4399a56d76
Five tests in crates/ml/src/trainers/dqn/smoke_tests had pass gates that validated existence rather than the invariant their doc-comment claimed to test. A trainer returning all-zero diagnostics (a plausible wiring regression) would have passed them. - reward_component_audit: was `is_finite() && >= 0.0` on 5 slots — passes trivially on all-zero stubs. Added `cf_flip > 0.1` and `trail_r > 0.01` floors (known-wired slots in smoke config; popart/micro/loss_aversion remain finite-only as they are legitimately near-zero in smoke). Run-observed values: cf=0.614, trail=0.295, la=0.006 — well above floors. - exploration_coverage: `.unwrap_or(0.0)` silently substituted 0 for a missing epoch, conflating "emission regressed" with "exploration collapsed". Now panics with a distinct message on missing entries, also asserts len >= 20, normalized range [0,1], and spread > 1e-6 to catch constant-output emitters. Run-observed spread: 0.275. - training_stability::50_epoch_convergence: entropy assertion was guarded behind `if entropy.is_finite()`, so NaN entropy (the more severe failure) silently passed. Fail hard on NaN first. - training_stability::trading_model_behavior: same `is_finite` guard pattern on action_entropy — now fails hard when the diagnostic is missing or NaN rather than skipping. - training_stability::gpu_collector_auto_initializes: only asserted training returned `Ok(_)`. A collector producing silent zeros would pass. Now also verifies epochs_trained, loss finiteness, and gradient flow. - walk_forward::no_overfitting_50_epochs: had two tautological "finite check" assertions (`x < x + 1` and the signum-adjusted ratio) that always passed regardless of divergence. Replaced with real `.is_finite()` checks plus a `div_ratio < 10.0` stability gate. Tests run under CUBLAS_WORKSPACE_CONFIG=:4096:8 + FOXHUNT_TEST_DATA=test_data/futures-baseline, release-test profile. reward_component_audit and exploration_coverage both PASS on the local RTX 3050. No threshold relaxation or quickfixes applied; any future wiring regression will now be caught by a meaningful assertion instead of a near-tautology. 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;