a72c5743fe
wild (https://github.com/davidlattimore/wild) is a Rust-native linker, typically 10-30% faster than mold on large release-LTO links. We have ~5 service binaries each doing release-LTO link, so the saving is meaningful: ~30-90 sec wall-time on a fresh compile. Approach (CI-only swap, zero local-dev impact): 1. Dockerfile.ci-builder-cpu installs wild 0.8.0 alongside mold (both linkers present; revert path is trivial). 2. .cargo/config.toml keeps `-fuse-ld=mold` as the file default so `cargo build` works locally without requiring wild on PATH. 3. compile-and-deploy-template.yaml's compile-services script does an in-place sed substitution `mold -> wild` immediately before `cargo build`, gated on `command -v wild` so a missing binary silently falls back to mold instead of failing the build. Why sed-in-script over RUSTFLAGS env var: RUSTFLAGS env var REPLACES the entire target.<triple>.rustflags array (per cargo docs precedence: env > target > build, mutually exclusive — they do NOT merge), which would silently drop our existing -Wl,-z,relro/--as-needed/target-cpu flags. Sed swap edits one token while preserving everything else. Validation: - python3 yaml.safe_load_all parses the template - cargo check --workspace --release --locked succeeds locally (still using mold per the unchanged config.toml default) - Dockerfile syntax visually verified; wild tarball URL confirmed live via curl https://api.github.com/repos/davidlattimore/wild/releases/latest 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.