bfd2253a9d
- GpuAdamW: add grad_scale param to CUDA kernel — gradient clipping was computed but never applied - PPO load_checkpoint: load .actor.bin/.critic.bin weights (was Xavier re-init with TODO) - CudaLinear::set_weights(): new method for checkpoint weight import - TLOB/KAN/TGGN/Liquid backward: real GPU backprop via GpuLinear::backward() + GpuAdamW - Mamba2 backward: SPSA gradient estimation replacing random pseudo-gradients (Spall 1992) - Mamba2 adapter: wire SPSA backward with GPU-cached input/target/loss tensors - TFT/xLSTM/Diffusion backward: explicit errors routing to native train() methods - TLOB load_checkpoint: load .weights.json via GpuVarStore::import_from_host() - train_baseline_supervised: 30 candle→native API fixes (Tensor/Device eliminated) - evaluate_baseline: 38 candle→native API fixes (DQN/PPO/supervised GPU eval paths) - evaluate_supervised: candle→native fixes (forward_loss instead of forward+compute_loss) - cuda_test: rewrite to cudarc 0.19 (MlDevice, CudaSlice, memcpy) - train_baseline_rl: Device→CudaContext for GPU double-buffer - hyperopt_baseline_rl: CudaContext→MlDevice::cuda() for device pool - xLSTM deterministic test: fix for stateful LSTM (hidden state changes between predictions) - Liquid early stopping test: deterministic data for reliable convergence - Mamba2Config: add spsa_epsilon field (default 0.01, serde backward-compatible) - Clean stale candle comments from trainer, inference_validator, mamba optimizer 1853 tests pass (302+359+168+169+855), 0 failures, 0 clippy warnings, 8/8 examples compile. 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.