023c62da28
Pre-existing test bug: line 164 asserted `ef >= 0.05` (post-Kelly-cap eval_dist), which gates on Kelly cold-start warmup completing within the smoke's training horizon — not on whether the Q-head learned to prefer Full magnitude. On the local-laptop smoke (1 quarter MBP-10), Kelly warmup never completes, pinning eval_dist[Full] = 0 even when Q(Full) > Q(Half) clearly. Per `project_magnitude_eval_collapse_kelly_capped`, the diagnostic split landed in #212: intent_dist measures policy learning, eval_dist measures policy + Kelly-cap. Tests asserting on Q-learning success must use intent_dist; the test was never updated. Smoke now PASSES with intent_full=0.057 (intent_full=0.866 in the prior re-run — both above the 0.05 threshold; Q(Full) is clearly preferred when Kelly cap doesn't suppress it). 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;