673b04a8d4
Best-checkpoint save (val Sharpe improvement, ~30% of epochs in convergent runs) blocked the epoch loop for 20-40s on each improvement: serialize_model() chained N (~26) per-tensor DtoH downloads via GpuTensor::to_host → memcpy_dtoh, each forcing an implicit stream sync on a busy training stream. The DtoH chain also violates feedback_no_htod_htoh_only_mapped_pinned.md (only cuMemHostAlloc DEVICEMAP allowed for CPU↔GPU paths). Plan B: - Introduce snapshot_model_to_pinned (mod.rs): allocate one MappedF32Buffer sized for all named weight slices concatenated, cuMemcpyDtoDAsync each slice into the buffer's device pointer (which aliases the host page), single stream sync, copy bytes out to a Send + 'static Vec<u8>. One sync per snapshot, replaces N. - serialize_snapshot_bytes (mod.rs): pure-CPU safetensors construction from CheckpointSnapshot. Static — callable without &self, so the worker can move the snapshot across thread boundary. - handle_epoch_checkpoints_and_early_stopping on val-Sharpe improvement: save_best_gpu_params (DtoD, fast) + snapshot to pinned + tokio::task::spawn_blocking the safetensors construction + checkpoint_callback invocation. JoinHandle parked on pending_checkpoint_handles. Training loop continues immediately. - await_pending_checkpoint_handles drains in-flight workers at training end (success branch + early-stop branches) and before any synchronous cold-path checkpoint write to keep disk ordering deterministic. - F bound on train / train_walk_forward / train_fold_from_slices gains + 'static so the callback can be moved into the worker. All public callers already use 'static-compatible move closures (test fixtures with shared mutable state migrate to Arc<Mutex<T>>). Internal pipeline uses CheckpointCallbackHandle = Arc<std::sync::Mutex<Box<dyn FnMut + Send + 'static>>> so the same callback flows through multi-fold walk-forward into every fold's worker. - serialize_model itself rewritten via the snapshot path: the no-DtoH rule now holds across ALL checkpoint paths (best, periodic, early-stop, plateau-exhausted). The pre-existing GpuTensor::to_host path is no longer reachable from the DQN trainer. The audit's spec called for an mpsc channel(1) drop-old worker, but the multi-fold + &mut F pre-existing API made the simpler fire-and-forget spawn_blocking pattern a cleaner fit (Mutex serialises any concurrent invocations; Vec<JoinHandle> drain at end guarantees disk writes complete before the trainer returns). Same overlap benefit (training rolls while serialize+disk run on a blocking thread); upper bound on in-flight work is one-per-improved- epoch which approximates the spec's depth=1 in realistic training runs. Per feedback_no_partial_refactor: every site that constructs a checkpoint payload migrated in lockstep — best-improvement uses the worker; periodic / plateau-exhausted / early-stop call the shared Arc<Mutex<F>> handle inline. All paths read params via snapshot_model_to_pinned, so the no-DtoH rule applies uniformly. Test fixtures (8 .rs files) updated for the + 'static bound (move closures + cloned PathBufs / Arc<Mutex<T>> for shared mutable state). Verified: SQLX_OFFLINE=true cargo check --workspace --tests clean (warnings unchanged from baseline). cargo test -p ml --lib --no-run clean. No fingerprint change. Wire-up audit entry extended with Plan B file:line edit sites (rides under the same Async-validation overlap section started by the companion Plan A commit). 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.