1f6a8436d3
Fundamental shift: reward optimizes Sharpe ratio contribution per trade instead of directional PnL magnitude. Targets Sharpe 5+ via many small consistent gains (spread capture, execution quality) instead of few large directional bets. DSR implementation (Moody & Saffell, 2001): - Per-episode EMA statistics in portfolio state slots [3]-[5] - DSR = (B*R - 0.5*A*R²) / (B - A²)^1.5 at trade completion - 10-trade warmup, clamped [-5, +5] - w_dsr=5.0 (primary reward signal) Reward hierarchy restructured: - DSR: 5.0 (NEW — primary, rewards Sharpe consistency) - Directional PnL: 2.0 (was 10.0 — demoted to secondary) - Order credit: 1.0 (was 0.1 — spread capture amplified 10×) - Urgency credit: 0.5 (was 0.1 — fill quality amplified 5×) - Inventory penalty: -0.005×|pos|/max_pos per bar (NEW) - Dense micro-reward: 0.0 (removed — noisy direction signal) Expected: WinRate increases (many small spread captures), per-trade variance decreases, Sharpe rises from consistency not prediction. 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;