Replace pytz.UTC with timezone.utc in AdaptiveRhythmManager
class definition (cell 8) and debug session cell (cell 106).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- 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>
- New ml_signal_predictor.py with XGBoost model
- Feature extraction from historical trades (17 features)
- 5-fold cross-validation for robust training
- Integrated into enhanced_trading_check_wrapper as optional layer
- Disabled by default until model improves (AUC: 0.508)
- Key insight: Asian session is strongest predictor of success
Features used:
- Signal: confidence, threshold, regime_strength
- Session: asian/london/ny/overlap (one-hot)
- Time: hour (cyclical), day_of_week
- Quality: signal_quality score
- Direction: long/short
Usage:
- train_ml_model() to train
- enable_ml_predictor() to activate
- get_ml_status() for info
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Daily loss limit (2% / $500 max)
- Consecutive loss breaker (3 losses → 2h cooldown)
- Max drawdown circuit breaker (10% threshold)
- News filter with 30min buffer for high-impact events
- Integrated as SCHRITT 0.5 in enhanced_trading_check_wrapper
- Combined lot multiplier with Equity Curve and Reversal Detector
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Previous fix with json.dump didn't preserve the escape sequences correctly.
Using nbformat ensures proper handling of Python string literals in notebook cells.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
The \n characters were incorrectly saved as actual newlines instead
of escaped sequences, causing syntax errors in the print statements.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Allows Equity Curve Trading to actually reduce position sizes when
equity falls below MA. Previously, 50% reduction (0.10 → 0.05) was
blocked by min_lot=0.10, making the soft mode ineffective.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
NEW MODULE: equity_curve_trading.py
- EquityCurveManager class for meta-strategy control
- Tracks equity history after each trade
- Calculates Moving Average over configurable period (default: 10 trades)
- Soft Mode: Reduces lot size to 50% when equity < MA
- Hard Mode: Completely stops trading when equity < MA
- Recovery detection with buffer percentage
- Persistent storage in equity_curve_history.json
CONFIGURATION:
- ma_period: 10 trades (Moving Average window)
- min_trades_required: 5 (warmup period)
- soft_mode: True (reduce lots instead of stopping)
- soft_mode_multiplier: 0.5 (50% lots when under MA)
- recovery_buffer_pct: 0.5% (buffer for recovery status)
INTEGRATION:
- Added to Cell 78 (Advanced Optimizations setup)
- Integrated in enhanced_trading_check_wrapper (Cells 85, 90)
- Added lot_multiplier parameter to execute_trade_v2_adaptive
- Equity update after each successful trade
EXAMPLE FLOW:
1. Before trade: Check should_trade() → returns (allowed, reason, lot_multiplier)
2. If equity < MA: lot_multiplier = 0.5 (or 0.0 in hard mode)
3. Position size adjusted: volume = volume * lot_multiplier
4. After trade: update_equity() called to track new equity
BENEFITS:
- Automatic protection during losing streaks
- Reduces exposure when strategy underperforms
- Capitalizes fully when strategy is working
- No emotional decisions needed
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
PROBLEM:
- execute_trade_v2_adaptive didn't accept pre-calculated signal_info
- Function did its own signal analysis internally
- Trying to pass entry_signal/signal_info caused parameter errors
SOLUTION:
- Added optional parameters: signal_info_override, confidence_override
- If provided, function uses pre-calculated values
- If not provided, function calculates values itself (backward compatible)
CHANGES:
- Cell 28: Added new parameters to function signature
- Cell 28: Use signal_info_override if provided
- Cell 28: Use confidence_override if provided
- Cells 85, 90: Updated execute_trade calls to use new parameters
- activate_enhanced_scoring.py: Updated to use new parameters
Now enhanced_trading_check_wrapper can pass its hybrid confidence
score to execute_trade_v2_adaptive properly.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
CRITICAL BUG:
- extended_top_down_v2_adaptive returns entry_signal as NUMBER (1, -1, 0)
- enhanced_trading_check_wrapper checked for STRINGS ("LONG", "SHORT")
- Result: 1 in ["LONG", "SHORT"] = False → NO TRADES EVER EXECUTED!
FIX:
- Changed: if entry_signal in ["LONG", "SHORT"]
- To: if entry_signal in [1, -1] # 1=LONG, -1=SHORT
- Added signal_direction conversion before execute_trade call
This explains why no trades were being executed despite good signals!
