71c856c720
Created unified roadmap consolidating all three optimization initiatives to reduce fragmentation and provide single source of truth. Three parallel data-driven optimizations: 1. Signal Quality (0/20 blocked signals) - 2-3 weeks 2. Position Scaling (160 trades, need v6 data) - 3-4 weeks 3. ATR-based TP (1/50 trades) - 6-8 weeks All follow same pattern: collect data → analyze → implement → A/B test Expected combined impact: 35-40% P&L improvement over 3 months 4-5 months to reach $2,500 Includes: - Unified timeline & priorities - Progress tracking framework - Weekly/monthly check-in questions - Risk management (no premature optimization) - Cross-references to all three roadmaps Single dashboard for all optimization efforts.
353 lines
11 KiB
Markdown
353 lines
11 KiB
Markdown
# Trading Bot Optimization - Master Roadmap
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**Last Updated:** November 12, 2025
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**Current Capital:** $97.55 USDC
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**Phase 1 Goal:** $106 → $2,500 (60%+ win rate, aggressive compounding)
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---
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## Overview: Three Parallel Data-Driven Optimizations
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All three initiatives follow the same pattern:
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1. **Collect data** with current system (20-50 trades)
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2. **Analyze patterns** via SQL backtesting
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3. **Implement changes** with A/B testing
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4. **Deploy if successful** (10%+ improvement required)
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---
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## 🎯 Initiative 1: Signal Quality Optimization
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**File:** [`SIGNAL_QUALITY_OPTIMIZATION_ROADMAP.md`](./SIGNAL_QUALITY_OPTIMIZATION_ROADMAP.md)
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### Purpose
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Filter out bad trades BEFORE entry by optimizing quality score thresholds.
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### Current Status
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📊 **Phase 1 (IN PROGRESS)** - Blocked Signals Collection
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- **Progress:** 0/10-20 blocked signals needed
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- **Started:** November 11, 2025
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- **System:** Automatically saves blocked signals to database with full metrics
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### What's Being Collected
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Every blocked signal saves:
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- Signal metrics: ATR, ADX, RSI, volumeRatio, pricePosition, timeframe
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- Quality score + breakdown (what caused low score)
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- Block reason (QUALITY_SCORE_TOO_LOW, COOLDOWN, etc.)
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- Future: Price movement tracking (would it have hit TP1/TP2/SL?)
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### Key Questions to Answer
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- Are we blocking good signals? (score 55-59 that would have won)
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- Are we letting bad signals through? (score 65-70 that lose)
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- Should threshold be 60? 65? 70? Symbol-specific?
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### Timeline
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- **Phase 1:** 1-2 weeks (need 10-20 blocked signals)
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- **Phase 2:** 1 day (SQL analysis)
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- **Phase 3:** 2-3 hours (adjust thresholds)
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- **Total:** ~2-3 weeks
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### Success Metrics
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- Win rate improves by 5%+ (60% → 65%)
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- Fewer losing trades in 50-60% drawdown range
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- Maintain or increase trade frequency
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### SQL Queries Ready
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See `BLOCKED_SIGNALS_TRACKING.md` for full query reference
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---
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## 📐 Initiative 2: Position Scaling & Exit Strategy
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**File:** [`POSITION_SCALING_ROADMAP.md`](./POSITION_SCALING_ROADMAP.md)
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### Purpose
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Optimize HOW MUCH to close at each target and WHEN to move stops.
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### Current Status
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✅ **Phase 5 COMPLETE** - TP2-as-Runner Implemented (Nov 11)
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- TP1 closes 75% (configurable via `TAKE_PROFIT_1_SIZE_PERCENT`)
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- TP2 activates trailing stop on remaining 25%
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- ATR-based dynamic trailing stop (Nov 11)
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📊 **Phase 2-4 PENDING** - Need more trade data
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- Requires 50+ trades with full MFE/MAE tracking
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- Currently: 160 trades total, need more with v6 indicator data
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### What's Being Collected
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Every trade tracks:
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- `maxFavorableExcursion` (MFE) - best profit % reached
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- `maxAdverseExcursion` (MAE) - worst drawdown % reached
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- `maxFavorablePrice` / `maxAdversePrice` - exact prices
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- Actual TP1/TP2/SL hit vs theoretical optimal exits
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### Key Questions to Answer
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- Should TP1 be at 0.4% or 0.6%? (optimize via MFE data)
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- Should runner be 25% or 35%? (quality-based sizing)
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- When to move SL to breakeven? (after TP1 or earlier?)
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- Should high-quality signals (95+) keep 35% runner vs 25%?
