feat: implement Professor AI recommendations v0.2.2 (5 critical fixes)
Exit Strategy v6.6 "Professor AI Validated" - All recommendations implemented FIX #1: Remove Misleading Debug Code - Removed manual trajectory calculation (line 1262-1269) - Trajectory predictor was CORRECT, debug comparison was WRONG - Cleaned up false "bug found" warnings FIX #2: Peak Detection Logic (CHECK 0A.4) - Detects approaching peak (vel > 0, accel < 0) - Holds position if peak within 30s and 15%+ profit ahead - Suppresses fuzzy exits during peak approach - Target: Peak capture 38% -> 70%+ - Added peak_hold_active field to PositionGuard FIX #3: London False Breakout Filter - London session + ATR ratio < 1.2 = whipsaw risk - Requires ML confidence 70% (instead of 60%) - Prevents false breakouts during low volatility - Implemented in main_live.py before signal logic FIX #4: Enhanced Kelly Partial Exit Strategy - Active for all profits >= tp_min * 0.5 (not just >$8) - Recommends partial exits for better peak capture - Full exit when Kelly suggests >70% close - Note: Actual partial close needs MT5 volume parameter (TODO) FIX #5: Unicode Encoding Fixes - Added UTF-8 encoding to file logger - Replaced all emoji (⚠️ -> [WARNING]) and arrows (-> -> ->) - No more UnicodeEncodeError on Windows console - Fixed in 11 src/*.py files Expected Performance: - Peak Capture: 38% -> 70%+ (+84%) - Avg Profit: $2.00 -> $4.50 (+125%) - Risk/Reward: 0.49 -> 1.2+ (+145%) - Win Rate: Maintain 76% Files Modified: - src/smart_risk_manager.py (peak detection, Kelly, unicode) - src/trajectory_predictor.py (unicode arrows) - main_live.py (London filter, UTF-8 encoding) - src/*.py (unicode cleanup: 11 files) - VERSION (0.2.1 -> 0.2.2) - CHANGELOG.md (comprehensive v0.2.2 docs) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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Claude Sonnet 4.5
Claude Sonnet 4.5
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# XAUBot AI v0.6.0 FIXED - Backtest
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Backtest dengan implementasi rekomendasi dari **Profesor AI & Ilmuwan Algoritma Trading**.
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## 📋 Fixes Implemented
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### PRIORITY 1: Tiered Fuzzy Exit Thresholds
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**Problem:** 75% wins adalah micro profits (<$1) karena fuzzy threshold fixed 90%
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**Solution:** Dynamic thresholds based on profit tier
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```python
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Micro (<$1): 70% threshold # Exit early (was 90%)
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Small ($1-3): 75% threshold # Small protection (was 85%)
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Medium ($3-8): 85% threshold # Hold for more (was 85%)
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Large (>$8): 90% threshold # Maximize (was 80%)
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```
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**Expected Impact:** Micro profits 75% → <20% (-73%)
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### PRIORITY 2: Trajectory Confidence Calibration
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**Problem:** Predictions $10-66 but actual $0.28-0.58 (error 95%+)
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**Solution:** Conservative predictions with regime penalty
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```python
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# Regime penalties
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ranging: 0.4 # 60% discount (low predictability)
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volatile: 0.6 # 40% discount (high noise)
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trending: 0.9 # 10% discount (best predictability)
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# Add 95% CI uncertainty
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prediction_std = abs(acceleration) * horizon * 5
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conservative = calibrated - 1.96 * prediction_std
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```
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**Expected Impact:** Prediction error 95% → <40% (-58%)
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### PRIORITY 3: Session Filter
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**Problem:** Sydney/Tokyo (00:00-10:00) generated micro profits only
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**Solution:** DISABLE low-volatility sessions
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```python
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Sydney/Tokyo (00:00-10:00): BLOCKED
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Late NY (22:00-01:00): BLOCKED
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London (14:00-20:00): ALLOWED (best performance)
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```
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**Expected Impact:** Avg profit/trade +50%+ (filtering bad trades)
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### PRIORITY 4: Unicode Fix
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**Problem:** Log corruption from emojis (⏳, →, ✓)
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**Solution:** ASCII-only logging
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**Impact:** Stable logs, easier debugging
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### PRIORITY 5: Tighter Stop-Loss
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**Problem:** Max loss -$34.70 (17x avg win)
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**Solution:** Reduce max loss per trade
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```python
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Max Loss: $50 → $25
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```
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**Expected Impact:** Avg loss $20.91 → $8-12 (-60%)
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---
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## 🎯 Expected Results
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| Metric | Before | Target | Improvement |
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|--------|--------|--------|-------------|
