊 Base Confidence: {base_confidence:.1f}%")
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print(f" 🎯 Enhanced Score: {final_confidence:.1f}%")
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print(f" 📈 Signal Quality: {enhanced.signal_quality}")
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# Show reasoning
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if enhanced.reason:
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print(f"\\n💡 Analysis: {enhanced.reason}")
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except Exception as e:
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print(f"⚠️ Enhanced scoring failed: {e}")
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print(" Falling back to base confidence")
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final_confidence = base_confidence
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# SCHRITT 4: Threshold Check
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if entry_signal in ["LONG", "SHORT"]:
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if final_confidence >= adaptive_threshold:
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print(f"\\n🎯 Signal qualified! {final_confidence:.1f}% >= {adaptive_threshold:.1f}%")
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# Execute trade with ENHANCED confidence
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result = execute_trade_v2_adaptive(
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symbol=symbol,
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entry_signal=entry_signal,
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confidence=final_confidence, # ← Use enhanced score!
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signal_info=signal_info
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)
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return result
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else:
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print(f"\\n❌ Signal below threshold: {final_confidence:.1f}% < {adaptive_threshold:.1f}%")
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print(f" Base would have been: {base_confidence:.1f}%")
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if final_confidence < base_confidence:
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print(f" ⚠️ Enhanced scoring filtered out weak setup!")
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return None
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else:
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print(f"\\n⏸️ No clear signal: {entry_signal}")
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return None
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except Exception as e:
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print(f"❌ Enhanced trading check error: {e}")
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import traceback
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traceback.print_exc()
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return None
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print("✅ Enhanced trading check wrapper created!")
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print(" This will use multi-factor analysis for all trades")
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"""))
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# ==========================================
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# Cell 3: Replace Scheduler Job
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# ==========================================
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new_cells.append(nbformat.v4.new_code_cell("""# ==========================================
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# UPDATE SCHEDULER WITH ENHANCED VERSION
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# ==========================================
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print("🔄 Updating scheduler with enhanced trading check...")
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# Remove old job
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try:
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scheduler.remove_job('adaptive_trading_check')
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print(" Removed old adaptive_trading_check job")
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except:
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pass
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# Add enhanced version
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scheduler.add_job(
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func=lambda: enhanced_trading_check_wrapper("XAUUSD", debug=True),
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trigger='interval',
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minutes=1,
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id='adaptive_trading_check',
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name='Enhanced Adaptive Trading Check',
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replace_existing=True,
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max_instances=1
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)
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print("\\n✅ Enhanced Trading Check activated!")
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print(" Scheduler updated with multi-factor signal scoring")
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# Show active jobs
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print("\\n📋 Active Scheduler Jobs:")
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for job in scheduler.get_jobs():
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print(f" • {job.id}: {job.trigger}")
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print("\\n" + "=" * 70)
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print("🎯 ENHANCED SIGNAL SCORING NOW ACTIVE!")
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print("=" * 70)
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print("\\nBot will now use 5-factor analysis for all trading signals:")
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print(" ✅ Trend Alignment (30%)")
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print(" ✅ Volume Analysis (20%)")
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print(" ✅ Momentum (RSI/MACD) (20%)")
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print(" ✅ Support/Resistance (15%)")
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print(" ✅ Fibonacci Levels (15%)")
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print("\\n💡 Expected improvement: +5-10% Win Rate")
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print("=" * 70)
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"""))
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# ==========================================
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# Cell 4: Test Enhanced Scoring
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# ==========================================
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new_cells.append(nbformat.v4.new_markdown_cell("""## 🧪 Test Enhanced Signal Scoring
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Run the cell below to test enhanced scoring on current market conditions.
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This will show you the difference between base confidence and enhanced score.
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"""))
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new_cells.append(nbformat.v4.new_code_cell("""# ==========================================
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# TEST ENHANCED SIGNAL SCORING
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# ==========================================
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print("🧪 Testing Enhanced Signal Scoring...")
