ba269d7ce7
A: Switch backtest eval from Gumbel softmax to greedy argmax (batch_greedy_actions) so hyperopt Sharpe reflects the agent's actual learned policy, not noisy sampling. C: Disable reward normalization (enable_normalization=false). EMA normalizer with ±3.0 clipping was flattening the reward landscape, preventing the agent from distinguishing large winners from scratch trades. D: Wire tx_cost_bps (0.1 bps for IBKR ES) through to EvaluationEngine via new_with_fee_rate(). Previously hardcoded at 15 bps (150x mismatch with actual commission costs), massively penalizing every trade in backtest. E: Scale PnL reward by agent's target exposure in calculate_pnl_reward(). Previously, a Short100 action received POSITIVE reward when market went up (pct_return ignored position direction). Now: reward = pct_return × exposure. 2735 tests pass, 0 clippy warnings. Co-Authored-By: Claude Opus 4.6 <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;