f42b5fff8d
Third of 3 Layer D producer kernels. Replaces host-side training_sharpe_ema, max_dd_ema, low_dd_ratio updates (host-side EMAs in training_loop.rs) with a fused GPU kernel chained through apply_pearls_ad_kernel. Per feedback_no_cpu_compute_strict. Note: agent investigated training_loop.rs and found the third metric is low_dd_ratio (not gamma_blend as the original plan brief named). The authored kernel reproduces the actual host-side EMA triplet present in the codebase. Additive only — no consumer wiring, no behavior change. The kernel + launcher are loaded into GpuDqnTrainer and the slots are reserved on the ISV bus, but the host-side updates continue to run unchanged. D4 (atomic Layer D commit) wires this and D1+D2 to call sites in the same atomic refactor per feedback_no_partial_refactor. Also note: training_loop.rs gains two state-reset-registry dispatch arms (sp5_health_composition for D2 + sp5_training_metrics_ema for D3) — registry plumbing required by the new entries, NOT consumer wiring of the kernels themselves; same pattern as SP5 Layer A bug-fix #281. What this lands: - training_metrics_ema_kernel.cu (single-block 3-thread fused EMA) - Rust launcher launch_training_metrics_ema() - 3 new ISV slots (TRAINING_SHARPE_EMA_INDEX..LOW_DD_RATIO_INDEX, 294..297) - ISV_TOTAL_DIM 294 → 297, SP5_PRODUCER_COUNT 120 → 123 (linear span) - LAYOUT_FINGERPRINT_SEED bump (auto via slot string) - StateResetRegistry entries for D2 (sp5_health_composition) + D3 (sp5_training_metrics_ema) with reset_named_state dispatch arms - build.rs cubin registration - GPU-gated unit test in sp5_producer_unit_tests.rs (analytical EMA) - SP5 contiguity slot test training_metrics_ema_slots_contiguous_and_above_health_block - Audit doc append Formula fidelity: kernel reproduces the host-side EMA update for sharpe/max_dd/low_dd_ratio bit-for-bit within float precision. β decay constants and any warmup/clamp logic migrate as Invariant 1 anchors with no algorithmic change. Verified by unit test asserting kernel output matches an analytical EMA sequence within 1e-6 rel-err. Tests: cargo check + cargo build clean; ISV slot + state_reset_registry unit tests pass (8/8 incl. new contiguity check); GPU correctness test fires on next L40S smoke. Refs: SP5 plan §D Task D3, builds on D1 (5ee795f14) + D2 (e49756ac9). Layer D additive infrastructure complete after this commit; D4 (atomic 5-site host-EMA → GPU migration) is next. 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.