b0fdf9b3e4
Per user directive, ran rg scans for PINMEM, ROMEM, LOCKHOT, BORROW, CPURO and populated the scoreboard with concrete file:line findings. PINMEM: 10 new findings (PINMEM-011..019, plus rescan confirmed 001-010) - Biggest surface: gpu_experience_collector.rs (7 htod sites) - High priority: gpu_iqn_head.rs dtod_copy for iqn_rewards/iqn_dones (PINMEM-013, score 25.0, switch to async-dtod, E=1) ROMEM: 5 new findings (004 expanded, 005-008 added) - ROMEM-004 now covers ~40 cuBLAS/cuBLASLt/cuDNN workspace casts across shared_cublas_handle.rs, gpu_iql_trainer.rs, gpu_iqn_head.rs, gpu_curiosity_trainer.rs, cublaslt_debug.rs — bulk false-positive candidates (FFI convention, not actual RO writes) - ROMEM-007 adds 6 more device-mapped pinned write sites (same pattern as ROMEM-001/002 — benign cuMemHostAllocMapped) LOCKHOT: 5 new findings (006-010) - LOCKHOT-006/008: tokio::sync::Mutex<PPO> and RwLock<TLOBTransformer> held across .await — deadlock risk. High priority. - LOCKHOT-007: Arc<Mutex<VecDeque<f64>>> history locks per step BORROW: 1 new finding (002) - BORROW-002: RefCell<PPO> × 2 in validation/ppo_adapter.rs — documented single-threaded, needs invariant verified CPURO: deferred — .len()/.shape() scan returns hundreds of mostly-Vec matches; left CPURO-000 task for next iter to classify per-site. Scoreboard now has ~40 open findings across 12 active categories.
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.