29a2615f6a
Lands the producer-side infrastructure for SP20 Phase 2's 4-quadrant
reward kernel (Task 2.2):
* New `[alloc_episodes]` `i32` device buffer `label_at_open_per_env`
on `GpuExperienceCollector`, allocated alongside `episode_starts_buf`.
* Two new `experience_env_step` kernel args (`aux_label_per_env` input,
`label_at_open_per_env` output), threaded into the launch site.
* Write site at the `entering_trade` branch maps the upstream aux
next-bar label (`{0,1,-1}` from `aux_sign_label_per_step_kernel`) to
the SP20 spec sign convention (`{-1,+1,0}`) per spec §4.1.
* StateResetRegistry entry `label_at_open_per_env` (FoldReset) +
`reset_named_state` dispatch arm via `stream.memset_zeros`.
* Registry invariant test `sp20_label_at_open_per_env_registered_fold_reset`
pins the FoldReset category + key description anchors.
* Audit-doc entry documents the design rationale (why per-env buffer
vs. derived-at-trade-close read), sign mapping, FoldReset semantics,
kernel-arg threading, and Task 2.1/2.2 forward references.
Producer-only this commit. The consumer (`is_close` branch reading
`label_at_open_per_env[i]` and passing to `sp20_compute_event_reward`)
lands atomically with the SP12 v3 reward block replacement in Task 2.2,
per `feedback_no_partial_refactor`.
NULL-tolerant kernel arg pair lets test scaffolds without aux-head
wiring continue to work; the FoldReset-zeroed buffer reads as sentinel
0 at the consumer (which Task 2.2 maps to "no info" / wrong-reason
quadrant — safer default than over-rewarding a no-signal trade).
Verification:
SQLX_OFFLINE=true cargo check -p ml # clean
SQLX_OFFLINE=true cargo test -p ml --lib sp20 # 18/18 (+1 new)
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;