docs: Major documentation reorganization + ENV variable reference
**Documentation Structure:** - Created docs/ subdirectory organization (analysis/, architecture/, bugs/, cluster/, deployments/, roadmaps/, setup/, archived/) - Moved 68 root markdown files to appropriate categories - Root directory now clean (only README.md remains) - Total: 83 markdown files now organized by purpose **New Content:** - Added comprehensive Environment Variable Reference to copilot-instructions.md - 100+ ENV variables documented with types, defaults, purpose, notes - Organized by category: Required (Drift/RPC/Pyth), Trading Config (quality/ leverage/sizing), ATR System, Runner System, Risk Limits, Notifications, etc. - Includes usage examples (correct vs wrong patterns) **File Distribution:** - docs/analysis/ - Performance analyses, blocked signals, profit projections - docs/architecture/ - Adaptive leverage, ATR trailing, indicator tracking - docs/bugs/ - CRITICAL_*.md, FIXES_*.md bug reports (7 files) - docs/cluster/ - EPYC setup, distributed computing docs (3 files) - docs/deployments/ - *_COMPLETE.md, DEPLOYMENT_*.md status (12 files) - docs/roadmaps/ - All *ROADMAP*.md strategic planning files (7 files) - docs/setup/ - TradingView guides, signal quality, n8n setup (8 files) - docs/archived/2025_pre_nov/ - Obsolete verification checklist (1 file) **Key Improvements:** - ENV variable reference: Single source of truth for all configuration - Common Pitfalls #68-71: Already complete, verified during audit - Better findability: Category-based navigation vs 68 files in root - Preserves history: All files git mv (rename), not copy/delete - Zero broken functionality: Only documentation moved, no code changes **Verification:** - 83 markdown files now in docs/ subdirectories - Root directory cleaned: 68 files → 0 files (except README.md) - Git history preserved for all moved files - Container running: trading-bot-v4 (no restart needed) **Next Steps:** - Create README.md files in each docs subdirectory - Add navigation index - Update main README.md with new structure - Consolidate duplicate deployment docs - Archive truly obsolete files (old SQL backups) See: docs/analysis/CLEANUP_PLAN.md for complete reorganization strategy
This commit is contained in:
mindesbunister
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docs/roadmaps/OPTIMIZATION_MASTER_ROADMAP.md
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# Trading Bot Optimization - Master Roadmap
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**Last Updated:** November 26, 2025
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**Current Capital:** $540 USDC (zero debt, 100% health, 15x SOL leverage)
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**Phase 1 Goal:** $106 → $2,500 (60%+ win rate, aggressive compounding)
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|
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**🎯 Recent Progress:**
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||||
- ✅ **HA Infrastructure COMPLETE** (Nov 25, 2025): Automatic DNS failover with Hostinger secondary server validated in production. Zero-downtime failover/failback operational.
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- ✅ **v8 Indicator DEPLOYED** (Nov 22, 2025): 8 trades completed with perfect quality score separation (winners ≥95, losers ≤90). Quality threshold raised to 91.
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- ✅ **Multi-Timeframe Quality Scoring** (Nov 26, 2025): All timeframes now get real quality scores (not hardcoded 0), enabling cross-timeframe win rate comparison.
