448b61d095
The 4-branch DQN (direction x magnitude) had 3 degenerate variants (Short25, Flat, Long25) that all mapped to 0.0 target exposure when direction=Flat, causing 82% Flat collapse. Collapse these into a single Flat variant, giving 7 levels (ShortSmall/Half/Full, Flat, LongSmall/Half/Full) and 63 total factored actions (7x3x3). - ExposureLevel enum: 9 variants -> 7 (add direction/magnitude/from_dir_mag) - FactoredAction: 81 -> 63 total actions, from_index/to_index updated - DQN epsilon-greedy: use from_dir_mag() instead of dir*3+mag indexing - DQN config: num_actions default 9 -> 7 - PPO action space: 45 -> 63 actions, action masking updated - Signal adapter CUDA kernel: 5-bin -> 7-bin exposure aggregation - All tests updated for new variant names and index ranges Co-Authored-By: Claude Opus 4.6 (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;