6e0cb3d62c
Task 3 refactored experience_state_gather (the WRITER) to use assemble_state() with canonical layout (OFI at [42..62)). But env_step and ofi_embed_build_input (the READERS) still hardcoded OFI at [66..84) — the OLD pre-refactor layout. This meant 7 locations were reading MTF/portfolio features as if they were OFI: 1. Line 1745: dense micro-reward ofi_cur = state+66 → actually MTF[4] 2. Line 1778: book_aggression = state[82] → actually plan_isv region 3. Line 1983: ps[30..37] OFI delta storage for NEXT bar — storing MTF data 4. Lines 5833/5836/5838/5840: ofi_embed_build_input — feeds 18→10 MLP into Mamba2 temporal SSM and attention. Entire temporal pipeline was training on MTF features dressed as OFI. Symptoms explained: - WinRate=20.9% on validation (anti-correlated): dense micro-reward computes quality=sign_pos × garbage_MTF_deltas, systematically rewarding wrong direction - mean_reward=+0.004 but Sharpe_raw=-0.0004: shaped reward exploits garbage signal, real portfolio loses money - grad_norm=23560 at epoch 2: gradients chasing noise - Q-value explosion to ±10 in one epoch: learning contradictions Fix: replaced all hardcoded 66/74/82/83 with SL_OFI_START from state_layout.cuh. Both reader kernels now use the same canonical layout as assemble_state(). Verified locally: smoke test passes, OFI_DIAG shows correct non-zero values (raw_mean=-0.36, delta_mean=-0.21, log_dur=-0.23). 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;