bf40677b22
Remove 3 per-step CPU-GPU synchronization points that dominated the 300ms/step wall time (pure compute for 323K params is <1ms on H100): 1. target_ema_update: removed cuStreamSynchronize — stream ordering guarantees EMA kernel sees Adam-updated weights (same stream). 2. apply_attention_forward: removed cuStreamSynchronize — save_h_s2 is written by graph_forward on the same stream. 3. Actions DtoH round-trip: GpuBatch.actions changed from GpuTensor (bf16) to CudaSlice<i32>. Eliminates synchronous GPU→CPU→GPU round-trip (bf16 download → i32 cast → upload) every training step. Actions now flow u32 → i32 via async DtoD in the replay buffer. Throttle expensive per-step features: 4. Gradient vaccine: runs every 10 steps (was every step). Full ungraphed forward+backward pass was ~100-150ms — the single largest bottleneck. 10-step amortization preserves gradient quality with ~90% cost reduction. 5. Causal intervention interval: 10 → 100. Each invocation runs 14 cuBLAS forward passes + sync + readback. Dead code removed: - u32_slice_to_gpu_tensor_gpu (56 lines) — obsolete bf16 cast path - u32_to_f32 CastKernel field — no longer needed - Old train_step fallback in training_loop — fused path only Expected: ~300ms/step → ~25ms/step → ~50s/epoch (was 614s) 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.