60fd7de96d
Phase 1 of the HEALTH_DIAG GPU port (Phase 0 inventory landed in
333ea7184). Lands the typed snapshot struct and the mapped-pinned
allocation that subsequent phases write into and read from — no kernel
and no caller wiring yet, deliberately so the type can stabilise before
Phase 2 invests in CUDA shmem reductions against its byte layout.
`HealthDiagSnapshot` (#[repr(C)] POD, 147×4 = 588 bytes) holds every
numeric field emitted across the 7 HEALTH_DIAG log sites. All fields
are `f32` or `u32` so CPU and GPU agree byte-for-byte with no padding
to reason about. Controller fire booleans (`fire_lr`/`fire_tau`/etc.)
are promoted from u8 → u32 per the Phase 0 design review's open-
question #3 — a `[u8; 6]` followed by `f32` would risk implementation-
defined padding mismatch between Rust's #[repr(C)] and CUDA's struct
layout rules; the +24 bytes of cost is acceptable.
`MappedHealthDiagSnapshot` wraps `cuMemHostAlloc(DEVICEMAP|PORTABLE)`
of `sizeof(HealthDiagSnapshot)` plus `cuMemHostGetDevicePointer_v2` —
mirrors the existing `MappedF32Buffer` allocator pattern but typed.
The kernel chain in Phase 2 will write through `dev_ptr()`; the host
emit code in Phase 4 reads through `host_ref()`. No `memcpy_dtoh`,
no `Vec` allocator on the path — consistent with
`feedback_no_htod_htoh_only_mapped_pinned.md`.
Three unit tests pin the layout (`snapshot_size_is_stable`,
`alignment_is_4_bytes`, `default_is_zeroed`) so any future field
add/reorder requires an explicit test update — guards the kernel-side
offset table from silent drift between commits. The size assertion
records the field count breakdown line by line in a comment so the
next maintainer can audit the math without re-deriving it.
Touched: cuda_pipeline/health_diag.rs (+339 LOC new),
cuda_pipeline/mod.rs (+8 re-exports). cargo check clean at 12 warnings
(workspace baseline). Three new unit tests pass on local CPU host (no
GPU required). Audit doc note added per Invariant 7 — explicitly notes
producer kernel + caller migrations land in Phase 2 / 4 commits.
No fingerprint change — separate mapped-pinned alloc, not part of the
param-tensor layout the fingerprint guards.
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.