cc55c8a25c
Plan C Phase 2 T8. The plan-prescribed pre-T2 snapshot approach was impossible (T2 had already landed); replacement strategy (ii) from the dispatch brief — behavioral parity vs an analytical Boltzmann reference computed in Rust — is used. Setup forces direction = Long (d=2) deterministically via peaked C51 logits (Long peaked at v=+0.8 atom; other directions at v=-0.5), so the kernel's Hold/Flat → mag_idx=0 short-circuit doesn't mask the magnitude branch's Boltzmann sampling. q_values are crafted with each branch (mag/ord/urg) peaked at a single bin with magnitude 1.0: Mag Q = [0.0, 0.0, 1.0] peak at Full (mag=2) Order Q = [1.0, 0.0, 0.0] peak at Market (ord=0) Urgency Q = [0.0, 1.0, 0.0] peak at urg=1 With q_range=1.0 in all three branches, tau collapses to 1.0 and the analytical Boltzmann probabilities are: P(best) = 1/(1 + 2/e) ≈ 0.5767 P(other) = 1/e/(1 + 2/e) ≈ 0.2117 Tolerance: at batch=8192 the 1-σ Bernoulli noise is ~0.0055 for p≈0.58; ±5% absolute tolerance covers ~9σ. Algorithmic divergence (e.g. an inadvertent strict-argmax substitution) would shift P(best) to 1.0 — trivially detected by the ±5% tolerance. Assertions: - dir_idx == Long for every sample (eval argmax E[Q] over peaked C51) - mag/ord/urg histograms each within ±5% of the Boltzmann reference 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;