f92c9d5f79
Eliminate candle-core/candle-nn from the KAN and Diffusion model forward
paths in ml-supervised. All dense layers now use cuBLAS sgemm via the new
gpu_tensor module. Element-wise ops (SiLU, sigmoid, tanh, exp) use host
roundtrips for now; fused CUDA kernels are a follow-up.
Changes:
- Add gpu_tensor.rs: GpuTensor (CudaSlice<f32> + shape), GpuLinear
(cuBLAS sgemm), and ~30 element-wise GPU ops
- Rewrite kan/{spline,layer,network}.rs to use GpuTensor instead of
candle_core::Tensor and candle_nn::{VarBuilder,Linear}
- Rewrite diffusion/{denoiser,noise,sampler}.rs to use GpuTensor and
GpuLinear instead of candle_nn::Linear
- Update ml crate trainable adapters (kan/trainable.rs,
diffusion/trainable.rs) to bridge Candle<->GpuTensor at the
UnifiedTrainable interface boundary
- Update ensemble inference adapters for both models
- Add cudarc 0.17 with cublas feature to ml-supervised Cargo.toml
- Candle deps retained in ml-supervised for unconverted models (TFT,
Liquid, Mamba, xLSTM) -- will be removed once all 8 models are
converted
Net: -424 lines, 509 -> ~453 Candle refs remaining (KAN: 0, Diffusion: 0)
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.