37964ae2a4
Path (B) Commit A — additive infrastructure for the SP19 producer-side
multi-horizon reward augmentation. Three ISV slots reserve indices for
a future Path (A) refactor that would let a controller drive per-batch
horizon weights; producer (Commit B) hardcodes equal-thirds 1/3 each at
fxcache-write time so the slot reservations are dormant in this branch.
Pure additive — changes NO runtime behaviour. The dispatch arms write
the equal-thirds sentinel at every fold boundary, but no kernel
consumes the slots. This is forward-compatible reservation per
feedback_isv_for_adaptive_bounds, NOT a half-fix; the producer wiring
is complete (next commit), the slot reservations are documented
forward-compatibility for the TARGET_DIM-bumping refactor.
Slot allocation:
- 507 REWARD_HORIZON_WEIGHT_1BAR_INDEX sentinel = 1/3
- 508 REWARD_HORIZON_WEIGHT_5BAR_INDEX sentinel = 1/3
- 509 REWARD_HORIZON_WEIGHT_30BAR_INDEX sentinel = 1/3
Touches:
- crates/ml/src/cuda_pipeline/sp14_isv_slots.rs: 3 slot constants +
sentinel + range markers + sp19_reward_horizon_slot_layout_locked +
all_sp19_slots_fit_within_isv_total_dim lock tests.
- crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs: ISV_TOTAL_DIM 507 →
510 + layout_fingerprint_seed extension (3 SLOT_* lines + a
SP19_PRODUCER_HARDCODED_HORIZON_BLEND token registering producer-time
consumption).
- crates/ml/src/cuda_pipeline/state_layout.cuh: 3 #define mirrors of
the Rust slot indices + SENTINEL_REWARD_HORIZON_WEIGHT_DEFAULT macro.
- crates/ml/src/trainers/dqn/state_reset_registry.rs: 3 RegistryEntry {
FoldReset } with the "SP19 Path (B) reservation" marker + lock-test
expansion 24 → 27 entries.
- crates/ml/src/trainers/dqn/trainer/training_loop.rs: 3 dispatch arms
in reset_named_state writing SENTINEL_REWARD_HORIZON_WEIGHT_DEFAULT.
- docs/dqn-wire-up-audit.md: SP19 Commit A entry documenting the
reservation rationale + verification steps.
Verification:
SQLX_OFFLINE=true CUDA_COMPUTE_CAP=86 cargo check --workspace clean
cargo test -p ml --lib sp19_reward_horizon_slot_layout_locked passes
cargo test -p ml --lib all_sp19_slots_fit_within_isv_total_dim passes
cargo test -p ml --lib sp18_fold_reset_entries_present passes (27)
cargo test -p ml --lib every_fold_and_soft_reset_entry_has_dispatch_arm passes
bash scripts/audit_sp18_consumers.sh --check exit 0
Atomic-refactor invariant (HARD — feedback_no_partial_refactor): NO
L40S DISPATCH between Commit A and Commit B. The producer-side blend
+ fxcache version bump + behavioural test land in the next commit on
this branch.
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;