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---
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## 🏗️ Infrastructure: High Availability Setup
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**File:** [`HA_SETUP_ROADMAP.md`](./HA_SETUP_ROADMAP.md)
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### Status: ✅ COMPLETE - PRODUCTION READY
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**Completed:** November 25, 2025
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**Cost:** ~$20-30/month (secondary server + monitoring)
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**Uptime:** 99.9% guaranteed with automatic failover
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### Implementation
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- **Primary Server:** srvdocker02 (95.216.52.28) - trading-bot-v4:3001
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- **Secondary Server:** Hostinger (72.62.39.24) - trading-bot-v4-secondary:3001
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- **Database:** PostgreSQL streaming replication (asynchronous, <1s lag)
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- **Monitoring:** dns-failover-monitor systemd service (30s checks, 3 failure threshold)
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- **SSL:** nginx with HTTPS on both servers
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- **Firewall:** pfSense health check rules configured
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### Live Test Results (Nov 25, 21:53-22:00 CET)
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- ✅ Detection Time: 90 seconds (3 × 30s health checks)
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- ✅ Failover Execution: <1 second (DNS update via INWX API)
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- ✅ Downtime: **0 seconds** (seamless secondary takeover)
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- ✅ Failback: Automatic and immediate when primary recovers
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- ✅ Data Integrity: Zero trade loss, database fully replicated
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### Why This Matters
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- **24/7 Operations:** Bot continues trading even if primary server crashes
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- **Peace of Mind:** Automatic recovery while user sleeps/travels
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- **Financial Protection:** No missed trades during infrastructure issues
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- **Enterprise-Grade:** Same HA approach used by exchanges and financial platforms
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### Documentation
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- Complete deployment guide: `docs/DEPLOY_SECONDARY_MANUAL.md` (689 lines)
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- Architecture diagrams, setup procedures, monitoring, troubleshooting
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- Git commit: 99dc736 (November 25, 2025)
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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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✅ **v8 Indicator DEPLOYED AND VALIDATED** (Nov 18-22, 2025)
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- **Money Line v8:** Sticky trend detection with 0.6% flip threshold
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- **Live Results:** 8 trades completed (57.1% WR, +$262.70 total P&L)
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- **Perfect Separation:** ALL winners quality ≥95, ALL losers quality ≤90
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- **Threshold Raised:** 91 minimum (from 60) based on data validation
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- **Status:** Production system, quality 91+ filter active
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- **Tracking:** Database tags trades as `indicatorVersion='v8'` for comparison
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✅ **Phase 1.5 COMPLETE** - Signal Frequency Penalties Deployed (Nov 14)
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- **Overtrading penalty:** 3+ signals in 30min → -20 points
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- **Flip-flop penalty:** Opposite direction <15min → -25 points
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- **Alternating pattern:** Last 3 trades flip → -30 points
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- **Impact:** Eliminates tight-range flip-flops (like $141-145 consolidation)
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📊 **Phase 1 (IN PROGRESS)** - Blocked Signals Collection
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- **Progress:** 8/20 blocked signals collected (6 complete with price tracking)
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- **Started:** November 11, 2025
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- **Quality Threshold:** Raised to 91 (Nov 21) after perfect separation validation
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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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- Frequency penalties applied (overtrading, flip-flop, alternating)
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- Future: Price movement tracking (would it have hit TP1/TP2/SL?)
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|
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### Key Questions to Answer
|
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- Are frequency penalties catching flip-flops effectively?
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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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### Next Phases (Planned)
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- **Phase 6:** TradingView range compression metrics (Nov 2025)
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- Detects compressed ranges and ADX-momentum mismatches
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- ~1-2 hour implementation
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- **Phase 7:** Volume profile integration (Dec 2025 - Q1 2026)
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- Uses Volume S/R Zones V2 for consolidation detection
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- Most powerful but most complex
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### Timeline
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- **Phase 1.5:** ✅ Complete (Nov 14, 2025)
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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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- **Phase 6:** 1-2 hours (TradingView alert modifications)
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- **Phase 7:** 2-3 hours (volume profile integration)
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- **Total:** ~3-4 weeks
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### Success Metrics
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- Win rate improves by 5-10%+ (current: ~45% → target: 55-60%)
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- Eliminate flip-flop losses in consolidation zones
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- Fewer losing trades in 50-60% drawdown range
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- Maintain or increase trade frequency on valid trends
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|
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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: 170+ trades total (8 v8 trades, collecting data)
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|
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### What's Being Collected
|
||||
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
|
||||
- Actual TP1/TP2/SL hit vs theoretical optimal exits
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|
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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
|
||||
- **Phase 1:** ✅ COMPLETE
|
||||
- **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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|
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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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||||
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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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|
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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:** 8/50 trades with ATR tracking (v8 indicator)
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- **Started:** November 11, 2025 (with indicator v6)
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- **Median ATR:** 0.43 for SOL-PERP (from 162 historical trades)
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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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|
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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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|
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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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|
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### SQL Backtest Ready
|
||||
Complete backtest query in `ATR_BASED_TP_ROADMAP.md` (lines 100-150)
|
||||
|
||||
---
|
||||
|
||||
## 📅 Unified Timeline & Priorities
|
||||
|
||||
### Current Week (Nov 12-18, 2025)
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||||
**Focus:** Data collection for all three initiatives
|
||||
- ✅ 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)
|
||||
**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
|
||||
- **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)
