facbf76eb5
Pearl 5's online_taus/target_taus/cos_features were declared as CudaSlice<f32> (device-only), populated via upload_f32_via_pinned which does a DtoD copy from a separate mapped-pinned staging buffer. The DtoD inside CUDA Graph capture triggers CUDA_ERROR_STREAM_CAPTURE_INVALIDATED and the 'continuing ungraphed' fallback observed in smoke-test-hhr5q. This violates feedback_no_htod_htoh_only_mapped_pinned: the rule is mapped-pinned (cuMemHostAlloc DEVICEMAP) for ALL CPU↔GPU paths. No DtoD copies, no HtoD copies, no exceptions. Fix: convert all 3 buffers (online_taus, target_taus, cos_features) to MappedF32Buffer per-branch [MappedF32Buffer; 4] arrays. Host writes go directly to host_ptr; IQN kernel reads dev_ptr of the same memory — no copy step at all. The mem::swap pattern is replaced with pure selection: activate_branch_taus sets active_branch_idx; kernel launch sites index online_taus_per_branch[active_branch_idx].dev_ptr. Eliminates upload_f32_via_pinned calls for these buffers entirely. Refresh becomes a host write to mapped-pinned host_ptr at fold boundary; subsequent kernel launches see the write through the mapped-pinned coherence guarantee after stream sync. cargo check + cargo build --release + cargo test --lib (sp4 sp5 state_reset_registry: 13/13) all clean. Sanity grep for upload_f32_via_pinned in gpu_iqn_head.rs returns zero. Co-Authored-By: Claude Sonnet 4.6 <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.