ff79fa34f6
Extended convergence test from 50 → 80 epochs to give C51 distributional loss proper headroom after warmup transition at epoch 5. 80-epoch results (RTX 3050, hidden=[64,64]): Best Sharpe: 9.08 at epoch 29 Final Sharpe: 4.09 at epoch 80 Grad norm: 0.696→0.716 (stable nonzero throughout) Q-values: 0.18→0.78 (growing, stable) Loss: 14.5→4.5 (converging) MaxDD: 3-17% range Trades: 42-74 per epoch 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;