e7ce4395e8
Flamegraph of precompute_features on 1Q ES showed 62% of CPU time in zstd decompression, 6% in DBN FSM parsing, and only 2% in the actual feature math — single-threaded zstd was the bottleneck, not compute. Two fixes: 1. Per-quarter parallelism on the volume-bar trades loop (was sequential `for file in &trade_files`); brings it in line with the OFI path that already used par_iter. 2. Predecoded sidecar cache in `crates/ml-features/src/predecoded.rs`: first call to a `.dbn.zst` writes a bincode'd Vec<Mbp10Snapshot> or Vec<DbnTrade> under `<output_dir>/predecoded/`. Subsequent calls deserialize the sidecar and skip zstd entirely. An mtime+size header self-invalidates the sidecar when the source changes — no manual flush needed when a quarter is re-downloaded. Local 1Q ES results: - cold (writes sidecar): 40.7s (was 39.3s; +1.4s for write) - warm (HIT): 4.7s (8.7× faster) - zstd in flat perf: 62% → 0% of CPU samples - sidecar disk per Q: ~150MB The sidecar layer also auto-dedupes within a single run: the OFI section re-loads trades, but the second call hits the sidecar that the volume-bar section wrote moments earlier. CLI: `--rebuild-predecoded` purges sidecars for cold-path testing or after a wire-format change to Mbp10Snapshot / DbnTrade. Sidecars also self-invalidate on format-version mismatch so old caches are skipped silently rather than mis-deserializing. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
ml
10-model ML ensemble for the Foxhunt HFT system, built on Candle v0.9.1.
Models
- DQN (Rainbow) — deep Q-network with prioritized replay, dueling heads, noisy nets
- PPO — proximal policy optimization with GAE, LSTM policies, clip-higher
- TFT — temporal fusion transformer for multi-horizon forecasting
- Mamba2 — state space model for sequence prediction
- Liquid Networks — biologically inspired networks for non-stationary data
- TLOB — transformer-based limit order book analysis
- KAN — Kolmogorov-Arnold networks
- xLSTM — extended LSTM architecture
- TGGN — temporal graph neural network
- Diffusion — diffusion-based generative model
Key Modules
ensemble— model ensemble coordination and confidence aggregationhyperopt— PSO-based hyperparameter optimization with per-model adapterstrainers— unified training loops (DQN, PPO, supervised)inference—InferenceAdaptertrait for predictioncheckpoint— model checkpointing and restorationevaluation— walk-forward evaluation pipeline
Usage
use ml::dqn::DQN;
use ml::ppo::PpoTrainer;