660f02ff40
The async-diag merge (3c0d26292, building ond9cb14f1b+673b04a8d) wired the GPU side of cross-stream eval pipelining correctly (training stream `cuda_stream`, eval stream `validation_stream`, `cuStreamWaitEvent` barrier on `train_done_event`) but left a host-side `stream.synchronize()` inside `gpu_backtest_evaluator::launch_metrics_and_download` that blocked the CPU thread for the FULL eval drain (~25-30s/epoch on L40S). The host thread is the same one that submits the next epoch's training kernels via `run_full_step` — so until eval drained, training submission was gated on it, defeating the dedicated `validation_stream`. Fix: split metrics readback into record/consume halves at the buffer-readback layer: * `launch_metrics_and_record_event` — submits the metrics kernel + DtoD-async copies of `actions_history_buf` / `intent_mag_buf` / `picked_action_history_buf` into mapped-pinned mirrors, then records `eval_done_event`. Returns immediately. * `consume_metrics_after_event` — `event.synchronize()` (the SOLE host wait per epoch boundary), then `read_volatile`s mapped-pinned `metrics_buf`. After the event syncs, the four action-distribution helpers read directly from the now-coherent mapped-pinned mirrors — eliminating four synchronous DtoH copies that violated `feedback_no_htod_htoh_only_mapped_pinned.md`. Caller migration (per `feedback_no_partial_refactor.md`): * `evaluate_dqn_graphed` (existing public API) becomes a thin sync wrapper: launch_async → consume. ABI unchanged. * New `evaluate_dqn_graphed_async` for the pipelined path. * `evaluate` / `evaluate_ppo` / `evaluate_supervised` migrated inline to launch+consume (no caller pipelines them). * `compute_validation_loss` replaced by `launch_validation_loss` + `consume_validation_loss`. Training loop now consumes pending at epoch start (cached_async_val_loss = epoch N-1's val_sharpe) and launches at epoch end (sentinel-only return). Final post-loop consume drains the LAST epoch's launch so smoke tests / hyperopt that read `last_eval_direction_dist` see the most recent epoch's data. Mathematical identity preserved: every val_sharpe consumed is bit-identical to what the prior synchronous flow would have produced for the same epoch — only the timing of the host parse differs (one epoch later, matching the existing `cached_async_val_loss` lag semantics; HEALTH_DIAG `val [...]` / `val_dir_dist` / `val_picked_dir_dist` emit moves with the consume). Audit: docs/dqn-gpu-hot-path-audit.md Fix 4. Out of scope: periodic chunk-level `stream.synchronize` calls in `submit_dqn_step_loop_cublas` (lines ~1596 + ~1863) for kernel-error detection — host-blocking but smaller in aggregate; future work. Workspace baseline preserved: 13 warnings. 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.