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Phase 2: Replace 1-warp/sample fused kernels with cuBLAS SGEMM batched forward/backward. - batched_forward.rs: cuBLAS SGEMM forward (10 GEMM + bias/ReLU per pass) - batched_backward.rs: cuBLAS SGEMM backward (chain rule via GEMM, no atomicAdd) - c51_loss_kernel.cu: standalone C51 distributional loss (256 threads, 2KB shmem) - c51_grad_kernel: dL/d_logits with dueling routing for cuBLAS backward - BF16 alignment fix: pad offsets to even for short2 vectorized loads - Training step: 10.7ms → 0.7ms (15x) on RTX 3050 Phase 3: Unified cuBLAS Q-forward + dead code elimination (-4,400 lines net). - Rewrite experience collector: timestep loop + cuBLAS replaces monolithic 3,272-line kernel - Delete dqn_training_kernel.cu (1,385 lines) — replaced by dqn_utility_kernels.cu (118 lines) - Delete dqn_experience_kernel.cu (3,272 lines) — replaced by experience_kernels.cu (656 lines) - Remove BF16 warp-matvec helpers from common_device_functions.cuh (-159 lines) - Remove dead methods/fields from GpuDqnTrainer (-500 lines) - Experience collection: 348ms → 12ms (29x) on RTX 3050 - No fallback paths — cuBLAS is the only Q-forward implementation - All 1,514 tests pass, GPU smoke test verified with real data 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.