0b3176e119
Branch worktree-agent-af5e15a7, commit 952302149. Full GPU IG
implementation — replaces the compute_gpu stub in
integrated_gradients.rs with a real kernel-driven loop. New
ig_kernels.cu (interpolate_input + perturb_dimension), new
crates/ml-explainability/build.rs (matches the crates/ml/build.rs
nvcc pattern for sm_89 L40S / sm_90 H100). Scratch buffers
(d_interpolated, d_x_plus, d_x_minus) allocated once outside the
step loop and reused across num_steps × num_features perturbations.
forward_fn scalar output pulled via GpuTensor::to_scalar; no
full-tensor dtoh inside the hot loop. No atomicAdd, deterministic
launch config.
4/4 existing CPU tests pass unchanged. New GPU smoke test
(test_ig_compute_gpu_linear_model) validates completeness axiom
within 1% and GPU↔CPU parity within 5% on local RTX 3050 Ti.
Motivation: next-run diagnosis of the L40S train-mdh86 regression
(see /tmp/l40s_diag/health.log). IG will let us compare per-feature
attributions at checkpoints ep 6 (healthy, Sharpe +34), ep 8
(pre-collapse, grad_ratio_mag_dir=115 down from 23754), and ep 10
(Sharpe regressed to -66) to determine whether the magnitude-vs-
direction gradient imbalance is driven by genuine feature weighting
or by a gradient-shape pathology that a multi-task balancer can fix.
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.