339eaca983
Task 5 of the GPU log ring plan. Adds `gpu_log` module behind the
`cuda-diag-log` feature gate:
- `LogRecord` (#[repr(C)], 64 B) mirroring the C-side layout in
`gpu_log_ring.cu`; const compile-time size assertion catches drift.
- `LogRing::alloc()` allocates and zero-initialises a 32k-slot
mapped-pinned ring (2 MiB pinned).
- `alloc_step_counter()` allocates the device-side i32 step counter.
- Decoder dispatch table with per-(kid, rt) decoders for
`KID_SMOOTHNESS_CONTROLLER` × {RT_INPUT, RT_STATE, RT_OUTPUT}
emitting structured tracing::info! events.
- `spawn_drain_task()` polls a mapped-pinned step-counter shadow at
500 ms cadence, walks new slots, validates magic + step, dispatches
to decoders. Emits tracing::warn! when the drainer falls behind the
ring window (no silent record drops).
Kernel ID + record-type constants are a manual mirror of
`crates/ml-alpha/cuda/gpu_log_ids.h`; drift is caught by the const
size assertion at compile time and (subsequently) by build.rs.
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.