dc2fcac103
4 priorities from risk module investigation:
P1: Kelly fraction sizing — new kernel parameter kelly_scale (f32).
Computed per-epoch on CPU, uploaded as scalar. Stacks with
CVaR × conviction: size = exposure × CVaR × conviction × kelly.
P3: Square-root market impact (Almgren & Chriss 2000) — replaced
quadratic x² with √x. Academic standard for futures markets.
Less penalty for moderate sizes, more realistic.
P4: Order-type tx cost differentiation — decode order_type branch
from factored action. Market=+0bps, IoC=+2bps, LimitMaker=-5bps.
LimitMaker rebate teaches model to prefer limit orders.
P2 (C51 VaR) deferred — IQN CVaR already provides this signal.
Position sizing chain is now:
target = exposure × max_pos × CVaR × conviction × kelly
cost = |delta| × price × (tx_mult × spread × √impact + order_premium)
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;