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/SIGNAL_QUALITY_OPTIMIZATION_ROADMAP.md
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# Signal Quality Optimization Roadmap
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**Goal:** Optimize signal quality thresholds and scoring logic using data-driven analysis
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**Current Status:** Phase 4 Complete ✅ - Automated price tracking operational | Phase 1-2 Active - Collecting + analyzing data
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**Last Updated:** November 24, 2025
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**🎉 MAJOR UPDATE: Phase 4 Complete (Nov 19-22, 2025)**
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- BlockedSignalTracker deployed and fully operational
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- 34 signals tracked: 21 multi-timeframe + 13 quality-blocked
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- Price tracking at 1/5/15/30 min intervals with TP/SL hit detection
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- First validation: Quality 80 signal blocked, would've profited +0.52%
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**🚀 v8 Money Line Indicator (Nov 18, 2025)**
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- Sticky trend detection with 0.6% flip threshold + momentum confirmation
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- 8 trades completed: 57.1% WR, +$262.70 total, 93.6 avg quality
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- Perfect quality separation: All winners ≥95, all losers ≤90
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---
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## Overview
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This roadmap guides the systematic improvement of signal quality filtering. We follow a **data-first approach**: collect evidence, analyze patterns, then make changes. No premature optimization.
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### Current System
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- **Quality Score Threshold:** 91 points (raised from 81 on Nov 21, 2025 after trade #7 analysis)
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- **Trade #7:** ADX 19.0, quality 90 → -$387 loss (weak trend, emergency stop)
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- **Perfect Separation:** Analysis of 7 v8 trades:
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- ALL 4 winners had quality ≥95 (95, 95, 100, 105) ✅
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- ALL 3 losers had quality ≤90 (80, 90, 90) ❌
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- 91 threshold would have prevented 100% of losses (-$624.90)
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- **v8 Performance:** 7 trades, 57.1% WR, $262.70 total, 93.6 avg quality
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- **Historical Performance (v5/v6):** 157 total, +$3.43 P&L, 44.5% WR
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---
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## Phase 1: Data Collection (CURRENT) ✅ IN PROGRESS
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**Status:** Infrastructure complete, collecting data
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**Started:** November 11, 2025
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**Target:** Collect 10-20 blocked signals (1-2 weeks)
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### Completed (Nov 11, 2025)
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- [x] Created `BlockedSignal` database table
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- [x] Implemented automatic saving in check-risk endpoint
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- [x] Deployed to production (trading-bot-v4 container)
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- [x] Created tracking documentation (BLOCKED_SIGNALS_TRACKING.md)
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### What's Being Tracked
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Every blocked signal captures:
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- **Metrics:** ATR, ADX, RSI, volume ratio, price position, timeframe
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- **Score:** Quality score (0-100), version, detailed breakdown
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- **Block Reason:** Quality score, cooldown, hourly limit, daily drawdown
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- **Context:** Symbol, direction, price at signal time, timestamp
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### What We're Looking For
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1. How many signals score 60-64 (just below threshold)?
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2. What are their characteristics (ADX, ATR, price position)?
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3. Are there patterns (extreme positions, specific timeframes)?
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4. Do they cluster around specific block reasons?
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### Phase 1 Completion Criteria
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- [ ] Minimum 10 blocked signals with quality scores 55-64
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- [ ] At least 2 signals in 60-64 range (close calls)
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- [ ] Mix of block reasons (not all quality score)
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- [ ] Data spans multiple market conditions (trending, choppy, volatile)
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### SQL Queries for Phase 1
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```sql
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-- Check progress
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SELECT COUNT(*) as total_blocked
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FROM "BlockedSignal";
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-- Score distribution
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SELECT
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CASE
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WHEN signalQualityScore >= 60 THEN '60-64 (Close)'
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WHEN signalQualityScore >= 55 THEN '55-59 (Marginal)'
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WHEN signalQualityScore >= 50 THEN '50-54 (Weak)'
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ELSE '0-49 (Very Weak)'
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END as tier,
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COUNT(*) as count
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FROM "BlockedSignal"
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WHERE blockReason = 'QUALITY_SCORE_TOO_LOW'
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GROUP BY tier
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ORDER BY MIN(signalQualityScore) DESC;
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```
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---
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## Phase 1.5: Signal Frequency Penalties ✅ DEPLOYED Nov 14, 2025
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**Status:** Production deployment complete (with critical bug fix)
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**Initial Commit:** 111e3ed (07:28 CET)
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**Bug Fix Commit:** 795026a (09:22 CET)
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**Container:** trading-bot-v4
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### What Was Implemented
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Real-time database analysis that detects overtrading and flip-flop patterns before trade execution.
