e580c1388c
`total_return` from financials.rs:80-94 is log-space cumulative growth
across every per-bar step_return. With ~4M step_returns in a fold-
convergence run, even sub-bps positive bars compound to absurd
magnitudes (observed: 1.93e37%) when displayed as `{:+.2}%`. Math is
correct; display needs scientific notation.
Surfaced in T10 train-multi-seed-xkjkb seed-0 ep3 epoch summary while
SP10 chain validates structural fixes. Cosmetic-only change; no
training-path impact. Audit doc updated with Cosmetic 38.1 entry.
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;