132609724e
Path A of the blocked 1.3.b investigation: fixes two architectural issues atomically and wires the launcher. (1) Bug fix: dd_state_kernel.cu was recomputing new_equity = PS_PREV_EQUITY + pnl_step and writing it back, but experience_env_step already maintains PS_PREV_EQUITY (experience_kernels.cu:3473-3475) — wiring as-is would silently double-accumulate equity every step. Kernel now READS PS_PREV_EQUITY / PS_PEAK_EQUITY only; does not modify them. pnl_step parameter dropped from both kernel and launcher signatures. (2) Per-env shape decision: kernel is single-thread/single-block; production has N envs but DD ISV slots [401..407) are scalars. Picks 'env 0 as canonical observable' — kernel reads pos_state[0 * PS_STRIDE + ...]. Per-env redesign (per-env tiles + reduction kernel) deferred to Phase 1.3.b-followup if L40S smoke shows single-env DD aggregation is insufficient. (3) Wire-up: launch added at gpu_experience_collector.rs step 5b in launch_timestep_loop, immediately after env_step writes PS_PREV_EQUITY, outside the exp-fwd graph capture region (which ends at line ~3829, well before env_step). Atomic per feedback_no_partial_refactor: kernel signature change + oracle test update + launcher call site update all in this commit. Eliminates the Phase 1.3 orphan launcher per feedback_wire_everything_up. Downstream Phase 3.3 / 3.5.2 / 3.5.4 / 3.5.5 readers will receive live DD values when their consumer wiring lands. 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;