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### Penalties Applied
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1. **Overtrading (3+ signals in 30min):** -20 points
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- Counts both executed trades AND blocked signals
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- Prevents excessive trading in consolidation zones
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2. **Flip-flop with price context (opposite direction <15min):** -25 points
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- **NEW:** Only applies if price moved <2% from opposite signal
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- Distinguishes chop from reversals
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- Example chop: $154.50 SHORT → $154.30 LONG (0.13% move) = blocked ⚠️
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- Example reversal: $170 SHORT → $153 LONG (10% move) = allowed ✅
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- Blocks rapid long→short→long whipsaws in tight ranges
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- **BUG FIX (795026a):** Now uses Pyth price data for accurate calculations
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- Previous issue: showed "100% move" for 0.2% actual movement (allowed false flip-flop)
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3. **Alternating pattern (last 3 trades):** -30 points
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- Detects choppy market conditions
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- Pattern: long→short→long = chop detection
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### Technical Details
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- **Function:** `getRecentSignals()` in `lib/database/trades.ts`
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- **Price source:** Pyth price monitor via `getPythPriceMonitor()` in check-risk
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- **Architecture:** `scoreSignalQuality()` now async
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- **Endpoints updated:** check-risk, execute, reentry-check
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- **Performance:** Indexed queries, <10ms overhead
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- **Validation:** Logs "🔍 Flip-flop price check: $X → $Y = Z%" for debugging
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### Expected Impact
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- Eliminate tight-range flip-flops (Nov 14 chart: $141-145 SOL)
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- Reduce overtrading during sideways markets
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- Target: +5-10% win rate improvement
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- Better capital preservation in chop
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### Monitoring
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Watch for detailed penalty and allowance messages in logs:
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```
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🔍 Flip-flop price check: $143.86 → $143.58 = 0.20%
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⚠️ Overtrading zone: 3 signals in 30min (-20 pts)
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⚠️ Flip-flop in tight range: 4min ago, only 0.20% move ($143.86 → $143.58) (-25 pts)
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✅ Direction change after 10.0% move ($170.00 → $153.00, 12min ago) - reversal allowed
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⚠️ Chop pattern: last 3 trades alternating (long → short → long) (-30 pts)
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```
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### Known Issues Fixed
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- **Nov 14, 06:05 CET:** Initial deployment allowed 0.2% flip-flop due to incorrect price data
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- Trade: SHORT at $143.58 (should have been blocked)
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- Result: -$1.56 loss in 5 minutes
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- Fix deployed: 09:22 CET (795026a) - now uses Pyth price monitor
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|
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### Validation Plan
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1. Monitor next 5-10 signals for accurate price calculations
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2. Verify penalties trigger with correct percentages
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3. Analyze if blocked signals would have lost
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4. If effective, proceed to Phase 6 (range compression)
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|
||||
---
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|
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## Phase 2: Pattern Analysis 🔜 NEXT
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**Prerequisites:** 10-20 blocked signals collected
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**Estimated Duration:** 2-3 days
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**Owner:** Manual analysis + SQL queries
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|
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### Analysis Tasks
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#### 2.1: Score Distribution Analysis
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```sql
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-- Analyze blocked signals by score range
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SELECT
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CASE
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WHEN signalQualityScore >= 60 THEN '60-64'
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WHEN signalQualityScore >= 55 THEN '55-59'
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ELSE '50-54'
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END as score_range,
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COUNT(*) as count,
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ROUND(AVG(atr)::numeric, 2) as avg_atr,
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ROUND(AVG(adx)::numeric, 1) as avg_adx,
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ROUND(AVG(pricePosition)::numeric, 1) as avg_price_pos,
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ROUND(AVG(volumeRatio)::numeric, 2) as avg_volume
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FROM "BlockedSignal"
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WHERE blockReason = 'QUALITY_SCORE_TOO_LOW'
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GROUP BY score_range
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ORDER BY MIN(signalQualityScore) DESC;
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```
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#### 2.2: Compare with Executed Trades
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```sql
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-- Find executed trades with similar scores to blocked signals
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SELECT
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'Executed' as type,
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signalQualityScore,
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COUNT(*) as trades,
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ROUND(AVG(realizedPnL)::numeric, 2) as avg_pnl,
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ROUND(100.0 * SUM(CASE WHEN realizedPnL > 0 THEN 1 ELSE 0 END) / COUNT(*)::numeric, 1) as win_rate
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FROM "Trade"
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WHERE exitReason IS NOT NULL
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AND signalQualityScore BETWEEN 60 AND 69
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GROUP BY signalQualityScore
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ORDER BY signalQualityScore;
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```
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|
||||
#### 2.3: ADX Pattern Analysis
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Key finding from existing data: ADX 20-25 is a trap zone!
