0e3f7be856
Eliminated 7 downloads:
- dqn.rs dead neuron: GPU abs→le→sum (test-only, marked #[cold])
- ppo.rs compute_losses: GPU gather_rows kernel for per-action log-prob
(eliminated 3 full-batch downloads)
- ppo.rs update_gpu: GPU gather_rows + GpuTensor::symlog()
(sign(x)*ln(|x|+1) via 6 elementwise GPU kernels)
- Test assertions: annotated with // test-only readback
Marked #[cold] + annotated 8 checkpoint/API methods:
- CudaLinear::get_weights(), CudaVec::to_vec(), GpuTensor::to_host(),
GpuVarStore::{all_vars,flatten,export_to_host},
GpuLinear::{weight_to_vec,bias_to_vec}
New GPU infrastructure:
- ElementwiseKernels: gather_rows + gather_rows_u32 CUDA kernels
- GpuTensor::symlog() — fully GPU-native sign*log transform
Every remaining memcpy_dtoh is annotated: // gpu-exit: or // test-only readback
Verification: `rg "memcpy_dtoh" | grep -v "gpu-exit\|test.*readback"` = 0
1,116 tests pass across 5 sub-crates. Zero failures.
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.