31c610b69a
Phase 1 — Training quality (14 structural bugs): - Reward: sqrt micro-reward scaling, remove sparse normalization, Kelly penalty, additive OFI, magnitude-scaled capital floor penalty - Gradient: CQL 25→10%, IQN 60→75%, MSE magnitude 4×, 3-phase counterfactual, tau_anneal 100K→500K, epsilon floor 2%, PopArt fold reset, dd_threshold 0.5% - Monitoring: per-branch Q-value instrumentation - Hold: min_hold_bars 5→1 Phase 2 — VarStore elimination: - Deleted copy_weights_from from all network types - Deleted to_varstore from DuelingQNetwork + DistributionalDuelingQNetwork - Deleted branching_to_varstore, 7 extract/sync VarStore functions - Deleted update_target_networks, legacy CPU training paths - Deleted iqn_target_network, target_network fields - Deleted gpu_kernel_parity_test.rs (705 lines) - Refactored GpuExperienceCollector to single flat buffer - Added weight_sets_from_branching() zero-copy replacement - EMA tau=1.0 init for target_params_buf KNOWN ISSUE: params_buf is zero-initialized but from_flat_buffer() creates weight set pointers into it BEFORE flatten_online_weights runs. The online forward reads zeros → NaN. Needs xavier_init_params_buf() at construction. 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;