Updated documentation to reflect critical bug found and fixed:
SIGNAL_QUALITY_OPTIMIZATION_ROADMAP.md:
- Added bug fix commit (795026a) to Phase 1.5
- Documented price source (Pyth price monitor)
- Added validation and logging details
- Included Known Issues section with real incident details
- Updated monitoring examples with detailed price logging
.github/copilot-instructions.md:
- Added Common Pitfall #31: Flip-flop price context bug
- Documented root cause: currentPrice undefined in check-risk
- Real incident: Nov 14 06:05, -$1.56 loss from false positive
- Two-part fix with code examples (price fetch + validation)
- Lesson: Always validate financial calculation inputs
- Monitoring guidance: Watch for flip-flop price check logs
This ensures future AI agents and developers understand:
1. Why Pyth price fetch is needed in check-risk
2. Why validation before calculation is critical
3. The real financial impact of missing validation
Updated flip-flop penalty documentation:
- Added 2% price movement threshold explanation
- Included real-world examples (ETH chop vs reversal)
- Updated monitoring log examples to show both penalty and allowance
- Clarifies distinction between consolidation whipsaws and legitimate reversals
This documents the improvement implemented in commit 77a9437.
Added two new optimization phases for future implementation:
PHASE 6: TradingView Range Compression Metrics (PLANNED)
- Target: November 2025 (after frequency penalties validated)
- Adds range%, priceChange5bars, ADX-momentum mismatch to alerts
- Detects fake trends (ADX passes but price not moving)
- Penalties: -20 pts for compressed range, -20 pts for momentum mismatch
- Implementation: 1-2 hours (TradingView alert modifications)
PHASE 7: Volume Profile Integration (ADVANCED)
- Target: December 2025 or Q1 2026
- Uses Volume S/R Zones V2 indicator for volume node detection
- Identifies high-probability chop zones (price stuck in volume node)
- Penalties: -25 to -35 pts for volume node entries
- Bonuses: +10 to +15 pts for breakout setups
- Implementation: 2-3 hours + Pine Script expertise
- Most powerful but also most complex
Also documented Phase 1.5 completion (signal frequency penalties).
Milestones updated with realistic timelines for each phase.
- Add BlockedSignal table with 25 fields for comprehensive signal analysis
- Track all blocked signals with metrics (ATR, ADX, RSI, volume, price position)
- Store quality scores, block reasons, and detailed breakdowns
- Include future fields for automated price analysis (priceAfter1/5/15/30Min)
- Restore signalQualityVersion field to Trade table
Database changes:
- New table: BlockedSignal with indexes on symbol, createdAt, score, blockReason
- Fixed schema drift from manual changes
API changes:
- Modified check-risk endpoint to save blocked signals automatically
- Fixed hasContextMetrics variable scope (moved to line 209)
- Save blocks for: quality score too low, cooldown period, hourly limit
- Use config.minSignalQualityScore instead of hardcoded 60
Database helpers:
- Added createBlockedSignal() function with try/catch safety
- Added getRecentBlockedSignals(limit) for queries
- Added getBlockedSignalsForAnalysis(olderThanMinutes) for automation
Documentation:
- Created BLOCKED_SIGNALS_TRACKING.md with SQL queries and analysis workflow
- Created SIGNAL_QUALITY_OPTIMIZATION_ROADMAP.md with 5-phase plan
- Documented data-first approach: collect 10-20 signals before optimization
Rationale:
Only 2 historical trades scored 60-64 (insufficient sample size for threshold decision).
Building data collection infrastructure before making premature optimizations.
Phase 1 (current): Collect blocked signals for 1-2 weeks
Phase 2 (next): Analyze patterns and make data-driven threshold decision
Phase 3-5 (future): Automation and ML optimization