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| Avg Win | $4.07 | $8-12 | +100-200% |
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| RR Ratio | 1:5 | 1.5:1 | +650% |
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| Micro Profits | 75% | <20% | -73% |
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| Win Rate | 57% | 62-65% | +8% |
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| Sharpe Ratio | 0.8 | 1.5+ | +87% |
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---
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## 🚀 Usage
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### Quick Run (90 days)
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```bash
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cd "C:/Users/Administrator/Videos/Smart Automatic Trading BOT + AI/backtests/v0.6.0_fixed"
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python run_backtest.py
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```
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### Custom Period
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```bash
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python run_backtest.py --days 30
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python run_backtest.py --days 180
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```
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### Save Results to CSV
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```bash
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python run_backtest.py --days 90 --save
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```
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---
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## 📁 Files
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- `backtest_v0_6_0_fixed.py` - Main backtest engine with fixes
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- `run_backtest.py` - Quick runner script
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- `README.md` - This file
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- `results_*.csv` - Backtest results (when using --save)
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---
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## 📊 Understanding Results
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### PASS Criteria
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- ✅ Avg Win ≥ $8
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- ✅ RR Ratio ≤ 1.5:1 (avg loss ≤ 1.5x avg win)
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- ✅ Micro Profits < 20%
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- ✅ Win Rate 62-65%
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- ✅ Sharpe Ratio ≥ 1.5
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### What to Look For
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1. **Micro Profit %** - Should be dramatically lower (<20% vs 75%)
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2. **RR Ratio** - Should be balanced (1.5:1 or better)
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3. **Sharpe Ratio** - Should exceed 1.5 (risk-adjusted returns)
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4. **Exit Reasons** - Fuzzy exits should dominate (not trajectory overrides)
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---
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## 🔬 Technical Details
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### Exit Logic Flow
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```
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1. Take Profit Hit → Exit (ideal)
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2. Max Loss ($25) → Exit (protection)
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3. Fuzzy Confidence > X% → Exit (tiered threshold)
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- <$1: 70% threshold
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- $1-3: 75% threshold
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- $3-8: 85% threshold
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- >$8: 90% threshold
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4. ML Reversal (>65%) → Exit (signal change)
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5. Timeout (8 hours) → Exit (stuck trade)
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```
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### Fuzzy Confidence Calculation
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```python
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Components (0.0-1.0):
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- Velocity (40%): crashing=-0.10 → conf +0.40
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- Retention (30%): <70% from peak → conf +0.30
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- Acceleration (20%): <-0.002 → conf +0.20
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- Time (10%): >6h → conf +0.10
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```
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---
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## 🎓 Next Steps
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### If Results PASS (meet targets):
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1. Apply fixes to `main_live.py`
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2. Update `smart_risk_manager.py` with new thresholds
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3. Demo account testing (2 weeks)
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4. Go live if Sharpe >1.2
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### If Results FAIL (below targets):
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1. Analyze exit reason distribution
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2. Adjust fuzzy thresholds (try 65-85%)
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3. Test different session windows
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4. Re-run with different parameters
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---
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**Author:** Profesor AI & Ilmuwan Algoritma Trading
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**Date:** 2026-02-11
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**Version:** v0.6.0 FIXED
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