ff562b07b2
Per-step (16K/epoch): - total_loss, mse_loss, grad_norm, q_divergence: CudaSlice → pinned device-mapped. GPU kernels write via dev_ptr, CPU reads via host_ptr. Zero copies in replay_adam_and_readback (was 4x cuMemcpyDtoHAsync). - readback_scalars_sync, execute_train_scalars_only: sync DtoH → direct pinned read after cuStreamSynchronize. Per-50-steps: - eval_v_range: cuMemcpyHtoDAsync → pinned host write (CPU writes v_min/v_max, GPU reads via dev_ptr, no copy). - per_branch_q_gaps: cuMemcpyHtoD → pinned host write (CPU writes 4 Q-gaps, GPU reads via dev_ptr in qlstm_step + liquid_tau_rk4_step). - q_stats + q_out readback: stack destination → pinned DtoHAsync destination (DMA-capable, faster async transfer). Structural changes: - launch_loss_reduce signature: &CudaSlice<f32> → u64 dev_ptr - loss_gpu_buf/grad_norm_gpu_buf → loss_gpu_ptr/grad_norm_gpu_ptr (u64) - memset_zeros on CudaSlice → cuMemsetD8Async on dev_ptr - 6 new pinned allocations in constructor, freed in Drop Only cuMemcpy remaining: constructor init, checkpoint save/restore, xavier_init upload, trajectory backtracking, causal intervention, compute_q_values inference. All per-step training copies eliminated. 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.