86f7f1fa76
Codebase audit identified 23 findings across 4 dimensions (production safety, code health, deployment readiness, test quality). This commit fixes all of them. Broker execution layer (was entirely stubbed): - Real IBKR TWS client via ibapi crate (950+ lines, feature-gated) - ICMarkets ctrader-openapi now always-on (removed feature flag) - Real broker routing with health monitoring and exponential backoff reconnect - Validated against live IB Gateway Docker (6/6 connectivity tests pass) Deployment blockers: - Fixed 6 broken Dockerfiles (removed COPY foxhunt-deploy) - Created foxhunt K8s namespace, secret templates, migration job - Added liveness probes to all 7 K8s services - IB Gateway manifest (ghcr.io/gnzsnz/ib-gateway:stable) - IBKR credentials in Scaleway Secret Manager via Terragrunt - Fixed port collisions and mismatches across services Production safety (9 critical + 6 high/medium fixes): - Asset-class-specific VaR volatility (not flat 2%) - Real parametric VaR with z-score 95th percentile - Kyle's lambda regression (100-bar rolling window) - Per-feature running statistics from historical data - VWAP-based slippage reference, regime duration tracking - Real Databento JSON parsing for OHLCV/Trade/Quote Code health: - Removed #![allow(dead_code)] from ml, data, config - Fixed log:: → tracing:: in 4 production files - Removed dead workspace deps (ratatui, crossterm) Verified: cargo check --workspace (0 errors), trading_engine 330 tests pass. 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.