fa4c649338
Essential workflow fixes: - ci.yml: add SQLX_OFFLINE=true, replace fictitious cargo subcommands (test-unit, ci-lint, audit-deps, etc.) with real cargo commands, remove broken 9-way test matrix, remove instrument-coverage RUSTFLAGS - test.yml: add SQLX_OFFLINE=true to build-check job, fix conflicting clippy flags (-D and -W on clippy::all), update JWT secret to 32+ chars - compilation-guard.yml: add SQLX_OFFLINE=true, remove references to non-existent paths (services/trading-engine, crates/common/types), remove dangerous auto-commit-to-main, remove MIRI on missing packages Deleted duplicates (justification): - comprehensive_testing.yml: duplicate of comprehensive-testing.yml (same purpose, underscore vs hyphen naming) - production-deploy.yml: duplicate of production-deployment.yml (both named "Production Deployment Pipeline", this one has stale paths) - coverage-fixed.yml: duplicate of coverage.yml (uses deprecated actions-rs/toolchain@v1 and actions/cache@v3) - performance.yml: duplicate of benchmark_regression.yml (both "Performance Regression Detection", this one less mature) - quality-baseline.json: not a workflow, stale fake data (999 warnings) - quality-metrics.json: not a workflow, stale fake data Co-Authored-By: Claude Opus 4.6 <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.