|
||||
|
||||
---
|
||||
|
||||
## 🎯 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
|
||||
|
||||
---
|
||||
|
||||
## 📊 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)
|
||||
|
||||
---
|
||||
|
||||
## 🔧 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`
|
||||
|
||||
---
|
||||
|
||||
## 📈 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)?
|
||||
|
||||
---
|
||||
|
||||
## 🚨 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)
|
||||
|
||||
---
|
||||
|
||||
## 📚 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
|
||||
|
||||
---
|
||||
|
||||
## 🎯 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
|
||||
|
||||
---
|
||||
|
||||
## 🚀 v9 Development Ideas (Nov 22, 2025 - Data Analysis)
|
||||
|
||||
**Based on 8 v8 trades + 36 v5/v6 archived trades pattern analysis**
|
||||
|
||||
### 1. Directional Filter (HIGHEST PRIORITY)
|
||||
**Pattern Discovered:**
|
||||
- **v8 LONGS:** 100% WR (3/3), +$565.03, Quality 98.3 avg, 174% avg MFE
|
||||
- **v8 SHORTS:** 40% WR (2/5), -$283.54, Quality 91.0 avg, 23% avg MFE
|
||||
- **v5/v6:** Shorts consistently outperform longs (50-60% WR vs 20-40%)
|
||||
|
||||
**Hypothesis:** Longs perform better across all indicator versions (44 total trades)
|
||||
|
||||
**v9 Options:**
|
||||
- **Conservative:** Configurable `DIRECTIONAL_BIAS` setting (`long_only`, `short_only`, `both`)
|
||||
- **Aggressive:** Smart direction filter - only trade direction with 7-day rolling WR ≥60%
|
||||
- **Expected Impact:** Eliminate 60% of losses if pattern holds
|
||||
|
||||
**Data Needed:** 20 more v8 trades to validate (target: 28 total trades)
|
||||
|
||||
**Decision Point:** After trade #28, analyze long/short performance split
|
||||
|
||||
---
|
||||
|
||||
### 2. Time-of-Day Filter (MODERATE PRIORITY)
|
||||
**Pattern Discovered:**
|
||||
- **00-06 UTC (Asia):** 66.7% WR, +$241.43 (3 trades)
|
||||
- **18-24 UTC (After):** 100% WR, +$257.56 (1 trade)
|
||||
- **06-12 UTC (EU):** 0% WR, -$138.35 (1 trade)
|
||||
- **12-18 UTC (US):** 66.7% WR, -$79.15 (3 trades)
|
||||
|
||||
**Hypothesis:** Asia/After-hours sessions outperform EU/US overlap
|
||||
|
||||
**v9 Options:**
|
||||
- Configurable preferred trading hours (e.g., `TRADING_HOURS=0-6,18-24`)
|
||||
- Block signals during low-performing sessions
|
||||
- **Expected Impact:** ~15-20% improvement if pattern holds
|
||||
|
||||
**Data Needed:** 50+ trades to validate (sample size currently too small)
|
||||
|
||||
**Decision Point:** After 50 v8 trades, re-analyze time-of-day patterns
|
||||
|
||||
---
|
||||
|
||||
### 3. Quality-Based Emergency SL (SAFETY IMPROVEMENT)
|
||||
**Pattern Discovered:**
|
||||
- Trade cmi92gky: Quality 90, -$386.62 loss (-411% MAE)
|
||||
- Only emergency exit in 8 trades, but 137% of total losses
|
||||
- Emergency at -2% may be too generous for borderline quality signals
|
||||
|
||||
**Hypothesis:** Low-quality signals (90-94) need tighter emergency stops
|
||||
|
||||
**v9 Options:**
|
||||
- Quality ≥95: Emergency at -2.0% (strong signal, give room)
|
||||
- Quality 91-94: Emergency at -1.5% (moderate signal, tighter stop)
|
||||
- Quality <91: BLOCKED (already implemented)
|
||||
- **Expected Impact:** Cut worst-case losses by 25%
|
||||
|
||||
**Data Needed:** 10+ more trades in 91-94 quality range to validate
|
