cc4c47f471
Audit script (audit-rust-consts.sh) scans Rust src/examples for numeric
literals mirroring structural kernel-side consts (N_ACTIONS, Q_N_ATOMS,
HIDDEN_DIM, MAX_UNITS, BOOK_LEVELS). Closes the layer-3 gap noted in
feedback_use_consts_not_literals_for_structural_dims:
Layer 1: kernel `#define` allowlist → audit-isv
Layer 2: Rust `pub const` canonical → exists (e.g. N_ACTIONS in rl/common.rs)
Layer 3: Rust literals mirroring (2) → audit-rust-consts (this commit)
Honors `// audit-ignore: <SYMBOL>` per-line markers and skips `[u8; N]`
byte-buffer patterns (high false-positive class — almost always I/O
scratch, not structural dims).
Cleanup driven by first run (19 real flags, no grandfathering):
* New canonical: `BOOK_LEVELS` in `ml-alpha/src/cfc/snap_features.rs`
(10 book levels = same place as `Mbp10RawInput` struct)
* `ml-backtesting/src/lob/mod.rs`: redefine as `pub use` re-export from
ml-alpha (single source of truth; ml-backtesting depends on ml-alpha
via `Mbp10RawInput` already)
* 19 sites switched literal `10` → `BOOK_LEVELS`:
- snap_features.rs:44-47 (struct fields)
- data/loader.rs:872-876, 960 (Mbp10Snapshot → Mbp10RawInput convert)
- data/aggregation.rs:161 (level-wise aggregation loop)
- trainer/perception.rs:2750-2756, 6272-6278, 6686-6690, 7247-7253
(snapshot → batch staging loops)
- tests/lob_sim_fuzz.rs:21, lob_sim_integrated_fuzz.rs:22 (duplicate
const → use ml_backtesting::lob::BOOK_LEVELS)
* 5 sites marked `// audit-ignore: BOOK_LEVELS — <reason>`:
- harness.rs:572,574,594 (conviction-bucket histograms, 10 ≠ depth)
- multi_horizon_labels.rs:489,557,564 (10-element test price vecs)
Re-run after fixes: 0 suspect literals flagged. PASS.
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.