02169e16e2
1. Risk management (CVaR, conviction, Kelly) re-enabled as ENVIRONMENT PHYSICS: - Agent observes scaling via portfolio state features - Learns to account for risk limits in its policy - No longer destroys credit assignment (scaling is physics, not action override) - Kelly uses half-Kelly (0.5x) for safety, activates after 20 trades 2. Backtest tx_cost now uses training's transaction_cost_multiplier from hyperopt (was hardcoded 0.1 bps — 17x lower than training). Training and eval see same costs. 3. Backtest env tx_cost formula expanded to match training: - Square-root market impact (Almgren-Chriss) - Order-type premiums (Market=0, IoC=+2bps, LimitMaker=-5bps) Result: first POSITIVE Sharpe (+0.0838) in project history. 134K trades. 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;