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### Timeline
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- **Phase 1:** ✅ COMPLETE
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- **Phase 2:** 2-3 weeks (collect 50+ trades)
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- **Phase 3:** 1 day (SQL analysis)
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- **Phase 4:** 2-3 hours (implement quality-based scaling)
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- **Total:** ~3-4 weeks
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### Success Metrics
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- 15%+ increase in total P&L
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- Maintain 60%+ win rate
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- Average winner size increases
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- Runner exits capture extended moves
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### Related Systems
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- ATR-based trailing stop (✅ implemented Nov 11)
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- TP2-as-runner activation (✅ implemented Nov 11)
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- Quality-based position sizing (🔜 after data collection)
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---
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## 📊 Initiative 3: ATR-Based Take Profit Levels
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**File:** [`ATR_BASED_TP_ROADMAP.md`](./ATR_BASED_TP_ROADMAP.md)
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### Purpose
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Replace fixed % targets with volatility-adaptive targets using ATR multipliers.
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### Current Status
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📊 **Phase 1 (IN PROGRESS)** - ATR Data Collection
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- **Progress:** 1/50 trades with ATR tracking
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- **Started:** November 11, 2025 (with indicator v6)
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- **System:** `atrAtEntry` field saved with every trade
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### Current System (Fixed %)
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```
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TP1: Entry + 0.4% (always)
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TP2: Entry + 0.7% (always)
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SL: Entry - 1.0% (always)
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```
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### Proposed System (ATR Multipliers)
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```
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TP1: Entry + (ATR × 1.5) # Adaptive target
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TP2: Entry + (ATR × 2.5) # Adaptive target
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SL: Entry - (ATR × 2.0) # Adaptive stop
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```
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### What's Being Collected
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Every trade now saves:
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- `atrAtEntry` (Float) - ATR % when trade opened
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- Entry price, exit price, realized P&L
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- MFE/MAE to determine if ATR-based targets would have hit
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### Key Questions to Answer
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- Does 1.5x ATR TP1 hit more often than fixed 0.4%?
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- Do ATR-based targets improve P&L by 10%+?
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- What multipliers work best? (1.5x? 2.0x? Symbol-specific?)
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- Should we use hybrid? (max of fixed % OR ATR-based)
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### Example Comparison
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**Current trade (ATR=0.26%, entry=$160.62):**
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- Fixed 0.4% TP1: $161.27 (+$0.64)
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- 1.5x ATR TP1: $161.25 (+$0.63) ← Almost identical!
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**But in volatile market (ATR=0.50%):**
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- Fixed 0.4% TP1: $161.27 (same)
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- 1.5x ATR TP1: $161.83 (+0.75%) ← 88% wider, more room!
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### Timeline
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- **Phase 1:** 2-4 weeks (collect 50+ trades with ATR)
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- **Phase 2:** 1-2 days (backtest analysis)
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- **Phase 3:** 2-3 hours (implementation)
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- **Phase 4:** 2-3 weeks (A/B testing)
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- **Total:** ~6-8 weeks
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### Success Metrics
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- TP1 hit rate ≥ 75% (vs current ~70%)
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- Win rate maintained at 60%+
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- Total P&L improvement of 10%+
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- Better performance in volatile vs calm markets
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### SQL Backtest Ready
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Complete backtest query in `ATR_BASED_TP_ROADMAP.md` (lines 100-150)
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---
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## 📅 Unified Timeline & Priorities
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### Current Week (Nov 12-18, 2025)
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**Focus:** Data collection for all three initiatives
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- ✅ Systems deployed and collecting data automatically
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- 🔄 Execute 10-15 trades (indicator v6 in production)
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- 📊 Monitor: 0 blocked signals, 1 ATR-tracked trade, 160 total trades
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### Week 2-3 (Nov 19 - Dec 2)
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**Focus:** Signal Quality Analysis (fastest ROI)
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- Should have 10-20 blocked signals by then
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- Run SQL analysis on blocked vs executed trades
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- Adjust quality thresholds if data shows improvement
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- **Expected Impact:** +5% win rate, fewer bad trades
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### Week 4-5 (Dec 3-16)
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**Focus:** Continue data collection
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- Target: 30-40 total trades with v6 + ATR data
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- Monitor performance with any signal quality changes
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- Begin preliminary ATR-based backtest analysis
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### Week 6-8 (Dec 17 - Jan 6)
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**Focus:** Position Scaling & ATR-based TP Analysis
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- Should have 50+ trades by then
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- Run comprehensive backtests on both initiatives
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- Implement changes with highest expected value
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- A/B test for 2-3 weeks
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### Phase 1 Complete (Late January 2026)
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**Target:** All three optimizations deployed and validated
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- Signal quality: Optimized thresholds
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- Position scaling: Quality-based runner sizing
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- ATR-based TP: Volatility-adaptive targets (if backtest successful)
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---
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## 🎯 Expected Combined Impact
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### Conservative Estimate
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If each initiative improves performance by 10% independently:
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- Signal Quality: +5% win rate → fewer losses
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- Position Scaling: +15% average win size → bigger winners
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- ATR-based TP: +10% total P&L → better hit rates
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**Combined:** ~35-40% improvement in total P&L over 3 months
