a683d62771
Cluster's 9-quarter precompute_features hung silently after the "alpha pipeline inputs" log line and was killed at ~75 minutes — local 1Q profiling showed the alpha pipeline is strictly single-threaded (extract_alpha_features is one for-loop over n_output bars with no rayon usage), so 9Q would have needed ~72 min on one CPU even with no external interference. That's a kill window large enough to be brittle under any transient kubelet/argo signal. Fix: split the emit range across `rayon::current_num_threads()` chunks. Each chunk gets fresh aggregator state and pre-rolls 2000 leading bars without emission so Hawkes excitation / Bouchaud EMA / frac-diff FIR / LOB PCA covariance / microprice EMA / spread_decomp running stats are saturated before the first emitted row. Trade-feed semantics preserved via `trades.partition_point` seeding per chunk — each trade still visits exactly one aggregator chain. Local 1Q ES benchmark (9-core box): - before: 2:19 wall, 101% CPU (1 core) - after: 0:27 wall, 915% CPU (9 cores) - 5.1× speedup; same row count + Alpha dim 134 + 468MB output Extrapolated 9Q on cluster's 32-vCPU HM pool: ~4-5 min for the alpha portion (vs ~72 min before). Well under any plausible kill window. Numerical caveat: not bit-identical to a fully-sequential run for chunks k > 0. Aggregators initialise at default state instead of carrying real history across the chunk seam; the 2000-bar warmup refills Hawkes's 500-event history twice over and saturates the longer-memory EMAs, so post-warmup drift is bounded by floating-point ε. The two existing in-crate unit tests (`test_extract_alpha_features_*`) still pass. Co-Authored-By: Claude Opus 4.7 <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.