d83c1d4c48
batched_forward.rs (-698 lines): - Delete sgemm_layer, sgemm_layer_raw (F32 cublasSgemm) - Delete 4 F32 bias launchers (launch_add_bias_relu/_raw, launch_add_bias/_raw) - Delete forward_online_bf16, forward_target_bf16 (conversion layer paths) - Delete bf16_weight_ptrs, 6 dead BF16 buffer accessors, raw_u16_ptr - Delete 15 CudaSlice<u16> internal BF16 mirror buffers from CublasForward - Delete F32 kernel fields (add_bias_relu_kernel, add_bias_kernel, f32_to_bf16_kernel) - compile_bias_kernels returns only BF16 kernels now gpu_dqn_trainer.rs (-199 lines): - Delete bf16_params_buf, bf16_target_params_buf mirror infrastructure - Delete launch_segmented_bf16_convert, launch_bf16_convert_online/target - Delete bf16_goff_byte_offsets, bf16_padded_total, bf16_mirrors_initialized - Delete bf16_to_f32_kernel accessor, raw_device_ptr_u16 helper - Remove sync_target_bf16 call from target_ema_update fused_training.rs (4 size_of::<f32> → size_of::<half::bf16>): - td_errors DtoD copy, ensemble buffer memset, dueling/branching weight clones 1722/1722 tests pass. Zero dead F32 code remains. 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.