4c231fa812
Addresses 5 IMPORTANT items from A13 code-quality review: 1. apply_pearls_to_slot helper extracted into sp4_wiener_ema.rs. Collapses ~30 lines of read_volatile / pearls_ad_update / write_volatile per-slot block into a single unsafe fn. 12 launchers now consume the helper (5 SP4 producers A5-A9, 6 A13 retrofits A13.0-A13.5, plus the inline label-scale block in aux_heads_forward Step 2b — including 2 apply_pearls closures inside multi-slot launchers and the cross-boundary GpuExperienceCollector consumer). Pearl C rate_deficit site untouched (different mapped-pinned buffer + Rust ema array, doesn't share the helper's pointer-based contract). 2. REWARD_COMPONENT_COUNT named constant added next to REWARD_POPART_EMA_INDEX in gpu_dqn_trainer.rs. Replaces 3 hardcoded `6` literals across collector launcher (block_dim, Pearls A+D loop) and training_loop fold-reset range arithmetic. Mirrors MOE_NUM_EXPERTS / SL_NUM_FEATURE_GROUPS invariant-guard pattern with debug_assert_eq! in the collector launcher. Kernel literal `6` retained (allows nvcc full unroll); kernel comment now documents the host-side invariant. 3. SP4_PRODUCER_COUNT, SP4_WIENER_FLOATS_PER_SLOT, SP4_WIENER_TOTAL_FLOATS promoted from fn-local consts inside `pub fn new` to module-level `pub const`s in gpu_dqn_trainer.rs, re-exported via cuda_pipeline::mod. All 13 redeclarations in tests/sp4_producer_unit_tests.rs replaced with single `use ml::cuda_pipeline::SP4_PRODUCER_COUNT;` import. Future buffer growth requires single-file edit. 4. _ema_alpha_unused: f32 caller-compat shim removed from 6 retrofitted launchers (launch_h_s2_rms_ema, launch_aux_heads_loss_ema, launch_vsn_mask_ema, launch_moe_expert_util_ema, launch_iqn_quantile_ema, launch_reward_component_ema_inplace) and the FusedTrainingCtx proxy. All callers in training_loop.rs + fused_training.rs updated to drop the unused argument per feedback_no_legacy_aliases (no soft-deprecated wrappers; rename all call sites directly). Doc-comments updated from "α dropped per SP4 — argument preserved so callers compile unchanged" to "α derived adaptively from per-slot Pearls A+D Wiener state — see sp4_wiener_ema::pearls_ad_update". 5. Stale doc reference fixed at gpu_aux_heads.rs:391 — comment referenced non-existent launch_label_scale_ema_with_pearls function. Now correctly points at the inline Pearls A+D block in GpuDqnTrainer::aux_heads_forward Step 2b (which now consumes apply_pearls_to_slot). Pure refactor — no spec or behaviour change. Same kernel launches, same Pearls A+D semantics, same ISV slot writes. cargo check -p ml --offline clean (12 pre-existing warnings, no new); cargo test -p ml --lib --offline sp4_wiener_ema 7/7 passing (6 originals + apply_pearls_to_slot_pearl_a_bootstrap_path); cargo test -p ml --lib --offline state_reset_registry 3/3 passing. Refs: A13 review (commits aada419de..c5add566d). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Foxhunt
Production HFT trading system in Rust.
Architecture
The workspace contains 32 crates organized as follows:
Core Libraries (16)
| Crate | Purpose |
|---|---|
trading_engine |
Order processing, FIX 4.4, IB TWS, SIMD, RDTSC timing |
risk |
VaR, Kelly, circuit breakers, kill switches, compliance |
risk-data |
Risk data types and shared structures |
trading-data |
Trading data types |
ml |
DQN Rainbow, PPO, TFT, Mamba2, ensemble inference |
ml-data |
ML data types and feature definitions |
data |
Market data ingestion and storage |
backtesting |
Replay engine, strategy tester |
adaptive-strategy |
Ensemble execution, microstructure analysis |
common |
Shared types, resilience, error handling |
storage |
S3 and local model storage |
model_loader |
Model serialization and loading |
market-data |
Market data feed handlers |
database |
PostgreSQL access layer (SQLx) |
config |
Configuration management |
tli |
CLI commands and tooling |
Services (8)
| Service | Purpose |
|---|---|
backtesting_service |
gRPC backtesting service |
broker_gateway_service |
FIX routing, broker connectivity |
trading_service |
Core trading operations |
ml_training_service |
Model training orchestration |
data_acquisition_service |
Market data acquisition |
trading_agent_service |
Autonomous trading agents |
api_gateway |
gRPC API gateway with auth |
web-gateway |
Axum REST + WebSocket gateway |
Frontend
web-dashboard/ -- React 19 + TypeScript + Vite + TradingView charts.
Building
# Check compilation (no PostgreSQL required)
SQLX_OFFLINE=true cargo check --workspace
# Run tests for a specific crate
SQLX_OFFLINE=true cargo test -p <crate> --lib
# Clippy
SQLX_OFFLINE=true cargo clippy --workspace
ML Models
Four production model architectures on Candle v0.9.1 with CUDA:
- DQN Rainbow -- Deep Q-Network with prioritized replay, dueling heads, noisy nets
- PPO -- Proximal Policy Optimization with GAE and LSTM policies
- TFT -- Temporal Fusion Transformer for multi-horizon forecasting
- Mamba2 -- State space model for sequence prediction
Each model has a standalone trainer and a UnifiedTrainable adapter for the hyperopt pipeline.
Infrastructure
- Git: Gitea at
git.fxhnt.ai(Tailscale-only), Scaleway DEV1-S - Observability: OpenTelemetry OTLP (env
OTEL_EXPORTER_OTLP_ENDPOINT) - Database: PostgreSQL with SQLx offline mode for CI
License
Proprietary. All rights reserved.