|
||||
```sql
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-- ADX distribution in blocked signals
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SELECT
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CASE
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WHEN adx >= 25 THEN 'Strong (25+)'
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WHEN adx >= 20 THEN 'Moderate (20-25)'
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WHEN adx >= 15 THEN 'Weak (15-20)'
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ELSE 'Very Weak (<15)'
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END as adx_tier,
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COUNT(*) as count,
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ROUND(AVG(signalQualityScore)::numeric, 1) as avg_score
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FROM "BlockedSignal"
|
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WHERE blockReason = 'QUALITY_SCORE_TOO_LOW'
|
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AND adx IS NOT NULL
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GROUP BY adx_tier
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ORDER BY MIN(adx) DESC;
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||||
```
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||||
|
||||
#### 2.4: Extreme Position Analysis
|
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Test hypothesis: Extremes (<10% or >90%) need different thresholds
|
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```sql
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-- Blocked signals at range extremes
|
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SELECT
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direction,
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signalQualityScore,
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ROUND(pricePosition::numeric, 1) as pos,
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ROUND(adx::numeric, 1) as adx,
|
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ROUND(volumeRatio::numeric, 2) as vol
|
||||
FROM "BlockedSignal"
|
||||
WHERE blockReason = 'QUALITY_SCORE_TOO_LOW'
|
||||
AND (pricePosition < 10 OR pricePosition > 90)
|
||||
ORDER BY signalQualityScore DESC;
|
||||
```
|
||||
|
||||
### Phase 2 Deliverables
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||||
- [ ] Score distribution report
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||||
- [ ] ADX pattern analysis
|
||||
- [ ] Extreme position analysis
|
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- [ ] Comparison with executed trades
|
||||
- [ ] **DECISION:** Keep threshold at 65, lower to 60, or implement dual-threshold system
|
||||
|
||||
---
|
||||
|
||||
## Phase 3: Implementation (Conditional) 🎯 FUTURE
|
||||
|
||||
**Trigger:** Analysis shows clear pattern worth exploiting
|
||||
**Prerequisites:** Phase 2 complete + statistical significance (15+ blocked signals)
|
||||
|
||||
### Option A: Dual-Threshold System (Recommended)
|
||||
**IF** data shows extreme positions (price <10% or >90%) with scores 60-64 are profitable:
|
||||
|
||||
**Implementation:**
|
||||
```typescript
|
||||
// In check-risk endpoint
|
||||
const isExtremePosition = pricePosition < 10 || pricePosition > 90
|
||||
const requiredScore = isExtremePosition ? 60 : 65
|
||||
|
||||
if (qualityScore.score < requiredScore) {
|
||||
// Block signal
|
||||
}
|
||||
```
|
||||
|
||||
**Changes Required:**
|
||||
- `app/api/trading/check-risk/route.ts` - Add dual threshold logic
|
||||
- `lib/trading/signal-quality.ts` - Add `isExtremePosition` helper
|
||||
- `config/trading.ts` - Add `minScoreForExtremes` config option
|
||||
- Update AI instructions with new logic
|
||||
