7d538d9304
Replaces Step A's NULL launcher passes with real device buffers.
Both kernel launch sites in gpu_backtest_evaluator.rs (backtest_env_step
and backtest_env_step_batch) now pass real per-trade tape pointers.
The kernel's per-trade emission block fires unconditionally on close
events — single-threaded per-window writes preserve event ordering and
enable race-free counter increment without atomicAdd.
New constants + types:
- MAX_TRADES_PER_WINDOW = 200_000 (typical eval window bar count;
per-window memory: 4 SoA buffers × 4 bytes × 200k = 3.2MB)
- pub struct EvalTrade with 5 fields: bar_index, pnl, holding_bars,
direction, magnitude. Does NOT include predicted_q / ensemble_var
— those need entry-time captures (entry_q, entry_var in portfolio
state) deferred to Phase 1.5.
New struct fields on GpuBacktestEvaluator:
- per_trade_pnl_buf: CudaSlice<f32> [n_windows × MAX_TRADES]
- per_trade_holding_bars_buf: CudaSlice<u32>
- per_trade_bar_index_buf: CudaSlice<u32>
- per_trade_dir_mag_buf: CudaSlice<u32> (packed dir/mag)
- per_trade_count_buf: CudaSlice<u32> [n_windows]
New methods:
- reset_per_trade_tape (folded into reset_evaluation_state): zeros
the count buffer at the start of each eval window. SoA value
buffers don't need zeroing — read up to count[w] only.
- pub fn read_per_trade_tape(&self) -> Result<Vec<EvalTrade>, MLError>:
reads count buffer first (cheap), early-returns empty if no trades,
else reads 4 SoA buffers (~16MB DtoH at PCIe ≈ 1ms) and flattens
window-major into chronological Vec<EvalTrade>.
Phase 2 follow-up (next commit) — wire read_per_trade_tape to the
enrichment caller in training_loop.rs:1510-1568, replacing
extract_eval_trades_from_metrics (the fake-trade synthesizer).
Phase 1.5 follow-up (if Phase 2 keeps E1+E5) — add entry_q + entry_var
to portfolio_state at trade open, extend per-trade tape with 6th/7th
SoA buffers.
Affected files:
- crates/ml/src/cuda_pipeline/gpu_backtest_evaluator.rs
(constants, struct fields, alloc, construction, 2 launcher sites,
reset_evaluation_state addition, read_per_trade_tape method)
Verification:
- cargo check -p ml --tests: passes (warnings only)
- GPU oracle tests: behavior preserved by construction (existing
WindowMetrics aggregator unaffected — separate kernel)
Plan reference: docs/plans/2026-05-10-sp21-train-eval-coherence-isv-defrost.md
T2.2 multi-phase scope; Phase 1 Step B closure.
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;