9ba08ff609
ZN.FUT tests in crates/ml/src/data_loader.rs were failing because no
valid ZN.FUT DBN data is available locally; gated with #[ignore].
ml-asset-selection's universe definition and backtesting's
zn_futures() slippage profile remain untouched — those are production
references to ZN as a candidate symbol, distinct from data availability.
Migrated 4 deprecated cudarc memcpy_stod/memcpy_dtov sites in the
test function test_eval_action_select_eval_argmax_picks_best in
crates/ml/src/cuda_pipeline/mod.rs to mapped-pinned per
feedback_no_htod_htoh_only_mapped_pinned:
- 3x memcpy_stod (f32 input uploads) → MappedF32Buffer::new +
write_from_slice + dev_ptr as raw u64 kernel arg; kernel reads
directly from mapped-pinned pages, no DtoD copy needed
- 1x memcpy_dtov (i32 output readback) → MappedI32Buffer::new +
dev_ptr as kernel arg + read_all() after stream sync
The cudarc deprecation suggested clone_htod/clone_dtoh as replacements
but those still perform HtoD/DtoH copies — violating the strict rule.
Mapped-pinned with direct dev_ptr kernel args is the correct pattern
(matches distributional_q_tests.rs).
Note: DqnGpuData/PpoGpuData upload paths also in mod.rs still use
clone_to_device_f32_via_pinned; migrating those requires changing
CudaSlice<f32> struct fields to MappedF32Buffer which is blocked until
gpu_dqn_trainer.rs consumers are also updated (separate scope).
Workspace cargo check warnings: 15 → 15 (test-only deprecated calls
not visible to cargo check; ZN gate adds 3 to ignored count).
cargo test -p ml --lib failures: 16 → 13 (3 ZN tests now ignored).
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;