6df3284d0d
Both parse_mbp10_file and parse_mbp10_streaming wrote the single MBP-10 update event's (price,size) into levels[0] via update_level(0,...) and then copied the authoritative book from mbp10.levels[1..] — starting at index 1, so the corrupt L0 was never overwritten with the real mbp10.levels[0]. Result on real cluster data (ES.FUT 2025-Q1 front-month, 2M records): 14.9% crossed books, 40% wide-L0 (>5pt) spikes, vs the raw inside quote which is pristine (0.016% crossed, 0% wide, 0.25pt median). Every mid/microprice/spread/OFI-L0 feature, the mid-based MTM reward, and the LOB-sim fill reference read this phantom L0. Extract the level-copy into a tested helper apply_mbp10_record() that: - copies the full mbp10.levels[0..max] canonical post-update book (including L0, the inside quote); - preserves trade_count on Trade-action records (a LIVE encoder feature [17]=log1p(trade_count) + the inter-snapshot trade delta in the ml-alpha/ml-features loaders) — naively dropping update_level(0) would have silently zeroed it. Adds RED-verified unit tests for no-crossed-L0 and trade_count semantics. cargo test -p data --lib: 377 passed. Sidecars (.predecoded.bin) are mtime/size-keyed and will NOT auto- invalidate on this parser change — they must be regenerated separately (local + PVC). Co-Authored-By: Claude Opus 4.8 (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.