68b6aa8313
Full migration off Scaleway Container Registry to internal GitLab registry backed by MinIO S3. All 4 images (ci-builder, ci-builder-cpu, foxhunt-runtime, foxhunt-training-runtime) rebuilt in internal registry. Registry & images: - All image refs → gitlab-registry.foxhunt.svc.cluster.local:5000/root/foxhunt/ - imagePullSecrets: scw-registry → gitlab-registry - Kaniko build template: two-step DAG (git-clone → kaniko-build) with shared PVC - Kaniko layer cache enabled at root/foxhunt/cache - AWS_ACCESS_KEY_ID: $SCW_ACCESS_KEY → $MINIO_ACCESS_KEY in .gitlab-ci.yml Network policies: - ci-pipeline: add HTTP/80, registry/5000, webservice/8181 egress rules DNS & Tailscale proxy cleanup: - Remove ci, prometheus, monitor DNS records (no longer exposed) - Rename s3 → minio DNS record - Remove Argo UI, Prometheus, monitor nginx server blocks - Remove argo-htpasswd volume mount - Tailscale proxy nodeSelector: infra → platform Terraform cleanup: - Delete infra/modules/registry/ (SCW CR namespace) - Delete infra/modules/object-storage/ (SCW S3 buckets) - Delete infra/modules/secrets/ (SCW secrets) - Delete corresponding live configs - TF state backend: S3 → GitLab HTTP Argo workflows: - Add events/ (GitLab push eventsource + ci-pipeline sensor) - ci-pipeline + training templates: SCW → internal registry - Delete obsolete compile-training-template.yaml 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.