98a81981ac
Per pearl_build_rs_rerun_if_env_changed, every std::env::var() must be paired with cargo:rerun-if-env-changed. Task #321 fixed crates/ml/build.rs (commite3d082968) but missed 6 sibling build.rs files. Each reads CUDA_COMPUTE_CAP without registering the rerun directive, so cargo sees no env-change between e.g. SM 89 (L40S) → SM 90 (H100) workflow re-submissions and cache-hits the wrong-arch cubin from the previous run. At runtime, the H100 fails to load the SM 89 cubin with CUDA_ERROR_NO_BINARY_FOR_GPU on rmsnorm — exactly what killed train-multi-seed-vlv8c (commit0371d6a76, post-Class-B chain). Files (all add `println!("cargo:rerun-if-env-changed=CUDA_COMPUTE_CAP")` just before the env::var() read): - crates/ml-dqn/build.rs (rmsnorm — root cause of vlv8c failure) - crates/ml-ensemble/build.rs - crates/ml-explainability/build.rs - crates/ml-ppo/build.rs - crates/ml-supervised/build.rs - crates/ml-core/build.rs Atomic single commit per feedback_no_partial_refactor. 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.