7540df1ecf
Header-only `__device__` function `sp4_histogram_p99<BLOCK_SIZE>(buf, count)`
returning p99(|buf|) via three-pass single-block algorithm:
Pass 1: block-wide max-reduce → step_max (0.0 short-circuit on degenerate)
Pass 2: linear-spaced [0, step_max] binning into 256 bins via per-warp
tiles (no atomicAdd; lane-collisions cost <0.012% precision)
Pass 3: cumulative-from-top → p99 = bin upper-edge
Returns 0.0 for degenerate step_max=0 (caller skips ISV update). Linear
spacing chosen over log because SP4 producers care about resolution at
the top of the distribution — top bin width = step_max/256 ≈ 0.39%, well
within the 1% quantile precision budget.
Wrapper kernel `sp4_histogram_p99_test_kernel` exposes the device fn for
Rust testing; mapped-pinned scalar output with __threadfence_system() so
host read_volatile sees the result (no memcpy_dtoh). Build.rs
registration mirrors thompson_test_kernel.cu pattern + adds
rerun-if-changed for the .cuh header.
Unit test: 4096 deterministic |N(0,1)| Box-Muller samples, sorted
ground-truth p99 ≈ 2.576 z-score one-tailed, asserts rel_err < 5%
against device output. GPU-gated (#[ignore]). Local L40S run:
true_p99=2.59758, computed_p99=2.59858, rel_err=0.039% — passes.
No producer wired yet — header is library code included only by the
test wrapper; SP4 Tasks A5-A9 add the magnitude-bound producer kernels
that #include "sp4_histogram_p99.cuh" directly. Behaviour unchanged.
cargo check --lib --tests 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.