072ca45686
nsys profile of multi_fold_convergence on L40S identified backtest_state_gather as the #1 GPU consumer at 37.2% (596 ms / 60,588 calls — kernel launch latency dominated compute) and the per-step TLOB cuBLAS gemvx calls as #2 at 25% (242K calls). Both share the same per-step amplification: chunk_len=512 separate gather launches + 512 separate TLOB.forward calls (4 SGEMMs each) per chunk before any Q-values can be computed. This commit replaces the per-step gather + DtoD pattern with a single batched launch, and reuses the same chunked buffer for a single chunk-wide TLOB forward. Per-chunk launch reduction: from 2*chunk_len + chunk_len*7 to 1 + 7 for the gather+TLOB phase (4608 -> 8 with chunk_len=512, a 576x reduction). New kernel `backtest_state_gather_chunk` (experience_kernels.cu): - Writes [chunk_len, N, padded_sd] directly into chunked_states_buf - chunk_len * N threads, 1 thread per output row - Mathematically identical to per-step gather: portfolio_buf and plan_isv_buf are CONSTANT within a chunk (env_step + plan_state_isv update at chunk boundary only). Each thread reads independent feature offsets, no atomics, no reordering. Chunked val TLOB (gpu_backtest_evaluator.rs + metrics.rs): - Val TLOB instance now sized to DQN_BACKTEST_CHUNK_SIZE * n_windows via new GpuBacktestEvaluator::val_tlob_batch_size() helper. - submit_dqn_step_loop_cublas calls tlob.forward(chunked_states, batch) ONCE per chunk on the chunk-wide buffer instead of chunk_len times on states_buf. - Partial last chunks reuse the same buffers (forward(b) accepts any b <= construction_batch). Borrow restructure: - Removed top-of-function `let ch_states = self.chunked_states_buf.as_ref()?` binding (TLOB needs &mut). Replaced with per-chunk ch_states_base raw u64 device pointer extracted in tight scope, reused by Phase 1 (gather) and Phase 2+3 (compute_q_values_to + last_step_states_ptr). The pointer is stable across the chunk because the Option<CudaSlice<f32>> does not reallocate. Per-step gather kernel `backtest_state_gather` retained unchanged for evaluate() / evaluate_ppo() / evaluate_supervised() paths that still need a single-step writer (closure-based callers with no chunked buffer). Audit doc dqn-gpu-hot-path-audit.md updated with Fix 18 entry per Invariant 7. Build: SQLX_OFFLINE=true cargo check -p ml --lib clean (12 warnings, baseline). Tests: cargo test -p ml --lib --no-run clean. 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.