fd24b53833
smoke-test-4rbv9 on b3b4d0278 (z-score implementation) showed bit-
identical w_pop=2.000 at ep1 to pre-z-score B1b smoke, proving the
z-score formula was structurally a no-op:
z[c] = mag[c] / fmaxf(sqrtf(0), EPS_DIV) = mag[c] x 1e6
ratio[c] = (mag[c] x 1e6) / (1e6 x sum(mag)) = mag[c] / sum(mag) -> linear
Root cause: launch_reward_component_ema_inplace at line 3707 ran
AFTER launch_sp11_mag_ratio_compute at line 3465. So ISV[64..68]
and ISV[362..366] held sentinel-0 values at ep1's canary read
(ep0 had no segment_complete fires). z[c]=0 for c=1..5 -> popart
ratio collapsed to 1.0 -> controller saturated.
This was structurally the same bug that motivated adding
launch_sp11_popart_component_ema at line 3441 (B1b follow-up).
That fix-up addressed popart but left cf/trail/micro/opp_cost/
bonus stale.
Moved launch_reward_component_ema_inplace from line 3707 to before
launch_sp11_popart_component_ema. Other launches at the original
site (trade_attempt_rate_ema, plan_threshold_update, etc.) stay
where they were — different consumers, different timing constraints.
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;