f4295e6902
Three fixes for production readiness: 1. Direction Flat floor: 30% unconditional Flat probability prevents over-trading (was 7% Flat = 93% directional = massive cost drag). Hard constraint, not Q-dependent, can't snowball. Combined with hold enforcement (min_hold_bars=10), actual Flat is ~13%. 2. MSE loss online_eq + target_eq: consistent mean-logit for d<=1. Both sides of TD error use the same representation — no mismatch. Removes the last C51 softmax bias from magnitude gradient path. Half grows 2.7%→5.7%, Full grows 2.7%→5.3% across epochs. 3. compute_expected_q: mean-logit for d<=1 (action selection + eval). Safe — not in training loss path. Result: 7/7 diversity sustained, Flat stable at 13%, Sharpe positive at 3/4 epochs. 19/19 smoke tests pass. 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;