c03cf9aa38
Audit (fxt-data-audit on ES.FUT_2024-Q1) revealed real source-data outliers that the hardcoded < 1e8 threshold didn't catch: - bid_min = -$4.85 (negative bid) - bid_p1 = $64.15 (1% of bids in sub-$100 range, far below ES) - ask_max = $53,012 (10x above any plausible ES price) The < 1e8 threshold = $100M was useless: never triggered on real ES. Fix: parameterize the range. min_reasonable_px / max_reasonable_px as per-backtest fields in UniformSimParams, ResolvedSimVariant, and BatchedSimConfig (defaults 0.0 / f32::INFINITY = no-check, preserves existing test fixtures). Sweep_smoke.yaml sets 1000/20000 for ES futures — catches all observed outliers without rejecting any plausible price. BacktestHarness calls upload_price_range() once after creating the sim so the bounds are active before the first apply_snapshot. CUDA kernel book_update_apply_snapshot gains two new args (min_reasonable_px[n_backtests], max_reasonable_px[n_backtests]) that replace the hardcoded > 0.0f && < 1.0e8f checks at both the top-of-book gate and the per-level sanitization pass. Test price_range_rejection_skips_snapshot validates: sub-$1000 snapshot, super-$20000 snapshot, and negative bid all skip with snapshots_skipped counter increment; valid ES snapshot passes through. 17/17 stop_controller tests pass. 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.