XauBot/backtests/v0.6.0_fixed

XAUBot AI v0.6.0 FIXED - Backtest

Backtest dengan implementasi rekomendasi dari Profesor AI & Ilmuwan Algoritma Trading.

📋 Fixes Implemented

PRIORITY 1: Tiered Fuzzy Exit Thresholds

Problem: 75% wins adalah micro profits (<$1) karena fuzzy threshold fixed 90% Solution: Dynamic thresholds based on profit tier

Micro  (<$1):  70% threshold  # Exit early (was 90%)
Small  ($1-3): 75% threshold  # Small protection (was 85%)
Medium ($3-8): 85% threshold  # Hold for more (was 85%)
Large  (>$8):  90% threshold  # Maximize (was 80%)

Expected Impact: Micro profits 75% → <20% (-73%)

PRIORITY 2: Trajectory Confidence Calibration

Problem: Predictions $10-66 but actual $0.28-0.58 (error 95%+) Solution: Conservative predictions with regime penalty

# Regime penalties
ranging:  0.4  # 60% discount (low predictability)
volatile: 0.6  # 40% discount (high noise)
trending: 0.9  # 10% discount (best predictability)

# Add 95% CI uncertainty
prediction_std = abs(acceleration) * horizon * 5
conservative = calibrated - 1.96 * prediction_std

Expected Impact: Prediction error 95% → <40% (-58%)

PRIORITY 3: Session Filter

Problem: Sydney/Tokyo (00:00-10:00) generated micro profits only Solution: DISABLE low-volatility sessions

Sydney/Tokyo (00:00-10:00): BLOCKED
Late NY      (22:00-01:00): BLOCKED
London       (14:00-20:00): ALLOWED (best performance)

Expected Impact: Avg profit/trade +50%+ (filtering bad trades)

PRIORITY 4: Unicode Fix

Problem: Log corruption from emojis (⏳, →, ✓) Solution: ASCII-only logging

Impact: Stable logs, easier debugging

PRIORITY 5: Tighter Stop-Loss

Problem: Max loss -$34.70 (17x avg win) Solution: Reduce max loss per trade

Max Loss: $50 → $25

Expected Impact: Avg loss $20.91 → $8-12 (-60%)

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🎯 Expected Results

Metric	Before	Target	Improvement
Avg Win	$4.07	$8-12	+100-200%
RR Ratio	1:5	1.5:1	+650%
Micro Profits	75%	<20%	-73%
Win Rate	57%	62-65%	+8%
Sharpe Ratio	0.8	1.5+	+87%

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🚀 Usage

Quick Run (90 days)

cd "C:/Users/Administrator/Videos/Smart Automatic Trading BOT + AI/backtests/v0.6.0_fixed"
python run_backtest.py

Custom Period

python run_backtest.py --days 30
python run_backtest.py --days 180

Save Results to CSV

python run_backtest.py --days 90 --save

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📁 Files

• backtest_v0_6_0_fixed.py - Main backtest engine with fixes
• run_backtest.py - Quick runner script
• README.md - This file
• results_*.csv - Backtest results (when using --save)

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📊 Understanding Results

PASS Criteria

• ✅ Avg Win ≥ $8
• ✅ RR Ratio ≤ 1.5:1 (avg loss ≤ 1.5x avg win)
• ✅ Micro Profits < 20%
• ✅ Win Rate 62-65%
• ✅ Sharpe Ratio ≥ 1.5

What to Look For

1. Micro Profit % - Should be dramatically lower (<20% vs 75%)
2. RR Ratio - Should be balanced (1.5:1 or better)
3. Sharpe Ratio - Should exceed 1.5 (risk-adjusted returns)
4. Exit Reasons - Fuzzy exits should dominate (not trajectory overrides)

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🔬 Technical Details

Exit Logic Flow

1. Take Profit Hit        → Exit (ideal)
2. Max Loss ($25)         → Exit (protection)
3. Fuzzy Confidence > X%  → Exit (tiered threshold)
   - <$1:  70% threshold
   - $1-3: 75% threshold
   - $3-8: 85% threshold
   - >$8:  90% threshold
4. ML Reversal (>65%)     → Exit (signal change)
5. Timeout (8 hours)      → Exit (stuck trade)

Fuzzy Confidence Calculation

Components (0.0-1.0):
- Velocity (40%):   crashing=-0.10 → conf +0.40
- Retention (30%):  <70% from peak → conf +0.30
- Acceleration (20%): <-0.002 → conf +0.20
- Time (10%):       >6h → conf +0.10

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🎓 Next Steps

If Results PASS (meet targets):

1. Apply fixes to main_live.py
2. Update smart_risk_manager.py with new thresholds
3. Demo account testing (2 weeks)
4. Go live if Sharpe >1.2

If Results FAIL (below targets):

1. Analyze exit reason distribution
2. Adjust fuzzy thresholds (try 65-85%)
3. Test different session windows
4. Re-run with different parameters

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Author: Profesor AI & Ilmuwan Algoritma Trading Date: 2026-02-11 Version: v0.6.0 FIXED