9ccc37749a
Bottleneck A of two: the loop in `mbp10_to_imbalance_bars` that calls `extract_trades_from_dbn_file` + `filter_front_month_mbp10` per file ran serially across the 9 quarterly DBN files. Each file is independent (different contract universe per quarter), the front-month filter is purely intra-file, and the final `all_trades.sort_by(|a, b| timestamp)` re-sequences across files — so reduce order across files is irrelevant to correctness. Switched the loop to `dbn_files.par_iter().filter_map(...).collect()`, mirroring the trades_loader-side pattern in `precompute_features.rs:551-565`. Per-file logging (raw count → filtered front-month count) preserved verbatim. On the `ci-compile-cpu` 28-core node decoding 9 .dbn.zst files, the file-decoder/zstd-decompress phase should drop from ~9× single-file latency to ~1× — bounded by the slowest single file. This is the trivial half of the parallelisation. Bottleneck B (the sequential `ImbalanceBarSampler` pass over the concatenated trade list) follows in the next commit with time-bucket sharding + warmup overlap + bit-equivalence test, mirroring the SP20 OFI pattern at `crates/ml-features/src/ofi_calculator.rs::compute_ofi_per_bar_parallel`. Build: `cargo check -p ml-features` clean. 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.