3836e25783
Replaces TWO host loops in step_decision_with_latency with two GPU kernels: 1. snapshot_pos_state — replaces the host-side snapshot_realized_pnl + snapshot_position_lots + snapshot_open_horizon_mask trio (3 separate memcpy_dtoh per decision). Now one kernel launch writes prev_pos_lots_d / prev_realized_pnl_d / prev_open_horizon_mask_d directly on the device. 2. detect_close_transitions_batched — replaces the host close-detect loop that called read_pos per close-eligible backtest (up to n_backtests memcpy_dtoh per decision). Now one kernel writes closed_horizon_mask_d + realised_return_d on the device, and isv_kelly_update_on_close consumes them with no host roundtrip. At n_parallel=140 these two loops together accounted for ~350M+ small host roundtrips per quarter. Combined with P2 the latency-path of step_decision_with_latency is now fully GPU-resident. isv_kelly_update_on_close kernel always launches (skips backtests with mask=0 internally) rather than gating via a host any_close check. All P1+P2 regression tests pass through the new GPU close-detect path (at n=1 with uniform config + immediate-fill latency, no close happens in the cold-start tests since they only check market_target; the close path is exercised indirectly). 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.