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docs: Add multi-timeframe data collection documentation

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- Updated copilot instructions with data collection system overview
- Documents 5min execute vs 15min/1H/4H/Daily collect pattern
- Explains zero-risk parallel data collection approach
- Notes SQL analysis capability for timeframe optimization
- References implementation location in execute endpoint
- Part of v8 indicator testing and optimization strategy
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mindesbunister
2025-11-18 20:39:08 +01:00
parent 9b9d80779d
commit 5146f37acc
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.github/copilot-instructions.md vendored
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@@ -2627,6 +2627,15 @@ See `POSITION_SCALING_ROADMAP.md` for planned position management optimizations:
**Blocked Signals Tracking (Nov 11, 2025):** System now automatically saves all blocked signals to database for data-driven optimization. See `BLOCKED_SIGNALS_TRACKING.md` for SQL queries and analysis workflows.
**Multi-Timeframe Data Collection (Nov 18, 2025):** Execute endpoint now supports parallel data collection across timeframes:
- **5min signals:** Execute trades (production)
- **15min/1H/4H/Daily signals:** Save to BlockedSignal table with `blockReason='DATA_COLLECTION_ONLY'`
- Enables cross-timeframe performance comparison (which timeframe has best win rate?)
- Zero financial risk - non-5min signals just collect data for future analysis
- TradingView alerts on multiple timeframes → n8n passes `timeframe` field → bot routes accordingly
- After 50+ trades: SQL analysis to determine optimal timeframe for live trading
- Implementation: `app/api/trading/execute/route.ts` lines 106-145
**Data-driven approach:** Each phase requires validation through SQL analysis before implementation. No premature optimization.
**Signal Quality Version Tracking:** Database tracks `signalQualityVersion` field to compare algorithm performance: