548737a936
- VPIN Calculator: implement real tick-rule classification (was entirely stubbed) - Correlation matrix: replace hardcoded 0.5 with Pearson from log-returns - Stress test: per-factor accumulation instead of single-max shortcut - EnsembleModel: delete dead code, redirect to MockModel with warning - Coordinator fallbacks: relabel fake "REAL" predictions as FALLBACK SIMULATION - Enhanced ML: add warn!() to 5 stub endpoints, fix retrain status code - Autonomous scaling: add warn!() to mock ml_confidence and diversification - Position limiter: add warn!() for unused portfolio_id Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
adaptive-strategy
Adaptive trading strategy framework combining ensemble ML models, execution algorithms, market microstructure analysis, and risk management.
Overview
AdaptiveStrategy is the top-level type. It wraps an EnsembleCoordinator in Arc<RwLock> and coordinates predictions from four model types -- DQN, PPO, TFT, and Mamba2 -- loaded via InferenceAdapterBridge from the ml crate.
EnsembleConfig is re-exported from ml (not defined in this crate).
Modules
- config -- Strategy configuration, seeded IDs (
default-production,development,aggressive) - ensemble --
EnsembleCoordinator, model weighting, signal aggregation - execution -- TWAP, VWAP, Implementation Shortfall, POV, Arrival Price algorithms
- microstructure -- Order book analysis, trade flow classification, price impact modeling
- regime -- Market regime detection (HMM, threshold, ML-based)
- risk -- Position sizing (Kelly, risk parity, vol targeting), portfolio limits, drawdown monitoring
Features
| Cargo feature | Description |
|---|---|
postgres |
Hot-reload strategy config from PostgreSQL |
Usage
use adaptive_strategy::{AdaptiveStrategy, StrategyConfig};
let config = StrategyConfig::default();
let strategy = AdaptiveStrategy::new(config).await?;
Testing
SQLX_OFFLINE=true cargo test -p adaptive-strategy --lib
Dependencies
Core: tokio, candle-core, serde, tracing, chrono
ML models provided by the ml crate.