27f536ba6e
Adds a no-wrapper L40S smoke runner alongside the nsys/sanitizer templates. Same compile + checkout + data-mount layout, no profiler binary, no sanitizer instrumentation, no MinIO artefact upload — just a plain test execution against the full training-data PVC. Use case: validate a smoke test passes on the real 27-month dataset when local data is too short (laptop only has 1 quarter of MBP-10/ trades, fxcache truncates below the 10-month minimum). - infra/k8s/argo/smoke-test-template.yaml: WorkflowTemplate smoke-test, entrypoint smoke-run, default test multi_fold_convergence::test_multi_fold_convergence. - infra/k8s/argo/kustomization.yaml: register the new template. - infra/k8s/argo/argo-workflow-netpol.yaml: extend the sanitizer/nsys NetworkPolicy podSelector to include smoke-test (identical egress requirements: git fetch, ci-builder pull, training-data PVC). - scripts/argo-smoke.sh: thin wrapper mirroring argo-nsys.sh / argo-sanitizer.sh (--multi-fold default, --test, --ref, --watch). Verified: kustomize dry-run clean, kubectl apply -k creates the template + reconfigures the netpol live in foxhunt namespace.
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.