251b871294
alpha-rl-mbg2n on H100 failed at LobSimCuda::new with `CUDA_ERROR_NO_BINARY_FOR_GPU` loading book_update.cubin. Root cause: ml-backtesting/build.rs read only `FOXHUNT_CUDA_ARCH` (set by lob-backtest-sweep-template) but NOT `CUDA_COMPUTE_CAP` (set by alpha-rl-template from `nvidia-smi --query-gpu=compute_cap` inside the compile pod). On H100 it silently fell through to default sm_86; the sm_86 cubin contains no PTX → no JIT path on sm_90 device. The docstring claimed "Mirrors crates/ml-alpha/build.rs" — it didn't (ml-alpha reads CUDA_COMPUTE_CAP). Completing the mirror now: detect_arch returns sm_<NN> honoring (in order): 1. CUDA_COMPUTE_CAP numeric env (alpha-rl-template) 2. FOXHUNT_CUDA_ARCH sm_-prefixed env (lob-backtest-sweep-template) 3. nvidia-smi --query-gpu=compute_cap at build time 4. Default sm_86 (RTX 3050 Ti local dev) Both production argo templates now produce sm_90 cubins on H100 and sm_89 on L40S without further changes. Co-Authored-By: Claude Opus 4.7 <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.