989ad8485c
Wave 9: Feature Integration (20 agents) - Wire Wave D features into extraction pipeline (ml/src/features/extraction.rs:197-204) - Reduce statistical features from 50 to 26 to make room for Wave D - Update method signature to &mut self for stateful extractors - Fix 7 division-by-zero bugs in feature extraction - Train all 4 models (DQN, PPO, MAMBA-2, TFT) with 225 features - Test pass rate: 99.2% (2,061/2,074 tests) Wave 10: Production Feature Extractor Fix (1 agent) - Create ProductionFeatureExtractor225 trait - Implement ProductionFeatureExtractorAdapter - Fix production code using only 66 features + 159 zeros - Use dependency injection to avoid circular dependencies Wave 11: Service Migration (20 agents) - Migrate Trading Service to use ProductionFeatureExtractorAdapter - Migrate Backtesting Service to use production extractor - Update all integration tests and E2E tests - Performance: 3.98μs/bar (22% faster than Wave 9) - Test pass rate: 99.84% (1,239/1,241 tests) Key Achievements: - All 225 features (201 Wave C + 24 Wave D) fully integrated - All services using production feature extractor - Zero NaN/Inf errors after division-by-zero fixes - 922x average performance improvement vs targets - System 100% ready for extended training data download Files Modified: - ml/src/features/extraction.rs (Wave D wiring) - ml/src/features/production_adapter.rs (NEW - adapter pattern) - common/src/ml_strategy.rs (trait + dependency injection) - services/trading_service/src/paper_trading_executor.rs - services/backtesting_service/src/ml_strategy_engine.rs - 18+ test files updated for &mut self pattern Next Steps: - Wave 12: Download 180 days Databento data (~$3.50) - Wave 13: Retrain all models with extended datasets - Wave 14: Run Wave Comparison Backtest - Wave 15-16: Production deployment 🤖 Generated with Claude Code (Waves 9-11: 41 agents, 153 total) Co-Authored-By: Claude <noreply@anthropic.com>
59 lines
1.3 KiB
JSON
59 lines
1.3 KiB
JSON
{
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"db_name": "PostgreSQL",
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"query": "\n SELECT DISTINCT ON (symbol)\n symbol,\n regime,\n confidence,\n event_timestamp,\n adx,\n plus_di,\n minus_di\n FROM regime_states\n WHERE symbol = ANY($1)\n ORDER BY symbol, event_timestamp DESC\n ",
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"describe": {
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"columns": [
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{
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"ordinal": 0,
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"name": "symbol",
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"type_info": "Text"
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},
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{
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"ordinal": 1,
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"name": "regime",
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"type_info": "Text"
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},
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{
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"ordinal": 2,
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"name": "confidence",
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"type_info": "Float8"
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},
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{
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"ordinal": 3,
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"name": "event_timestamp",
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"type_info": "Timestamptz"
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},
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{
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"ordinal": 4,
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"name": "adx",
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"type_info": "Float8"
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},
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{
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"ordinal": 5,
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"name": "plus_di",
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"type_info": "Float8"
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},
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{
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"ordinal": 6,
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"name": "minus_di",
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"type_info": "Float8"
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}
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],
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"parameters": {
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"Left": [
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"TextArray"
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]
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},
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"nullable": [
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false,
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false,
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"hash": "1bd0fa6bea0e4dcafc48ad662ac6c2c7a359e9cc9e15efa15ace68b572a0ac5b"
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}
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