cdd3dc6edb
Completes the F-3 observability story. Smoke runs now print
aux_trade_outcome_ce_ema every epoch in the standard HEALTH_DIAG output
— no ISV inspector required.
Changes:
health_diag.rs:
- New field aux_trade_outcome_ce: f32 appended at END of
HealthDiagSnapshot per "Field order is stable; adding fields
appends to the end" doc rule. Existing fields' byte offsets
preserved → no kernel-side word offset re-validation.
- snapshot_size_is_stable pin: 149 × 4 → 150 × 4 = 600 bytes.
health_diag_kernel.cu:
- New WORD_AUX_TRADE_OUTCOME_CE = 149 (appended at end).
- WORD_TOTAL = 150 (was 149); static_assert bumped.
- New kernel arg aux_trade_outcome_ce_idx appended after
moe_lambda_eff_idx.
- New mirror write after the existing MoE mirrors. Stream-implicit
ordering: aux_outcome_ce_ema_update (F-3b) fires before
health_diag_isv_mirror, so the read picks up the just-updated EMA.
gpu_health_diag.rs:
- launch_isv_mirror gets new aux_trade_outcome_ce_idx: i32 arg.
gpu_dqn_trainer.rs:
- launch_health_diag_isv_mirror passes
AUX_TRADE_OUTCOME_CE_EMA_INDEX as the new arg.
training_loop.rs:
- Per-epoch metrics push appends ("aux_trade_outcome_ce_ema",
ISV[538]) to the standard out vec. Console / CSV automatically
includes the new column.
End-to-end F-3 chain now closed:
K=3 fwd → aux_to_loss_scalar_buf → aux_outcome_ce_ema_update
→ ISV[538] → health_diag_isv_mirror → snap.aux_trade_outcome_ce
→ training_loop metrics → console.
Operator sees CE every epoch:
- Cold-start: 0.000
- After bootstrap: ~1.098 (= ln(3))
- After training: ideally 0.5-0.7 (head learning)
Phase F end-to-end ready. The vNext stack has:
- Full GPU kernel chain (A2-A5 + D + plan-conditioning)
- Full Rust wireup (B0-B4, B4b-1/2, C-1/C-2, B5b)
- Real labels reaching trainer
- K=3 → policy via state slots + atom-shift
- End-to-end CE observability
Verification:
- cargo check -p ml clean.
- cargo test -p ml --lib → 1016/0 green (incl. bumped
snapshot_size_is_stable byte-size pin test).
Audit: docs/dqn-wire-up-audit.md Phase F-3c section.
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;