|
||||
### Option B: ADX-Based Gates (Alternative)
|
||||
**IF** data shows strong ADX trends (25+) with lower scores are profitable:
|
||||
|
||||
**Implementation:**
|
||||
```typescript
|
||||
const requiredScore = adx >= 25 ? 60 : 65
|
||||
```
|
||||
|
||||
**Changes Required:**
|
||||
- Similar to Option A but based on ADX threshold
|
||||
|
||||
### Option C: Keep Current (If No Clear Pattern)
|
||||
**IF** data shows no consistent profit opportunity in blocked signals:
|
||||
- No changes needed
|
||||
- Continue monitoring
|
||||
- Revisit in 20 more trades
|
||||
|
||||
### Phase 3 Checklist
|
||||
- [ ] Decision made based on Phase 2 analysis
|
||||
- [ ] Code changes implemented
|
||||
- [ ] Updated signalQualityVersion to 'v5' in database
|
||||
- [ ] AI instructions updated
|
||||
- [ ] Tested with historical blocked signals
|
||||
- [ ] Deployed to production
|
||||
- [ ] Monitoring for 10 trades to validate improvement
|
||||
|
||||
---
|
||||
|
||||
## Phase 4: Price Analysis Automation ✅ COMPLETE
|
||||
|
||||
**Goal:** Automatically track if blocked signals would have been profitable
|
||||
**Status:** Deployed Nov 19-22, 2025 - Fully operational
|
||||
**Implementation:** BlockedSignalTracker background service
|
||||
|
||||
### Architecture ✅
|
||||
```
|
||||
BlockedSignalTracker (runs every 5 min)
|
||||
↓
|
||||
Fetch BlockedSignal records where:
|
||||
- analysisComplete = false
|
||||
- createdAt within last 30 minutes
|
||||
↓
|
||||
For each signal (both DATA_COLLECTION_ONLY and QUALITY_SCORE_TOO_LOW):
|
||||
- Query current price from Drift oracle
|
||||
- Calculate profit % based on direction
|
||||
- Check if TP1 (~0.86%), TP2 (~1.72%), SL (~1.29%) would have hit
|
||||
- Update priceAfter1Min/5Min/15Min/30Min
|
||||
- Track maxFavorableExcursion and maxAdverseExcursion
|
||||
- Set wouldHitTP1/TP2/SL flags
|
||||
- Mark analysisComplete = true after 30 minutes
|
||||
↓
|
||||
Save results back to database
|
||||
```
|
||||
|
||||
### Implementation Complete ✅
|
||||
- [x] Created BlockedSignalTracker service (`lib/analysis/blocked-signal-tracker.ts`)
|
||||
- [x] Price tracking from Drift oracle (not history, but real-time monitoring)
|
||||
- [x] TP/SL hit calculation using ATR-based targets
|
||||
- [x] Background job running every 5 minutes
|
||||
- [x] Auto-starts on container startup
|
||||
- [x] API endpoint (`/api/analytics/signal-tracking`) for monitoring
|
||||
- [x] **Enhanced Nov 22:** Tracks both multi-timeframe AND quality-blocked signals
|
||||
|
||||
### Current Data (Nov 24, 2025)
|
||||
- **34 total blocked signals tracked**
|
||||
- 21 multi-timeframe (DATA_COLLECTION_ONLY): 19 complete
|
||||
- 13 quality-blocked (QUALITY_SCORE_TOO_LOW): 6 complete
|
||||
- **First validation result:** Quality 80 signal would have profited +0.52% (+$43)
|
||||
|
||||
### Success Metrics ✅
|
||||
- System validates if blocks were correct (SL hits) vs missed opportunities (TP hits)
|
||||
- Enables data-driven threshold optimization
|
||||
- Risk-free analysis of non-executed signals
|
||||
|
||||
---
|
||||
|
||||
## Phase 5: ML-Based Scoring (DISTANT FUTURE) 🤖
|
||||
|
||||
**Status:** Future consideration - requires extensive data
|
||||
**Estimated Start:** Q2 2026 or later
|
||||
**Prerequisites:** 500+ trades with quality scores, proven manual optimization
|
||||
|
||||
### Approach
|
||||
1. Build ML model to predict trade success from metrics
|
||||
2. Use predicted success rate as quality score
|
||||
3. Continuously learn from new trades
|
||||
4. Auto-adjust weights based on market regime
|
||||
|
||||
### Requirements
|
||||
- Sufficient training data (500+ trades minimum)
|
||||
- Market regime classification system
|
||||
- ML infrastructure (model training, deployment)
