e140392f86
Adds 6 output slots to compute_backtest_metrics_kernel — per-bucket
(win_rate, trade_count) for the {Trending, Ranging, Volatile} regime
split — so the val backtest surfaces whether the long-running ~46%
aggregate WR hides regime-conditional edge. Trades are bucketed at
trade-OPEN by feature[40] (ADX-norm) per the structural thresholds
(T:ADX>0.4, R:ADX<0.2, V:otherwise), mirroring
gpu_walk_forward.rs::classify_regime_from_features. Block tree-reduce
only per feedback_no_atomicadd. Observability-only emission via new
HEALTH_DIAG[N]: val_regime [wr_T=... n_T=... wr_R=... n_R=... wr_V=...
n_V=...] line; thresholds remain kernel constants per
feedback_isv_for_adaptive_bounds (no controller consumer yet).
Implementation atomic (kernel + launcher + WindowMetrics + HEALTH_DIAG
emit + 3 GPU oracle tests + audit doc):
- backtest_metrics_kernel.cu: per-thread per-regime trade counters,
2-slot boundary buffer extension carrying open-bar regime through
block stitch, output stride 13 → 19, shmem 5 → 11 reduction tiles
- gpu_backtest_evaluator.rs: WindowMetrics +6 fields, metrics_buf
size 13 → 19, launcher passes features_buf + feature_dim, consume
populates per-regime fields
- metrics.rs: val_regime HEALTH_DIAG line in consume_validation_loss
- regime_wr_oracle_tests.rs (NEW): 1 CPU sanity + 3 GPU oracle tests
(bit-exact match ε=1e-5 vs CPU oracle on stratified 30/40/30 batch)
Validation: cargo check --workspace clean; 17/17 gpu_backtest_evaluator
unit tests pass; 1+3/4 regime WR tests pass on local RTX 3050 Ti
(1.89s); audit_sp18_consumers.sh --check exit 0.
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;