952302149e
Implements the CUDA kernels (`interpolate_input`, `perturb_dimension`)
and wires `compute_gpu` in `integrated_gradients.rs` to run the full
IG algorithm on-device. Replaces the stub that previously returned
`MLError::ModelError("IG GPU kernels not available: cubins not yet
wired")` and fell back to CPU.
Algorithm: for each of `num_steps` interpolation points along the
baseline->input path, compute central-finite-difference gradients for
all `num_features` dimensions via two GPU forward passes per feature.
Single cubin (`ig_kernels.cubin`) compiled via build.rs (following the
crates/ml/build.rs pattern — nvcc, `-arch=sm_\${CUDA_COMPUTE_CAP}`, O3,
f32) and embedded with `include_bytes!`. The `forward_fn` consumers
operate on `GpuTensor`, so no dtoh round-trips inside the inner loop —
only the scalar `[1]` tensor output of each forward pass is pulled to
host (via `to_scalar`) per gradient sample. The three scratch buffers
(`interpolated`, `x_plus`, `x_minus`) are allocated once outside the
step loop and reused across all steps and features. No atomicAdd —
the kernels are trivial 1-D element-wise writes.
Tests: existing CPU tests pass unchanged. Added GPU smoke test
`test_ig_compute_gpu_linear_model` (gated `#[cfg(feature = \"cuda\")]`
+ `#[ignore]`) that builds a linear model as a `GpuTensor`-native
forward (elementwise mul + mean), verifies the completeness axiom
within 1%, and cross-checks GPU attributions against the CPU path
within 5%. Passes locally on RTX 3050 Ti (sm_86).
Removes 3 TODO markers and the embedded `_IG_CUDA_SRC` const that
were awaiting this work, along with the `#[allow(unused_variables)]`
stub attribute on `compute_gpu`.
Co-Authored-By: Claude Opus 4.7 (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.