||||
|
||||
**Decision Point:** After 5+ emergency exits, analyze quality vs loss magnitude
|
||||
|
||||
---
|
||||
|
||||
### 4. Perfect Quality Threshold (ULTIMATE FILTER)
|
||||
**Pattern Discovered:**
|
||||
- Quality ≥95: 5 trades, 100% WR, +$906.39 (+$181/trade avg)
|
||||
- Quality ≤90: 3 trades, 0% WR, -$624.90 (-$208/trade avg)
|
||||
- **Perfect separation at 91 threshold validated**
|
||||
|
||||
**Hypothesis:** Raising to 95 after data collection = 100% WR
|
||||
|
||||
**v9 Options:**
|
||||
- Keep threshold 91 until trade #28 (data collection)
|
||||
- Raise to 95 after 20 more trades if 95+ pattern holds
|
||||
- Zero tolerance for borderline signals
|
||||
- **Expected Impact:** Potential 100% WR if pattern continues
|
||||
|
||||
**Data Needed:** 20 more trades (12 with quality ≥95 expected)
|
||||
|
||||
**Decision Point:** After trade #28, compare 91-94 vs 95+ performance
|
||||
|
||||
---
|
||||
|
||||
### 5. MFE/MAE-Based Position Sizing (ADVANCED)
|
||||
**Pattern Discovered:**
|
||||
- **Winners:** 137% avg MFE, -27% avg MAE (5:1 upside/downside)
|
||||
- **Losers:** 10% avg MFE, -176% avg MAE (1:18 ratio)
|
||||
- Winners move in our favor quickly, losers reverse hard
|
||||
|
||||
**Hypothesis:** Scale-in on early confirmation, fast-exit on early reversal
|
||||
|
||||
**v9 Options:**
|
||||
- Open 50% position initially
|
||||
- If profit ≥+0.5% within 5 minutes: Scale-in 50% more
|
||||
- If loss ≥-0.3% within 5 minutes: Close entire position (fast exit)
|
||||
- **Expected Impact:** Reduce loss exposure 50%, increase winner exposure 50%
|
||||
|
||||
**Data Needed:** 50+ trades with minute-by-minute price tracking
|
||||
|
||||
**Decision Point:** Phase 2 or Phase 3 (requires infrastructure changes)
|
||||
|
||||
---
|
||||
|
||||
### Development Timeline
|
||||
|
||||
**Current Phase (Trades 9-28):** Continue data collection, threshold 91
|
||||
- Validate directional bias pattern
|
||||
- Collect time-of-day data
|
||||
- Monitor quality ≥95 performance
|
||||
- Track emergency exits by quality tier
|
||||
|
||||
**After Trade #28:** Analyze patterns, decide v9 features
|
||||
- If long bias validates → Implement directional filter (v9a)
|
||||
- If time patterns validate → Add session filter (v9b)
|
||||
- If quality 95+ = 100% → Raise threshold (v9c)
|
||||
|
||||
**After Trade #50:** Advanced features
|
||||
- MFE/MAE-based scaling
|
||||
- Machine learning quality scoring
|
||||
- Adaptive emergency SL
|
||||
|
||||
**Priority Order (Impact × Ease):**
|
||||
1. **Directional Filter** - Highest impact if validated
|
||||
2. **Emergency SL by Quality** - High safety, easy implementation
|
||||
3. **Raise Threshold to 95** - Zero effort, high impact if pattern holds
|
||||
4. **Time-of-Day Filter** - Moderate impact, needs more data
|
||||
5. **MFE/MAE Scaling** - Advanced, requires infrastructure
|
||||
|
||||
---
|
||||
|
||||
**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. 📊🚀
|
||||
|
||||
**v9 Strategy:** Conservative approach - collect 20 more trades (target: 28 total), then make data-driven decisions about directional bias, quality thresholds, and safety improvements. Pattern recognition is powerful, but statistical significance requires larger sample sizes.
|
||||
Reference in New Issue
Block a user