9223795c70
Brings the 4-commit work from wip/adaptive-learning-rootcause into main:24f96ab78fix(n1): distillation now actually pulls weights during collapsef21a7d661test(e1): 20-epoch smoke test + raw-q_gap assertion + disable early-stopa3fd02a95fix(early-stop): log real training epoch, not internal call counter9dbd8d7e9docs(design): unify training and validation environments Key outcomes: - Distillation mechanism now actually pulls weights during collapse (fixed three bugs: epoch-boundary grad_buf erasure, CUDA-graph scalar baking, wrong q_gap signal at snapshot gate). E1 smoke test passes deterministically. - Design doc lays out the next step: unifying training and validation env kernels so validation Sharpe tracks training Sharpe (currently diverges catastrophically by ~-150 absolute). - Incidental: early-stopping log now reports the real training epoch instead of its internal call counter. Co-Authored-By: Claude Opus 4.7 (1M context) <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;