trading_bot_v4/OPTIMIZATION_MASTER_ROADMAP.md

Trading Bot Optimization - Master Roadmap

Last Updated: November 12, 2025
Current Capital: $97.55 USDC
Phase 1 Goal: $106 → $2,500 (60%+ win rate, aggressive compounding)

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Overview: Three Parallel Data-Driven Optimizations

All three initiatives follow the same pattern:

1. Collect data with current system (20-50 trades)
2. Analyze patterns via SQL backtesting
3. Implement changes with A/B testing
4. Deploy if successful (10%+ improvement required)

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🎯 Initiative 1: Signal Quality Optimization

File: SIGNAL_QUALITY_OPTIMIZATION_ROADMAP.md

Purpose

Filter out bad trades BEFORE entry by optimizing quality score thresholds.

Current Status

📊 Phase 1 (IN PROGRESS) - Blocked Signals Collection

• Progress: 0/10-20 blocked signals needed
• Started: November 11, 2025
• System: Automatically saves blocked signals to database with full metrics

What's Being Collected

Every blocked signal saves:

• Signal metrics: ATR, ADX, RSI, volumeRatio, pricePosition, timeframe
• Quality score + breakdown (what caused low score)
• Block reason (QUALITY_SCORE_TOO_LOW, COOLDOWN, etc.)
• Future: Price movement tracking (would it have hit TP1/TP2/SL?)

Key Questions to Answer

• Are we blocking good signals? (score 55-59 that would have won)
• Are we letting bad signals through? (score 65-70 that lose)
• Should threshold be 60? 65? 70? Symbol-specific?

Timeline

• Phase 1: 1-2 weeks (need 10-20 blocked signals)
• Phase 2: 1 day (SQL analysis)
• Phase 3: 2-3 hours (adjust thresholds)
• Total: ~2-3 weeks

Success Metrics

• Win rate improves by 5%+ (60% → 65%)
• Fewer losing trades in 50-60% drawdown range
• Maintain or increase trade frequency

SQL Queries Ready

See BLOCKED_SIGNALS_TRACKING.md for full query reference

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📐 Initiative 2: Position Scaling & Exit Strategy

File: POSITION_SCALING_ROADMAP.md

Purpose

Optimize HOW MUCH to close at each target and WHEN to move stops.

Current Status

✅ Phase 5 COMPLETE - TP2-as-Runner Implemented (Nov 11)

• TP1 closes 75% (configurable via TAKE_PROFIT_1_SIZE_PERCENT)
• TP2 activates trailing stop on remaining 25%
• ATR-based dynamic trailing stop (Nov 11)

📊 Phase 2-4 PENDING - Need more trade data

• Requires 50+ trades with full MFE/MAE tracking
• Currently: 160 trades total, need more with v6 indicator data

What's Being Collected

Every trade tracks:

• maxFavorableExcursion (MFE) - best profit % reached
• maxAdverseExcursion (MAE) - worst drawdown % reached
• maxFavorablePrice / maxAdversePrice - exact prices
• Actual TP1/TP2/SL hit vs theoretical optimal exits

Key Questions to Answer

• Should TP1 be at 0.4% or 0.6%? (optimize via MFE data)
• Should runner be 25% or 35%? (quality-based sizing)
• When to move SL to breakeven? (after TP1 or earlier?)
• Should high-quality signals (95+) keep 35% runner vs 25%?

Timeline

• Phase 1: ✅ COMPLETE
• Phase 2: 2-3 weeks (collect 50+ trades)
• Phase 3: 1 day (SQL analysis)
• Phase 4: 2-3 hours (implement quality-based scaling)
• Total: ~3-4 weeks

Success Metrics

• 15%+ increase in total P&L
• Maintain 60%+ win rate
• Average winner size increases
• Runner exits capture extended moves

Related Systems

• ATR-based trailing stop (✅ implemented Nov 11)
• TP2-as-runner activation (✅ implemented Nov 11)
• Quality-based position sizing (🔜 after data collection)

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📊 Initiative 3: ATR-Based Take Profit Levels

File: ATR_BASED_TP_ROADMAP.md

Purpose

Replace fixed % targets with volatility-adaptive targets using ATR multipliers.

