71a0275f54
Code-quality review caught that the two Pearl 2 unit tests verified each output (c51, iqn, cql, ens) against its analytical expected value within 1% relative tolerance, but did NOT assert the structural invariant `c51 + iqn + cql + ens ≈ 1.0` that the kernel maintains by construction (`ens = max(0, 1 - iqn - c51 - cql)`). A coefficient typo (e.g. BASE_IQN=0.111 instead of 0.11) would produce individually-plausible per-component values that all still pass the relative checks while quietly summing to 0.97 or 1.04. The sum check catches that class of regression. Adds `assert!((c51+iqn+cql+ens - 1.0).abs() < 1e-4)` inside both per-branch loops in the flat-regime and sharp-regime tests. Co-Authored-By: Claude Opus 4.7 (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;