b98dc2730d
Adds 11 buffer fields + 2 orchestrator ops handles (fwd + bwd) to the
trainer struct, mirroring the existing aux_nb_* / aux_partial_nb_*
pattern at K=3 instead of K=2.
Trainer struct additions:
- aux_to_fwd: AuxTradeOutcomeForwardOps (Phase B0 scaffold)
- aux_to_bwd: AuxTradeOutcomeBackwardOps
- aux_to_hidden_buf [B, H=128] saved post-ELU
- aux_to_logits_buf [B, K=3] saved logits
- aux_to_softmax_buf [B, K=3] saved softmax (3 future consumers)
- aux_to_label_buf [B] i32 sparse {-1, 0, 1, 2}
- aux_to_loss_scalar_buf [1] mean CE
- aux_to_valid_count_buf [1] B_valid for backward
- aux_dh_s2_to_buf [B, SH2] SAXPYs into dh_s2_aux_accum
- aux_partial_to_w1 [B, H, SH2] per-sample dW1
- aux_partial_to_b1 [B, H] per-sample db1
- aux_partial_to_w2 [B, K=3, H] per-sample dW2
- aux_partial_to_b2 [B, K=3] per-sample db2
Memory: aux_partial_to_w1 = 256 MB at B=2048 — identical to K=2 head's
partial size (same SH2, same H). Total new aux-to footprint ≈ 260 MB.
The existing aux_param_grad_final_buf scratch is sized to the largest
tensor across all aux heads; trade-outcome head's largest is W1 [H, SH2]
= 32,768 floats — identical to K=2/K=5 W1s. No resize needed.
Cold-start label semantics: alloc_zeros yields label 0 (Profit) for
every sample. Until the producer wires in (B3), the trainer's CE loss
treats every sample as "should have predicted Profit" — degraded but
well-defined (no NaN). Mirrors the K=2 head's known-degraded state
between B1.1a and B1.1b.
No FoldReset registration: these buffers are overwritten every batch
— no stale-state-leak risk across folds (matches the existing aux_nb_*
pattern).
Phase B3 next: collector-side rollout buffers + forward chain wireup
into collect_experiences_gpu (per-env softmax → per-(i, t) fan-out
scatter for trainer's aux_to_softmax_buf population).
Audit: docs/dqn-wire-up-audit.md Phase B2 section.
Cargo check clean (21 warnings, none new on aux_to_* fields).
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;