|
||||
- Monitoring for model drift
|
||||
|
||||
### Risks
|
||||
- Overfitting to past data
|
||||
- Model degrades as markets change
|
||||
- Black box decision-making
|
||||
- Increased system complexity
|
||||
|
||||
**Note:** Only pursue if manual optimization plateaus or if pursuing ML as learning exercise.
|
||||
|
||||
---
|
||||
|
||||
## Phase 6: TradingView Range Compression Metrics (PLANNED) 📏
|
||||
|
||||
**Status:** Planned - Next after frequency penalties prove effective
|
||||
**Estimated Start:** November 2025 (after Phase 1 validation)
|
||||
**Prerequisites:** Phase 1 deployed, 5-10 signals monitored
|
||||
|
||||
### What It Does
|
||||
Adds NEW metrics to TradingView alerts to detect range compression and momentum mismatches that indicate choppy markets.
|
||||
|
||||
### New TradingView Calculations
|
||||
```javascript
|
||||
// 1. Range compression (20-bar high/low range as % of price)
|
||||
rangePercent = ((highest(high, 20) - lowest(low, 20)) / close) * 100
|
||||
|
||||
// 2. Price change over 5 bars (momentum check)
|
||||
priceChange5bars = ((close - close[5]) / close[5]) * 100
|
||||
|
||||
// 3. ADX-momentum mismatch (ADX says trend but price not moving)
|
||||
adxMismatch = (adx > 15 AND abs(priceChange5bars) < 0.3)
|
||||
```
|
||||
|
||||
### Quality Score Penalties
|
||||
- **Range < 1.5× ATR**: -20 points (compressed range = chop likely)
|
||||
- **ADX 15+ but price change < 0.3%**: -20 points (fake trend signal)
|
||||
- **Price oscillating around MA**: -15 points (whipsaw zone)
|
||||
|
||||
### Why This Helps
|
||||
Current system can pass ADX 12-22 even when price just bouncing in tight zone. This detects the mismatch between "ADX says trending" vs "price says chopping."
|
||||
|
||||
**Example from Nov 14 chart:** Multiple signals in $141-145 range passed quality check despite obvious consolidation. Range compression would have caught this.
|
||||
|
||||
### Implementation Steps
|
||||
1. Add calculations to TradingView strategy (30 min)
|
||||
2. Update webhook JSON to include new fields (15 min)
|
||||
3. Modify `scoreSignalQuality()` to use range metrics (30 min)
|
||||
4. Test alerts on historical data (1 hour)
|
||||
5. Deploy and monitor (ongoing)
|
||||
|
||||
### Expected Impact
|
||||
- Catch "fake trends" where ADX misleads
|
||||
- Reduce entries in tight consolidation zones
|
||||
- Improve win rate by 3-5% in choppy markets
|
||||
- Complement frequency penalties (Phase 1)
|
||||
|
||||
### Success Metrics
|
||||
- Reduction in flip-flop losses
|
||||
- Fewer blocked signals in validated trending moves
|
||||
- Better P&L in sideways market conditions
|
||||
|
||||
---
|
||||
|
||||
## Phase 7: Volume Profile Integration (ADVANCED) 📊
|
||||
|
||||
**Status:** Future consideration - most complex but most powerful
|
||||
**Estimated Start:** December 2025 or Q1 2026
|
||||
**Prerequisites:** Phase 6 completed, Volume S/R Zones V2 indicator expertise
|
||||
|
||||
### What It Does
|
||||
Uses Volume S/R Zones V2 indicator to detect when price is stuck in high-volume consolidation nodes where flip-flops are most likely.
|
||||
|
||||
### How Volume Profile Works
|
||||
- Shows horizontal bars representing volume at each price level
|
||||
- **Volume nodes** (thick bars) = high volume = price gets stuck (S/R zones)
|
||||
- **Thin zones** (low volume) = price moves through quickly (breakout zones)
|
||||
- Price bouncing inside volume node = high probability chop
|
||||
|
||||
### Required Indicator Modifications
|
||||
Expose these values from Volume S/R Zones V2:
|
||||
```javascript
|
||||
// New indicator outputs (requires Pine Script modifications)
|
||||
inVolumeNode: true/false // Is price inside thick volume bar?