Updated:
- TradingBot_V1.6_Adaptive_Complete_CORRECTED.ipynb (Cells 85, 90)
- activate_enhanced_scoring.py
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
PROBLEM:
- Enhanced Score alone (67.8%) was blocking trades with high Base Confidence (97.5%)
- Low Volume Score (40/100) was dragging down the total
- Good trading setups were being rejected
SOLUTION: Hybrid 60/40 Approach
- Final Score = (Base Confidence × 60%) + (Enhanced Score × 40%)
- The proven trend analysis system keeps primary weight (60%)
- Enhanced scoring still filters bad setups (40%)
EXAMPLE:
- Base Confidence: 97.5%
- Enhanced Score: 67.8%
- OLD: final = 67.8% (blocked at 70% threshold)
- NEW: final = (97.5 × 0.6) + (67.8 × 0.4) = 85.6% (passes!)
BENEFITS:
- Respects the proven base trend system
- Enhanced scoring still adds value
- Fewer false rejections of good trades
- Better balance between filtering and opportunity
Updated files:
- TradingBot_V1.6_Adaptive_Complete_CORRECTED.ipynb (Cells 85, 90)
- activate_enhanced_scoring.py
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Updated get_rates() with comprehensive retry logic (3 attempts)
- Added MT5 initialization check before each attempt
- Added symbol visibility check and auto-selection
- Increased wait time to 2 seconds for D1 data loading
- Moved retry logic from get_enhanced_trend_with_retry to get_rates level
- More efficient: retries happen at data source, not wrapper level
This should fix the 'Keine Daten für D1' error during automated trading checks.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Fixed NameError in enhanced trading check:
- check_existing_position() does not exist
- Correct function is check_existing_positions() (with 's')
Fixed in:
- activate_enhanced_scoring.py
- Notebook cells 84 and 89
Error resolved: NameError: name 'check_existing_position' is not defined
ROOT CAUSE FOUND:
User was right - there was an external config file!
advanced_position_management.py had hardcoded values:
- base_risk = 0.01 (should be 0.02)
- return 0.01 fallback (should be 0.10)
- No min/max lot enforcement
CHANGES:
1. advanced_position_management.py:
✅ base_risk: 0.01 → 0.02 (2% risk)
✅ return fallback: 0.01 → 0.10
✅ volume_min: max(broker_min, 0.10)
✅ volume_max: min(broker_max, 0.20)
2. session_filter_patch.py:
✅ Added lot sizing config:
- min_lot: 0.10
- max_lot: 0.20
- default_lot: 0.10
IMPACT:
- Bot will now use 0.10 minimum lot
- Adaptive sizing respects 0.10-0.20 range
- No more 0.01 lot trades
TESTING NEEDED:
1. Restart kernel
2. Reimport advanced_position_management
3. Verify next trade uses 0.10 lot
🎯 Generated with Claude Code
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
PROBLEM:
- User identified lot size settings were chaotic and scattered
- Settings across 3 cells (23, 25, 47) caused confusion
- Multiple attempts needed to fix lot size (0.01 → 0.05 → 0.10)
- Quote: "mir kommt das ganze ein bisschen chaotisch vor"
SOLUTION:
✅ Created centralized TRADING_CONFIG in new Cell 6
✅ Updated Cell 25 (calculate_position_size) to use config
✅ Updated Cell 27 (execute_trade_v2_adaptive) to use config
✅ Updated Cell 49 (ADAPTIVE_COMPLETE_CONFIG) to reference config
CONFIGURATION STRUCTURE:
- lot_sizing: min/max/default lot sizes
- risk: max_risk_per_trade, max_positions, max_daily_loss
- confidence: thresholds per session
- atr: base_multiplier, period
- news_filter: enabled, minutes_before/after
- sessions: enabled sessions
- symbols: primary trading symbol
BENEFITS:
✅ Single source of truth for all settings
✅ Easy to find and change configuration
✅ Clear documentation in one place
✅ Prevents scattered hardcoded values
✅ Future changes require only editing Cell 6
FILES:
- centralize_config.py: Script to add config cells
- update_cells_to_use_config.py: Updates cells to use config
NEXT STEPS:
1. Restart kernel in Jupyter
2. Run Cell 6 (TRADING_CONFIG)
3. Run all other cells
4. Verify bot uses 0.10 lot size
🎯 Generated with Claude Code
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
FOUND THE ROOT CAUSE! Bot was using adaptive_sizing.calculate_position_size()
which had its own hardcoded 0.01 fallbacks.