Current Status

📊 Phase 1 (IN PROGRESS) - ATR Data Collection

• Progress: 1/50 trades with ATR tracking
• Started: November 11, 2025 (with indicator v6)
• System: atrAtEntry field saved with every trade

Current System (Fixed %)

TP1: Entry + 0.4% (always)
TP2: Entry + 0.7% (always)
SL:  Entry - 1.0% (always)

Proposed System (ATR Multipliers)

TP1: Entry + (ATR × 1.5)  # Adaptive target
TP2: Entry + (ATR × 2.5)  # Adaptive target
SL:  Entry - (ATR × 2.0)  # Adaptive stop

What's Being Collected

Every trade now saves:

• atrAtEntry (Float) - ATR % when trade opened
• Entry price, exit price, realized P&L
• MFE/MAE to determine if ATR-based targets would have hit

Key Questions to Answer

• Does 1.5x ATR TP1 hit more often than fixed 0.4%?
• Do ATR-based targets improve P&L by 10%+?
• What multipliers work best? (1.5x? 2.0x? Symbol-specific?)
• Should we use hybrid? (max of fixed % OR ATR-based)

Example Comparison

Current trade (ATR=0.26%, entry=$160.62):

• Fixed 0.4% TP1: $161.27 (+$0.64)
• 1.5x ATR TP1: $161.25 (+$0.63) ← Almost identical!

But in volatile market (ATR=0.50%):

• Fixed 0.4% TP1: $161.27 (same)
• 1.5x ATR TP1: $161.83 (+0.75%) ← 88% wider, more room!

Timeline

• Phase 1: 2-4 weeks (collect 50+ trades with ATR)
• Phase 2: 1-2 days (backtest analysis)
• Phase 3: 2-3 hours (implementation)
• Phase 4: 2-3 weeks (A/B testing)
• Total: ~6-8 weeks

Success Metrics

• TP1 hit rate ≥ 75% (vs current ~70%)
• Win rate maintained at 60%+
• Total P&L improvement of 10%+
• Better performance in volatile vs calm markets

SQL Backtest Ready

Complete backtest query in ATR_BASED_TP_ROADMAP.md (lines 100-150)

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📅 Unified Timeline & Priorities

Current Week (Nov 12-18, 2025)

Focus: Data collection for all three initiatives

• ✅ Systems deployed and collecting data automatically
• 🔄 Execute 10-15 trades (indicator v6 in production)
• 📊 Monitor: 0 blocked signals, 1 ATR-tracked trade, 160 total trades

Week 2-3 (Nov 19 - Dec 2)

Focus: Signal Quality Analysis (fastest ROI)

• Should have 10-20 blocked signals by then
• Run SQL analysis on blocked vs executed trades
• Adjust quality thresholds if data shows improvement
• Expected Impact: +5% win rate, fewer bad trades

Week 4-5 (Dec 3-16)

Focus: Continue data collection

• Target: 30-40 total trades with v6 + ATR data
• Monitor performance with any signal quality changes
• Begin preliminary ATR-based backtest analysis

Week 6-8 (Dec 17 - Jan 6)

Focus: Position Scaling & ATR-based TP Analysis

• Should have 50+ trades by then
• Run comprehensive backtests on both initiatives
• Implement changes with highest expected value
• A/B test for 2-3 weeks

Phase 1 Complete (Late January 2026)

Target: All three optimizations deployed and validated

• Signal quality: Optimized thresholds
• Position scaling: Quality-based runner sizing
• ATR-based TP: Volatility-adaptive targets (if backtest successful)

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🎯 Expected Combined Impact

Conservative Estimate

If each initiative improves performance by 10% independently:

• Signal Quality: +5% win rate → fewer losses
• Position Scaling: +15% average win size → bigger winners
• ATR-based TP: +10% total P&L → better hit rates

Combined: ~35-40% improvement in total P&L over 3 months

Impact on Phase 1 Goal ($106 → $2,500)

Current trajectory: 20-30% monthly returns = 6-7 months With optimizations: 25-35% monthly returns = 4-5 months

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📊 Data Requirements Summary

Initiative	Data Needed	Current Progress	Est. Completion
Signal Quality	10-20 blocked signals	0/20 (0%)	~2 weeks
Position Scaling	50+ trades w/ MFE/MAE	160/50 (✅) but need v6 data	~3 weeks
ATR-based TP	50+ trades w/ ATR	1/50 (2%)	~4 weeks

