b435d25bec
Bug-hunt review on 5e16b67ca found two real bugs in experience_env_step
post-B1b structural refactor — same class as the slot 63 overload
(pre-SP11 invariants exposed by post-decomposition semantic).
Bug 1 (Critical): reward_components_per_sample[+1..+5] not zero-init at
per-bar entry. Slots written only on execution paths that fire (rc[2]
inside segment_complete trail-trigger, rc[3] inside positioned-non-
complete, rc[4] inside flat-with-features, rc[5] inside multiple
bonus paths, rc[1] inside CF block). Non-firing paths leave stale
values from previous batch's same out_off, contaminating SP4
reward_component_ema_kernel -> slots 64..68 -> SP11 mag-ratio canary
-> wrong controller weights.
Fix: zero-init rc[0..6) at the same location where r_<component>
locals are zero-initialized, so locals and buffer reset together.
Eliminates the entire stale-rc class of bugs.
Bug 2 (Important): cf_flip applied BEFORE inventory/churn/conviction
modifiers. Spec section 3.4.4 requires cf_flip LAST so subtractive
penalties operate on the right sign and multiplicative scales
attenuate before direction is flipped. Pre-fix on flipped samples,
inventory/churn ADDED to negated reward (penalty became bonus) and
conviction scaled the wrong-signed value. Asymmetric gradient signals
between flipped/non-flipped -> contaminated SP11 mag-ratio canary at
the cf axis.
Fix: move cf_flip to after conviction_scale, making it the last
modifier before out_rewards write. CF-block contract preserved
(reward at cf_off is still the post-flipped final value; CF block
already does `do_flip ? -reward : reward` to recover unflipped base).
Stale doc-strings: state_reset_registry.rs (2 sites) and
training_loop.rs (1 site) referenced pre-fix-up SP5_PRODUCER_COUNT=186
/ wiener_buffer=771. Updated to formula form
`(71 + SP5_PRODUCER_COUNT) × 3` so they don't drift on the next SP;
current value (post-B1b fix-up slot 360) is SP5_PRODUCER_COUNT=187,
buffer=774 floats.
dqn-wire-up-audit.md: appended SP11 B1b bug-hunt fix-up section
documenting both bugs, the structural fix, the doc-string sweep,
and verification (cargo check/build/tests).
cargo check + build clean; 6/6 SP11 GPU tests + 10/10 sp5_isv_slots
contract tests + 4/4 state_reset_registry tests still pass. The two
bugs were latent in the local smoke (sharpe 3 vs B1a 30 likely
traceable to either or both); structural fix sound. L40S smoke on
this commit will validate the empirical recovery.
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;