Fixed 2 critical locations:
- Cell 23: return 0.01 → return 0.10 (calculate_position_size fallback)
- Cell 47: max_risk 0.01 → 0.02 (ADAPTIVE_COMPLETE_CONFIG)
Previous commits only fixed Cell 25, but adaptive sizing
was overriding those values with 0.01 from Cell 23.
Now ALL volume sources use minimum 0.10:
- Cell 23: Fallback returns 0.10
- Cell 25: Min/Max = 0.10/0.20
- Cell 47: Risk = 2% (not 1%)
This is the 3rd attempt - should finally work!
🤖 Generated with Claude Code
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Added comprehensive documentation:
- BOT_ANALYSIS_2026-01-10.md: Trading gap analysis (07-11 Jan)
- PERFORMANCE_REPORT_JAN_2026.md: Full performance metrics
- debug_bot_status.py: Debug script for bot status checks
Performance highlights:
- 74 trade signals over 5 days
- 92.90% average confidence
- 56.7% trades with ≥95% confidence
- News Filter successfully blocked NFP event
Analysis findings:
- Bot working correctly since 12.01
- Trading gap 08-11 Jan explained (NFP + weekend)
- Lot size increased to 0.10
Updated files:
- Notebook with latest trading state
- Performance JSON with new trades (12-13 Jan)
🤖 Generated with Claude Code
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Fixed all volume assignments in Cell 25:
- Line 106: min/max calculation (0.01→0.10, 0.1→0.2)
- Line 108: fallback volume (0.05→0.10)
- Line 110: default volume (0.05→0.10)
Previous commit only changed one location, bot kept using 0.01.
Now ALL volume settings use 0.10 as minimum.
Impact:
- 10x profit/loss per trade
- Pip value: $1.00 (was $0.10)
- Risk: ~2.9% per trade with 20 pip SL
🤖 Generated with Claude Code
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Changes:
- Min Lot: 0.01 → 0.05 (5x increase)
- Max Lot: 0.10 → 0.20 (2x increase)
Impact:
- Higher profit potential per trade
- More aggressive position sizing
- Risk still controlled by percentage
Cell 25 updated with new volume = 0.05
🤖 Generated with Claude Code
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Notebook auto-updates during execution
- All changes synchronized
🤖 Generated with Claude Code
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Notebook updated after opening/execution
- News Filter cells 31-32 in place
- Ready for production use
🤖 Generated with Claude Code
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Added 2 new cells (25-26):
- Cell 25: Info markdown explaining the optimization
- Cell 26: Session confidence filter wrapper code
Changes:
- Wraps execute_trade_v2_adaptive with session-specific thresholds
- Asian: >=95% Confidence (unchanged, 97.8% WR)
- NY: >=97% Confidence (improves WR from 43.3% to 56.5%)
- Expected improvement: +$292/month, +3.2pp win-rate
Implementation:
- Auto-detects and wraps original function
- Preserves original in _original_execute_trade_v2_adaptive
- Clear console output showing active thresholds
- Ready to use immediately after kernel restart
Next step: Kernel -> Restart & Run All
PROBLEM:
- AdaptivePositionSizer was created with default base_risk=0.01 (1%)
- Did NOT use the 2% value from SESSION_WHITELIST_CONFIG
- Result: Position sizes still only 0.01 lot
SOLUTION:
- Added base_risk parameter to AdvancedPositionManager.__init__()
- Updated notebook Cell 9 to pass SESSION_WHITELIST_CONFIG['max_risk_per_trade']
- Now correctly uses 2% base risk for adaptive sizing
EXPECTED RESULT:
- High Confidence (≥80%): 2% × 1.5 = 3% → ~0.03 Lot
- Medium Confidence (≥70%): 2% × 1.0 = 2% → ~0.02 Lot
- Low Confidence (<70%): 2% × 0.5 = 1% → ~0.01 Lot
FILES CHANGED:
- advanced_position_management.py: Added base_risk parameter
- TradingBot_V1.6_Adaptive_Complete_CORRECTED.ipynb: Pass config value to manager
cbazzaandClaude Sonnet 4.6