f29dcc47c9
Lays the consumer-side infrastructure for true E7 hindsight synthetic injection. Phase 6.5b will follow with the producer wireup. What lands: 1. EvalTrade.window_index + HindsightExperience.window_index fields (host-side only; set in read_per_trade_tape from the w loop var and propagated by compute_hindsight_labels). 2. GpuBacktestEvaluator.retained_states_buf — mapped-pinned MappedF32Buffer sized [max_len × n_windows × state_dim_padded]. Populated by a DtoD copy after every launch_gather_chunk inside submit_dqn_step_loop_cublas; layout matches chunked_states_buf so the copy is a single contiguous block per chunk (no transpose). 3. pub fn read_retained_state(window_idx, bar_idx) — zero-copy host read via std::ptr::read_volatile on host_ptr (no memcpy_dtoh per feedback_no_htod_htoh_only_mapped_pinned). Mapped-pinned decision (jgrusewski review): - Initial draft used CudaSlice<f32> + memcpy_dtoh for host read, caught at review: violates feedback_no_htod_htoh_only_mapped_pinned. - Refactored to MappedF32Buffer (cuMemHostAlloc DEVICEMAP). The DtoD copy remains (rule forbids HtoD/DtoH, not DtoD; kernel writes via dev_ptr aliasing pinned host memory). Caller must sync eval stream before read_retained_state — production path's consume_metrics_after_event already does this. Memory cost at production cfg (max_len=200_000, n_windows=5, state_dim_padded≈128): ~512 MB pinned host RAM. Substantial but feasible on L40S host (192 GB+). Files changed: - crates/ml/src/cuda_pipeline/gpu_backtest_evaluator.rs: +retained buffer + accessor; EvalTrade.window_index field - crates/ml/src/trainers/dqn/trainer/enrichment.rs: HindsightExperience .window_index field - docs/dqn-wire-up-audit.md: 2026-05-11 audit entry Verification (passing): - cargo check -p ml --tests --features cuda: 0 errors - cargo test -p ml --lib sp21_isv_slots: 3/3 - sp20_aggregate_inputs_test: 12/12 - sp20_phase1_4_wireup_test: 2/2 - sp20_emas_compute_test: 4/4 - sp20_controllers_compute_test: 7/7 - sp21_per_trade_predicted_q_test: 3/3 Total: 34 tests, 0 failures. Infrastructure works without exercising it (accessor returns None until eval populates the retained buffer — graceful degradation for test scaffolds bypassing the full eval). After this commit (Phase 6.5b): - Mapped-pinned synthetic-tuple scratch on GpuReplayBuffer - New insert_synthetic_via_pinned API (raw dev_ptrs, no HtoD) - training_loop hindsight injection wireup (~300 LOC) 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;