|
||||
nearVolumeEdge: true/false // Near top/bottom of node? (breakout setup)
|
||||
nodeStrength: 0-100 // Volume concentration at this level
|
||||
distanceFromNode: % // How far from nearest node?
|
||||
volumeNodeWidth: % // How wide is current node? (tight = strong)
|
||||
```
|
||||
|
||||
### Quality Score Adjustments
|
||||
**Penalties:**
|
||||
- **In volume node (stuck)**: -25 points (high chop probability)
|
||||
- **Node strength > 80**: -30 points (VERY strong S/R = rejection likely)
|
||||
- **Tight node width (<1%)**: -35 points (extreme consolidation)
|
||||
|
||||
**Bonuses:**
|
||||
- **Near volume edge + high volume**: +10 points (breakout setup)
|
||||
- **Distance from node > 2%**: +5 points (free movement zone)
|
||||
- **Breaking through node with volume**: +15 points (momentum trade)
|
||||
|
||||
### Why This Is Powerful
|
||||
**Example from Nov 14 chart:** Price bouncing $141-145. Volume profile would show THICK volume node at that exact level - instant warning. System would:
|
||||
1. Detect price in node → apply -25 to -35 penalty
|
||||
2. Block most entries in that zone
|
||||
3. Wait for breakout above/below node
|
||||
4. Bonus points when price clears node with volume
|
||||
|
||||
### Implementation Complexity
|
||||
**High - Requires:**
|
||||
1. Modify Volume S/R Zones indicator source code (Pine Script)
|
||||
2. Expose new variables in indicator settings
|
||||
3. Add webhook outputs from indicator (JSON formatting)
|
||||
4. Parse in n8n workflow (new data structure)
|
||||
5. Update quality scorer with volume logic (complex conditionals)
|
||||
6. Test on historical data with indicator overlays
|
||||
7. Validate against manual chart analysis
|
||||
|
||||
**Estimated Time:** 2-3 hours + TradingView Pine Script knowledge
|
||||
|
||||
### Risks & Considerations
|
||||
- Indicator must stay updated (TradingView updates can break)
|
||||
- Volume profile changes dynamically (recalculates with new data)
|
||||
- May over-filter in ranging markets (miss valid mean-reversion trades)
|
||||
- Complexity increases debugging difficulty
|
||||
|
||||
### Success Metrics
|
||||
- Elimination of entries in obvious consolidation zones
|
||||
- Higher win rate specifically in ranging markets
|
||||
- Reduction in whipsaw losses (target: 30-50% fewer)
|
||||
- Improved P&L per trade (better entries near node edges)
|
||||
|
||||
### Alternatives to Consider
|
||||
- Use simpler "volume concentration" metric (easier to calculate)
|
||||
- Implement fixed support/resistance zones instead of dynamic profile
|
||||
- Combine with Phase 6 range compression (may be sufficient)
|
||||
|
||||
**Recommendation:** Only implement if Phase 1 + Phase 6 don't adequately solve flip-flop problem. Volume profile is most powerful but also most fragile.
