3b71d21834
Aux's original label was (p_{t+1} > p_t) — pure HFT-scale microstructure
noise that's unlearnable at our HFT-MFT trading frequency. Migrated to
(p_{t+H} > p_t) where H is read from ISV[AUX_PRED_HORIZON_BARS_INDEX=450].
Adaptive producer drives H from observed avg winning hold time:
- Pearl-A first-observation bootstrap: replace sentinel H=60 directly
on first valid observation
- Steady-state Wiener-α EMA blend, slow (α=0.01) for stable horizon
(no target-variance EMA available, fallback per
pearl_wiener_optimal_adaptive_alpha)
- "No winning trades yet" guard keeps sentinel until first valid observation
Lookahead truncation: labels at t where t+H >= total_bars are masked
(sentinel -1, loss-reduce skips). The existing aux_next_bar_loss_reduce
in aux_heads_kernel.cu already supports the -1 mask convention via the
B_valid count — no new valid_mask parameter needed.
Step 5b finding: Case B — existing per-sample buffers
(hold_at_exit_per_sample, trade_profitable_per_sample) populated by
unified_env_step_core, but no aggregate ISV slot. Added new aggregator
slot AVG_WIN_HOLD_TIME_BARS_INDEX=451 + new producer kernel
avg_win_hold_time_update_kernel.cu (block-tree-reduce, no atomicAdd).
ISV_TOTAL_DIM bumped 450 → 452.
ATOMIC migration per feedback_no_partial_refactor: both label kernels
(aux_sign_label_kernel.cu trajectory + aux_sign_label_per_step_kernel.cu
per-rollout-step) migrated together to the new
(targets, bar_indices, isv, isv_h_idx, out_labels, total, total_bars)
signature. The lookahead host-passed scalar argument is removed; H is
read from ISV inside the kernel (broadcast value, single read per
thread, on-device clamp [1, 240]).
Producer chain (per-epoch boundary): new
GpuDqnTrainer::launch_aux_horizon_chain orchestrates
avg_win_hold_time_update → aux_horizon_update sequentially alongside
launch_kelly_cap_update at the existing epoch-boundary slot in
training_loop.rs.
Trunk math (C.2/C.3/C.4) unchanged — separate aux trunk is label-
agnostic. Validation in C.10 will use H=60 cold-start; the adaptive
producer drives H from real winning-trade observations.
Tests (8 oracle, 5 new + 3 preserved):
- aux_trunk_forward_matches_numpy_reference (C.3) ✓
- aux_trunk_backward_gradient_check (C.4) ✓
- aux_trunk_backward_does_not_write_dx (C.4) ✓
- aux_sign_label_h_bar_horizon (NEW) ✓
- aux_sign_label_lookahead_mask (NEW) ✓
- aux_horizon_pearl_a_bootstrap (NEW) ✓
- aux_horizon_converges_to_steady_target (NEW) ✓
- aux_horizon_holds_sentinel_with_no_winning_trades (NEW) ✓
8/8 pass on RTX 3050 Ti.
Phase C.4b of SP14 Layer C separate-aux-trunk refactor.
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;