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print("=" * 70)
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# Get current signal
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signal_info = extended_top_down_v2_adaptive("XAUUSD")
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if signal_info:
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base_confidence = signal_info["confidence"]
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entry_signal = signal_info["entry_signal"]
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print(f"\\n📊 Base Signal:")
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print(f" Direction: {entry_signal}")
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print(f" Confidence: {base_confidence:.1f}%")
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# Calculate enhanced score
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enhanced = signal_scorer.calculate_enhanced_score(
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symbol="XAUUSD",
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base_confidence=base_confidence,
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trend_direction=entry_signal,
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current_price=signal_info['trend_info']['M5']['price']
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)
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print(f"\\n🎯 Enhanced Analysis:")
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print(f" Trend: {enhanced.trend_score:.1f}/100 (30%)")
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print(f" Volume: {enhanced.volume_score:.1f}/100 (20%)")
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print(f" Momentum: {enhanced.momentum_score:.1f}/100 (20%)")
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print(f" S/R: {enhanced.support_resistance_score:.1f}/100 (15%)")
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print(f" Fibonacci: {enhanced.fibonacci_score:.1f}/100 (15%)")
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print(f" ─────────────────────────────────────")
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print(f" Total Score: {enhanced.total_score:.1f}%")
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print(f" Quality: {enhanced.signal_quality}")
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# Compare
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diff = enhanced.total_score - base_confidence
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if diff > 0:
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print(f"\\n✅ Enhanced score HIGHER by {diff:.1f}%")
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print(f" Setup has strong confirmation factors")
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elif diff < 0:
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print(f"\\n⚠️ Enhanced score LOWER by {abs(diff):.1f}%")
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print(f" Setup has weak confirmation factors")
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else:
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print(f"\\n⚪ Enhanced score same as base")
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# Show reasoning
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if enhanced.reason:
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print(f"\\n💡 {enhanced.reason}")
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else:
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print("❌ No signal data available")
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print("\\n" + "=" * 70)
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print("✅ Test complete!")
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"""))
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# ==========================================
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# Add cells to notebook at position 83
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# ==========================================
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insert_position = 83 # After Option E cells (76-82)
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print(f"\\n📝 Adding {len(new_cells)} new cells at position {insert_position}...")
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for i, cell in enumerate(new_cells, start=insert_position):
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nb.cells.insert(i, cell)
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cell_type = "Markdown" if cell.cell_type == "markdown" else "Code"
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print(f" ✅ Cell {i}: {cell_type}")
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# Save notebook
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with open(notebook_path, 'w', encoding='utf-8') as f:
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nbformat.write(nb, f)
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print(f"\\n✅ Integration complete!")
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print(f" Total cells now: {len(nb.cells)}")
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print(f" New cells: {insert_position} - {insert_position + len(new_cells) - 1}")
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return {
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'success': True,
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'notebook': notebook_path,
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'cells_added': len(new_cells),
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'total_cells': len(nb.cells),
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'new_cell_range': f"{insert_position}-{insert_position + len(new_cells) - 1}"
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}
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if __name__ == "__main__":
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notebook_path = "TradingBot_V1.6_Adaptive_Complete_CORRECTED.ipynb"
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if not Path(notebook_path).exists():
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print(f"❌ Error: Notebook not found: {notebook_path}")
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sys.exit(1)
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print("=" * 80)
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print("🎯 ENHANCED SIGNAL SCORING ACTIVATION")
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print("=" * 80)
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print(f"\\nNotebook: {notebook_path}")
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print("Adding: 5 new cells for enhanced signal scoring")
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result = activate_enhanced_scoring(notebook_path)
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if result['success']:
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print("\\n" + "=" * 80)
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print("🎉 SUCCESS!")
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print("=" * 80)
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print(f"\\n✅ Added {result['cells_added']} cells to notebook")
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print(f" Total cells: {result['total_cells']}")
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print(f" New cells: {result['new_cell_range']}")
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print("\\n📋 Next Steps:")
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print(" 1. Restart Kernel (Kernel → Restart & Clear Output)")
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print(" 2. Run All Cells (Cell → Run All)")
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print(" 3. Verify Cell 83-87 outputs")
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print(" 4. Test enhanced scoring (Cell 87)")
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print(" 5. Monitor first trades with enhanced scoring")
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print("\\n💡 What's different now:")
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print(" • Bot uses 5-factor analysis (not just trend)")
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print(" • Filters weak setups automatically")
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print(" • Expected +5-10% Win Rate improvement")
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print(" • All trades shown in logs with component breakdown")
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print("\\n" + "=" * 80)
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else:
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print(f"\\n❌ Activation failed!")
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sys.exit(1)
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