6bb8f87805
- GpuBatch.indices: GpuTensor → CudaSlice<u32> (fixes 2402 compute-sanitizer memory errors from per_update_priorities_kernel reading u32 from bf16 buffer) - fused_training PER: eliminate bf16→host→u32→GPU roundtrip, pass u32 directly - train_step accumulation: GPU DtoD concat for CudaSlice<u32> indices - grad_norm kernel: float accumulator via separate CudaSlice<f32> buffer (bf16 sum-of-squares overflows at 147K params; atomicAdd on native float) - grad_norm finalize kernel: runs OUTSIDE CUDA graph, converts float→bf16 L2 norm - Adam + clip_grad + clipped_saxpy kernels: read float sum-of-squares directly - training guard: read loss/grad from fused trainer's GPU buffers (not GpuTrainResult's hardcoded zeros), raw_ptr() for kernel args (no event tracking) - guard accumulator: reset between epochs for per-epoch metrics - Q-stats padding: pad input to config.batch_size for CUTLASS tile alignment - training_profile tests: update BF16-tuned values (spectral_norm 1.5, noisy_sigma 0.3) 895/895 unit tests pass, 5/9 smoke tests pass (remaining 4 need loss kernel float arithmetic — C51/MSE softmax overflows bf16 after ~100 training steps). Co-Authored-By: Claude Opus 4.6 (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.