b28b349ac3
Adds collector-side trade-outcome head: 5 struct fields + allocations
+ per-step forward + per-step label producer launches in the rollout
loop. Mirrors the K=2 next-bar head's collector wireup at K=3.
Collector struct additions:
- exp_aux_to_fwd: AuxTradeOutcomeForwardOps (3 kernel handles)
- exp_aux_to_hidden_buf [alloc_episodes × H=128] saved post-ELU
- exp_aux_to_logits_buf [alloc_episodes × K=3] saved logits
- exp_aux_to_softmax_buf [alloc_episodes × K=3] softmax tile
- exp_aux_to_label_buf [alloc_episodes] i32 sparse {-1, 0, 1, 2}
Per-step launches in collect_experiences_gpu rollout loop:
1. aux_trade_outcome_forward — launched immediately after the K=2
sibling's forward_next_bar, parallel on the same stream. Reads
exp_h_s2_aux + weights at flat-buffer indices [163..167) (Phase
B1 additions). Writes hidden/logits/softmax tiles. No consumer
yet — Phase C wires state assembly; Phase B4 wires trainer
scatter.
2. trade_outcome_label_kernel — launched immediately after
experience_env_step on the same stream, reading the save-for-
backward buffers (pnl_vs_target_at_close_per_env, pnl_vs_stop_at_
close_per_env) that env_step just wrote at segment_complete.
Stream-implicit producer→consumer ordering. Emits per-env
{-1, 0, 1, 2} labels — sparse, ~95-99% bars produce -1 (mask).
Dead-code discipline per feedback_wire_everything_up: every kernel arg
+ producer site is real wiring (not NULL placeholder) — only the
absence of consumers reading the produced tiles is "dead". The smoke
run produces softmax tiles + labels every step bit-identical to
pre-vNext baseline (no consumer = no effect on training behavior).
Phase B4 next: trainer-side replay-batch chain (forward + loss_reduce
+ backward + Adam SAXPY for the 4 new weight tensors).
Audit: docs/dqn-wire-up-audit.md Phase B3 section.
Verification:
- cargo check -p ml clean (21 warnings, none new on aux_to_*).
- cargo test -p ml --lib → 1016 passing / 0 failing (unchanged from
post-fix-sweep baseline at ebc1b1502).
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;