3c035ce1ae
Two coupled fixes to the vol-EMA regime detector exposed by walk-forward CV after all features were promoted to always-on: (1) Bootstrap window for vol_ref (slot 551 = REGIME_VOL_REF_SAMPLES) - Replace the Pearl-A "first observation replaces directly" bootstrap with a running mean over the first N=100 vol_ema observations, then switch to β-tracking. For IID observations the running-mean estimator has variance σ²/N — a 100-sample mean is 10× less noisy than the single-shot replace. (2) Permanent floor on vol_ref (slot 552 = REGIME_VOL_REF_FLOOR) - The bootstrap alone exposed the asymmetric deadband-deadlock: if vol_ref converged to a tiny value during a calm initial stretch, vol_ref / vol_ema fired the moment any realistic vol resumed and Kelly stayed trapped at regime_scale_floor=0.25 forever. Floor lives in ISV slot (TrainingPersist) with a hardcoded 1e-12 sub-floor inside the kernel as numerical-underflow guard. - Host seeds slot 552 with 1e-9. Future controller kernel will refine this from observed cell-level vol minima with cross-fold persistence. Walk-forward CV on Q1 fxcache (3 folds, window=700K, train_frac=0.6): cost fold-A fold-B fold-C mean ± SD 0.00 -19.77 +65.04 (100%) +6.45 +17.24 ± 43.42 Fold B turnaround is the headline: -47.64 (bootstrap-only) → +65.04 (bootstrap + floor) confirms the floor is the load-bearing fix. Cross-fold std-dev compressed 24% at cost=0; mean dropped from +38.94 (pre-defense) to +17.24 (with defense). Classic mean/variance trade. Block extended to 14 slots (539..=552). Kernel sig: vol_ref_floor moved from f32 scalar to vol_ref_floor_index i32, so the anchor is named/addressable in ISV. 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;