bde510bf8e
Eliminate all CPU fallback paths in the PPO trainer, PPO hyperopt adapter, and DQN hyperopt adapter. On H100, every GPU operation must succeed or abort — silent CPU fallback runs at 1/10th throughput and produces stale/inconsistent results. PPO trainer (5 sites): - GPU unavailable → hard error (was warn + CPU fallback) - Collector init, episode reset, collection, weight sync → hard errors - Test updated: GPU batch limit test expects error without CUDA PPO hyperopt adapter (8 sites): - ensure_gpu_data() now returns Result<(), MLError> - Features/targets upload, collector init, weight sync → hard errors - gpu_collect_trajectories() now returns Result<TrajectoryBatch> - Experience collection caller uses ? instead of Option fallback - GPU backtest failure → hard error DQN hyperopt adapter (3 sites): - CUDA synchronize between trials → hard error - GPU backtest None on CUDA → hard error (upstream of extract_objective) - extract_objective: panic! on CUDA build if backtest_metrics is None - CPU backtest path guarded by #[cfg(not(feature = "cuda"))] continuous_ppo.rs (1 site): - clip_grads passed &Device::Cpu instead of actor's actual device - Forces CPU roundtrip for gradient norm computation on every mini-batch - Fixed: passes `device` (from self.actor.device()) — stays GPU-resident gpu_experience_collector.rs (1 site): - cuCtxSetLimit(STACK_SIZE) failure: warn → hard error - 16KB stack is required — kernel segfaults without it Co-Authored-By: Claude Opus 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.