e27bb078c4
Phase 0 (commit333ea7184): exhaustive inventory of every numeric value emitted across the 7 HEALTH_DIAG sites in training_loop.rs and metrics.rs. Classified each as GPU-already / CPU-bound / Mixed / Host-state / ISV-already. Aggregate cost: ~50-150 MB/epoch DtoH + ~70 sec/epoch CPU compute (the original observation that motivated the port). Inventory lives at docs/health_diag_inventory.md. Phase 1 (commit60fd7de96): foundation only — no kernel, no producer launch, no CPU code deletion yet. * HealthDiagSnapshot #[repr(C)] POD with 147 fields (all f32 or u32) covering every numeric value in the HEALTH_DIAG line family. Field order matches the existing log format strings (downstream parsers like aggregate-multi-seed-metrics.py depend on this). * Controller fire-bools promoted from u8 to u32 to avoid #[repr(C)] padding mismatch when followed by f32 fields. ~24-byte cost. * MappedHealthDiagSnapshot wrapper around cuMemHostAlloc(DEVICEMAP| PORTABLE) + cuMemHostGetDevicePointer_v2, mirroring the MappedF32Buffer pattern. * Default impl via MaybeUninit::zeroed() (allocator-free, valid bit-pattern). * 3 unit tests pinning size (588 bytes), alignment (4 bytes), zeroed-default — all passing on local CPU host. Phases 2-5 deferred (kernel family + wiring + CPU emit rewrite + old code deletion). The 5 kernels each read different device buffers with different masking semantics — getting any one wrong would silently produce numerically-different HEALTH_DIAG output that downstream parsers would mis-parse. Per the prompt's 'don't wedge yourself' guidance and feedback_no_quickfixes.md, the safer move was to land the typed wrapper cleanly so subsequent commits can iterate kernel by kernel against the actual buffer-by-buffer reduction.
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.