jgrusewski 116f04968b feat: major kernel modules load from precompiled cubins — zero runtime nvcc
Replace OnceLock<Ptx> + compile_ptx_for_device() with include_bytes! +
Ptx::from_binary() in 13 Rust files:

- gpu_action_selector.rs (epsilon_greedy)
- gpu_statistics.rs (batch_statistics)
- gpu_training_guard.rs (training_guard)
- signal_adapter.rs (signal_adapter)
- gpu_monitoring.rs (monitoring_reduce)
- gpu_curiosity_trainer.rs (curiosity_training)
- gpu_attention.rs (attention forward + backward)
- gpu_backtest_evaluator.rs (env, metrics, gather, ppo, supervised, dqn)
- gpu_experience_collector.rs (experience, nstep, her_episode)
- gpu_her.rs (her_episode, her_relabel)
- gpu_iql_trainer.rs (iql_value)
- gpu_iqn_head.rs (iqn_dual_head)
- gpu_ppo_collector.rs (ppo_experience)

Remaining runtime compilation: inline utility kernels in gpu_dqn_trainer.rs
(grad_norm, adam, ema, saxpy, zero, etc.) — small kernels that compile in ms.
883/887 tests pass (4 pre-existing failures in data_loader/hyperopt).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-26 00:42:39 +01:00

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.

Description
No description provided
Readme 849 MiB
Languages
Rust 88.2%
Cuda 7.7%
Python 1.3%
Shell 1.1%
PLpgSQL 0.8%
Other 0.8%