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### Impact on Phase 1 Goal ($106 → $2,500)
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**Current trajectory:** 20-30% monthly returns = 6-7 months
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**With optimizations:** 25-35% monthly returns = 4-5 months
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---
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## 📊 Data Requirements Summary
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| Initiative | Data Needed | Current Progress | Est. Completion |
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|------------|-------------|------------------|-----------------|
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| Signal Quality | 10-20 blocked signals | 0/20 (0%) | ~2 weeks |
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| Position Scaling | 50+ trades w/ MFE/MAE | 160/50 (✅) but need v6 data | ~3 weeks |
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| ATR-based TP | 50+ trades w/ ATR | 1/50 (2%) | ~4 weeks |
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**Bottleneck:** Trade frequency (3-5 signals/day = 10-17 days for 50 trades)
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---
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## 🔧 Technical Implementation Status
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### ✅ Already Implemented (Ready for data)
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- `atrAtEntry` field in database (Nov 11)
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- `indicatorVersion` tracking (Nov 12)
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- ATR-based trailing stop (Nov 11)
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- TP2-as-runner system (Nov 11)
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- BlockedSignal table & auto-logging (Nov 11)
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- MFE/MAE tracking (existing)
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- Signal quality scoring v4 (Nov 11)
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### 🔜 Needs Implementation (After data analysis)
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- Quality threshold adjustments
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- Quality-based position sizing
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- ATR-based TP/SL calculation (optional toggle)
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- Hybrid target system (max of fixed % or ATR)
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### 📝 Configuration Ready
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All systems have ENV variables and config structure ready:
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- `MIN_SIGNAL_QUALITY_SCORE` (adjustable after analysis)
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- `TAKE_PROFIT_1_SIZE_PERCENT` (70% default, adjustable)
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- `USE_ATR_BASED_TARGETS` (false, will enable after backtest)
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- `TP1_ATR_MULTIPLIER`, `TP2_ATR_MULTIPLIER`, `SL_ATR_MULTIPLIER`
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---
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## 📈 Progress Tracking
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### Weekly Check-in Questions
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1. How many trades executed this week?
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2. How many blocked signals collected?
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3. Any patterns emerging in blocked signals?
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4. What's the current win rate vs target 60%?
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5. Are MFE/MAE averages improving?
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### Monthly Review Questions
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1. Do we have enough data for next optimization phase?
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2. What's the biggest win/loss this month and why?
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3. Is indicator v6 outperforming v5? (need 20+ trades each)
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4. Should we adjust any thresholds based on patterns?
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5. Are we on track for Phase 1 goal ($2,500)?
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---
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## 🚨 Risk Management
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### Don't Optimize Too Early
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- ❌ Bad: Change thresholds after 5 trades and 2 blocked signals
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- ✅ Good: Wait for 20 blocked signals + 50 trades minimum
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- Statistical significance matters!
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### Keep Historical Baseline
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- Always compare against "what would the old system do?"
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- Track `signalQualityVersion` and `indicatorVersion` for this
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- Can revert if changes make things worse
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### A/B Test Before Full Deploy
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- Test new thresholds on 50% of signals (coin flip)
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- Compare results after 20-30 trades
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- Only deploy if statistically better (p < 0.05)
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---
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## 📚 Related Documentation
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**Core System:**
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- `copilot-instructions.md` - Full system architecture
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- `TRADING_GOALS.md` - 8-phase financial roadmap ($106 → $1M+)
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- `ATR_TRAILING_STOP_FIX.md` - Dynamic trailing stop implementation
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**Data Analysis:**
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- `BLOCKED_SIGNALS_TRACKING.md` - SQL queries for signal analysis
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- `docs/analysis/SIGNAL_QUALITY_VERSION_ANALYSIS.sql` - Version comparison queries
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**Implementation Guides:**
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- `SIGNAL_QUALITY_SETUP_GUIDE.md` - How signal scoring works
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- `PERCENTAGE_SIZING_FEATURE.md` - Position sizing system
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---
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## 🎯 Next Actions
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### Immediate (This Week)
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1. ✅ Deploy indicator v6 to TradingView production
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2. 🔄 Execute 10-15 trades to start data collection
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3. 📊 Monitor blocked signals (target: 2-3 this week)
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4. 🎯 Verify current trade closes correctly with new fixes
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### Short Term (2-3 Weeks)
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1. Collect 10-20 blocked signals
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2. Run signal quality analysis
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3. Adjust MIN_SIGNAL_QUALITY_SCORE if data shows improvement
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4. Continue collecting ATR data (target: 30-40 trades)
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### Medium Term (4-8 Weeks)
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1. Run position scaling backtest (50+ trades)
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2. Run ATR-based TP backtest (50+ trades)
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3. Implement quality-based position sizing
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4. A/B test ATR-based targets vs fixed %
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5. Deploy winning strategies
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### Long Term (Phase 1 Complete)
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1. Document all optimizations in `copilot-instructions.md`
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2. Prepare for Phase 2 ($2,500 → $10,000)
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3. Consider new optimizations:
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- Time-of-day filtering
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- Symbol-specific thresholds
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- Volatility regime detection
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- Machine learning for signal scoring
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---
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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. 📊🚀
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