Bottleneck: Trade frequency (3-5 signals/day = 10-17 days for 50 trades)

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🔧 Technical Implementation Status

✅ Already Implemented (Ready for data)

• atrAtEntry field in database (Nov 11)
• indicatorVersion tracking (Nov 12)
• ATR-based trailing stop (Nov 11)
• TP2-as-runner system (Nov 11)
• BlockedSignal table & auto-logging (Nov 11)
• MFE/MAE tracking (existing)
• Signal quality scoring v4 (Nov 11)

🔜 Needs Implementation (After data analysis)

• Quality threshold adjustments
• Quality-based position sizing
• ATR-based TP/SL calculation (optional toggle)
• Hybrid target system (max of fixed % or ATR)

📝 Configuration Ready

All systems have ENV variables and config structure ready:

• MIN_SIGNAL_QUALITY_SCORE (adjustable after analysis)
• TAKE_PROFIT_1_SIZE_PERCENT (70% default, adjustable)
• USE_ATR_BASED_TARGETS (false, will enable after backtest)
• TP1_ATR_MULTIPLIER, TP2_ATR_MULTIPLIER, SL_ATR_MULTIPLIER

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📈 Progress Tracking

Weekly Check-in Questions

1. How many trades executed this week?
2. How many blocked signals collected?
3. Any patterns emerging in blocked signals?
4. What's the current win rate vs target 60%?
5. Are MFE/MAE averages improving?

Monthly Review Questions

1. Do we have enough data for next optimization phase?
2. What's the biggest win/loss this month and why?
3. Is indicator v6 outperforming v5? (need 20+ trades each)
4. Should we adjust any thresholds based on patterns?
5. Are we on track for Phase 1 goal ($2,500)?

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🚨 Risk Management

Don't Optimize Too Early

• ❌ Bad: Change thresholds after 5 trades and 2 blocked signals
• ✅ Good: Wait for 20 blocked signals + 50 trades minimum
• Statistical significance matters!

Keep Historical Baseline

• Always compare against "what would the old system do?"
• Track signalQualityVersion and indicatorVersion for this
• Can revert if changes make things worse

A/B Test Before Full Deploy

• Test new thresholds on 50% of signals (coin flip)
• Compare results after 20-30 trades
• Only deploy if statistically better (p < 0.05)

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📚 Related Documentation

Core System:

• copilot-instructions.md - Full system architecture
• TRADING_GOALS.md - 8-phase financial roadmap ($106 → $1M+)
• ATR_TRAILING_STOP_FIX.md - Dynamic trailing stop implementation

Data Analysis:

• BLOCKED_SIGNALS_TRACKING.md - SQL queries for signal analysis
• docs/analysis/SIGNAL_QUALITY_VERSION_ANALYSIS.sql - Version comparison queries

Implementation Guides:

• SIGNAL_QUALITY_SETUP_GUIDE.md - How signal scoring works
• PERCENTAGE_SIZING_FEATURE.md - Position sizing system

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🎯 Next Actions

Immediate (This Week)

1. ✅ Deploy indicator v6 to TradingView production
2. 🔄 Execute 10-15 trades to start data collection
3. 📊 Monitor blocked signals (target: 2-3 this week)
4. 🎯 Verify current trade closes correctly with new fixes

Short Term (2-3 Weeks)

1. Collect 10-20 blocked signals
2. Run signal quality analysis
3. Adjust MIN_SIGNAL_QUALITY_SCORE if data shows improvement
4. Continue collecting ATR data (target: 30-40 trades)

Medium Term (4-8 Weeks)

1. Run position scaling backtest (50+ trades)
2. Run ATR-based TP backtest (50+ trades)
3. Implement quality-based position sizing
4. A/B test ATR-based targets vs fixed %
5. Deploy winning strategies

Long Term (Phase 1 Complete)

1. Document all optimizations in copilot-instructions.md
2. Prepare for Phase 2 ($2,500 → $10,000)
3. Consider new optimizations:
   • Time-of-day filtering
   • Symbol-specific thresholds
   • Volatility regime detection
   • Machine learning for signal scoring

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Bottom Line: Three complementary optimizations, all data-driven, all on track. Focus on collecting clean data now, analyze when we have enough, implement what works. No premature optimization. 📊🚀