2f4bd8e58b
Diagnostic kernel printf in backtest_env_step_batch identified the gate
(Kelly cap warm-branch deadlock) and verified the fix
(0c9d1ee39: max(kelly_f, warmup_floor)) on train-4r6p8:
Before fix (train-4fpzx, fresh model epoch 0):
val_picked_dir_dist [short=0.19 hold=0.20 long=0.39 flat=0.21]
val_dir_dist [short=0.0001 hold=0.20 long=0.0000 flat=0.80]
trade_count = 23 over 214K bars (active_frac = 0.0001)
After fix (train-4r6p8, fresh model epoch 0):
val_picked_dir_dist [short=0.24 hold=0.17 long=0.42 flat=0.17]
val_dir_dist [short=0.24 hold=0.17 long=0.42 flat=0.17] 1:1
trade_count = 139,695 over 214K bars (active_frac = 0.6590)
Picked and realised distributions now bit-identical — every Boltzmann
pick translates faithfully to actual_dir.
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;