91535deb1a
Eight sub-crates whose Rust modules in crates/ml/src/ are leaf-level
(no other ml/src module references them) are now feature-gated:
ml-backtesting → feature `backtest-mod` (ml::backtesting)
ml-paper-trading → feature `paper-trading-mod` (ml::paper_trading)
ml-stress-testing → feature `stress-testing-mod` (ml::stress_testing)
ml-explainability → feature `explainability-mod` (ml::explainability)
ml-universe → feature `universe-mod` (ml::universe)
ml-regime-detection → feature `regime-detection-mod` (ml::regime_detection)
ml-validation → feature `validation-mod` (ml::validation)
ml-data-validation → feature `data-validation-mod` (ml::data_validation)
Added `full-stack` feature aggregating all eight, included in `default`.
Callers using `ml.workspace = true` see no behavioral change because
the workspace dep keeps default-features = true.
Per-service ml-* dep count (cargo tree):
ml-training-service 24 → 16 (already used default-features = false)
trading-agent-service 22 → 14 (already used default-features = false)
trading-service 23 → 22 (still uses default = true; gated
sub-crates would drop with future
default-features = false flip)
backtesting-service 23 → 22 (same — future commit can drop them)
cargo check --workspace passes (only pre-existing 11 ml warnings).
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.