d49003de6d
Closes the TrainingPersist loop for the regime defense per the KELLY_F_SMOOTH precedent (pearl_kelly_cap_signal_driven_floors). Three layers: (1) Per-step: alpha_regime_vol_update_kernel tracks min(vol_obs > 1e-12) in new slot 553 (REGIME_VOL_OBS_MIN_INDEX). Filtered against artifact- zero observations from stationary snapshots where curr == prev. (2) Per-cell: stacker_threshold_controller_update gains a fifth branch that reads vol_ref (slot 550) at cell-end, computes `target = floor_target_ratio × vol_ref`, slow-EMAs the floor anchor (slot 552) toward the target with rate `floor_update_rate` (0.1 per cell). Subfloor 1e-12 inside the kernel guards against the anchor collapsing to zero. (3) Per-invocation: alpha_baseline reads `config/ml/alpha_baseline_state.json` at startup and seeds slot 552 from the `regime_vol_ref_floor` field. At end of main(), the learned floor is written back via tmp+rename atomic write so concurrent walk-forward invocations see a consistent file. Matches the cross-fold-persistent shape of KELLY_F_SMOOTH. Block extended to 15 slots (539..=553). Smoke + kernel unit test pass -1/-1 for the new floor-controller indices (backward compat). Walk-forward CV verdict (Q1 fxcache, 3 sequential folds): iteration fold-A fold-B fold-C mean ± SD pre-defense (no regime) +91.52 -21.44 +46.74 +38.94 ± 56.88 hardcoded 1e-9 floor -19.77 +65.04 +6.45 +17.24 ± 43.42 learned floor (0.5 × cell_min) +74.78 -12.72 +8.80 +23.62 ± 45.59 learned floor (0.1 × vol_ref) -19.53 -26.51 +15.46 -10.20 ± 22.49 Controller infrastructure is structurally correct (loop closes, floor persists across invocations, kernel + disk + ISV all roundtrip). The TUNING is data-dependent — single-quarter CV doesn't have enough regime diversity to anchor the floor against. Multi-quarter fxcache validation is the next step (built cluster-side on the 9-quarter 2024-Q1..2026-Q1 ES futures dataset, downloaded as a single artifact for local CV). 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;