f40ccc16a7
Combined spec/quality review caught two minor issues in the Pearl 6 commit. Both are mechanical fixes; no behavior change. 1. Test 12 (pearl_6_kelly_within_fold_ewma_blend) was missing an explicit assertion for slot 282 (TRADE_VAR_SMOOTH_IDX). The test setup initialized tvar_i32 and the launcher passed it through the kernel's parameter slot, but no assert! ever fired against the resulting ISV value. With n_envs=1, the kernel's `(kelly_count > 1) ? variance : 0.0f` branch returns 0 (no cross-env variance possible with 1 env), so EWMA blend yields 0.99 × 0.5 + 0.01 × 0.0 = 0.495. Added the missing assertion to close the within-fold coverage gap for s==2. 2. pearl_6_kelly_kernel.cu:136 doc comment said the slot computes "standard deviation of per-env Kelly fractions" but the code actually computes `ksum_sq / kelly_count` — i.e. the variance (second moment), not the standard deviation. The slot name TRADE_VAR_SMOOTH_INDEX correctly indicates variance; the comment was wrong. Updated comment to match: 'variance of per-env Kelly fractions' with explicit note that this is the second moment, NOT std-dev (no sqrtf applied). 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;