|
||||
|
||||
---
|
||||
|
||||
## Progress Tracking
|
||||
|
||||
---
|
||||
|
||||
## Key Principles
|
||||
|
||||
### 1. Data Before Action
|
||||
- Minimum 10 samples before any decision
|
||||
- Prefer 20+ for statistical confidence
|
||||
- No changes based on 1-2 outliers
|
||||
|
||||
### 2. Incremental Changes
|
||||
- Change one variable at a time
|
||||
- Test for 10-20 trades after each change
|
||||
- Revert if performance degrades
|
||||
|
||||
### 3. Version Tracking
|
||||
- Every scoring logic change gets new version (v4 → v5)
|
||||
- Store version with each trade/blocked signal
|
||||
- Enables A/B testing and rollback
|
||||
|
||||
### 4. Document Everything
|
||||
- Update this roadmap after each phase
|
||||
- Record decisions and rationale
|
||||
- Link to SQL queries and analysis
|
||||
|
||||
---
|
||||
|
||||
## Progress Tracking
|
||||
|
||||
### Milestones
|
||||
- [x] Nov 11, 2025: Phase 1 infrastructure complete (blocked signals tracking)
|
||||
- [x] Nov 14, 2025: Phase 1.5 complete (signal frequency penalties deployed)
|
||||
- [x] Nov 19, 2025: **Phase 4 COMPLETE** (multi-timeframe price tracking deployed)
|
||||
- [x] Nov 22, 2025: **Phase 4 ENHANCED** (quality threshold validation added)
|
||||
- [ ] Target: ~Nov 20-25, 2025: Phase 2 analysis complete (10-20 blocked signals)
|
||||
- [ ] Target: ~Nov 25-30, 2025: Phase 3 implementation (threshold adjustment)
|
||||
- [ ] Target: ~Dec 1-7, 2025: Phase 6 implementation (TradingView range metrics)
|
||||
- [ ] Target: ~Dec 15-31, 2025: Phase 7 evaluation (Volume Profile integration)
|
||||
- [ ] TBD: Phase 5 ML-based scoring (Q2 2026 or later)
|
||||
|
||||
### Metrics to Watch
|
||||
- **Blocked signals collected:** 34/10 minimum ✅ (21 multi-timeframe + 13 quality-blocked)
|
||||
- **Data collection complete:** 25/34 signals (73.5% completion rate) ✅
|
||||
- **Quality-blocked signals:** 13 collected, 6 complete (Nov 22 enhancement)
|
||||
- **Multi-timeframe signals:** 21 collected, 19 complete (Nov 19 deployment)
|
||||
- **First validation:** Quality 80 blocked signal would've profited +0.52% (+$43) - FALSE NEGATIVE
|
||||
- **Days of data collection:** 5 days (Nov 19-24) - ongoing
|
||||
- **Market conditions covered:** 3/3 (trending, choppy, volatile) ✅
|
||||
|
||||
### Review Schedule
|
||||
- **Weekly:** Check blocked signal count
|
||||
- **After 10 blocked:** Run Phase 2 analysis
|
||||
- **After Phase 2:** Decide on Phase 3 implementation
|
||||
- **Monthly:** Review overall system performance
|
||||
|
||||
---
|
||||
|
||||
## Questions to Answer
|
||||
|
||||
### Phase 1 Questions
|
||||
- [ ] How many signals get blocked per day?
|
||||
- [ ] What's the score distribution of blocked signals?
|
||||
- [ ] Are most blocks from quality score or other reasons?
|
||||
|
||||
### Phase 2 Questions
|
||||
- [ ] Do blocked signals at 60-64 have common characteristics?
|
||||
- [ ] Would lowering threshold to 60 improve performance?
|
||||
- [ ] Do extreme positions need different treatment?
|
||||
- [ ] Is ADX pattern valid in blocked signals?
|
||||
|
||||
### Phase 3 Questions
|
||||
- [ ] Did the change improve win rate?
|
||||
- [ ] Did it increase profitability?
|
||||
- [ ] Any unintended side effects?
|
||||
|
||||
---
|
||||
|
||||
## Appendix: Historical Context
|
||||
|
||||
### Why This Roadmap Exists
|
||||
**Date:** November 11, 2025
|
||||
|
||||
**Situation:** Three TradingView signals fired:
|
||||
1. SHORT at 05:15 - Executed (score likely 65+) → Losing trade
|
||||
2. LONG at 05:20 - Executed (score likely 65+) → Losing trade
|
||||
3. SHORT at 05:30 - **BLOCKED** (score 45) → Would have been profitable
|
||||
|
||||
**User Question:** "What can we do about this?"
|
||||
|
||||
**Analysis Findings:**
|
||||
- Only 2 historical trades scored 60-64 (both winners +$45.78)
|
||||
- Sample size too small for confident decision
|
||||
- ADX 20-25 is a trap zone (-$23.41 in 23 trades)
|
||||
- Low volume (<0.8x) outperforms high volume (counterintuitive!)
|
||||
|
||||
**Decision:** Build data collection system instead of changing thresholds prematurely
|
||||
|
||||
**This Roadmap:** Systematic approach to optimization with proper data backing
|
||||
|
||||
---
|
||||
|
||||
**Remember:** The goal isn't to catch every winning trade. The goal is to optimize the **risk-adjusted return** by catching more winners than losers at each threshold level. Sometimes blocking a potential winner is correct if it also blocks 3 losers.
|
||||
Reference in New Issue
Block a user