6a869ad366
The B0 audit (commit 62ab8ed85) under-counted insert_batch test
callers as 2 (1 production + 1 in-file unit test). Surfaced during
B1.0 implementation when cargo check --workspace --tests failed
with 5 arity-mismatch errors after B0's signature change.
Root cause: B0 audit's grep filter was `grep -v test` and didn't
enumerate crates/ml/src/trainers/dqn/smoke_tests/ (compiled as
part of the lib's test binary, not behind #[cfg(test)]) nor
crates/ml/tests/.
Sites fixed (zero-init i32 alloc, threaded through):
- crates/ml/src/trainers/dqn/smoke_tests/training_stability.rs:152, 196
- crates/ml/src/trainers/dqn/smoke_tests/performance.rs:142
- crates/ml/src/trainers/dqn/smoke_tests/gpu_residency.rs:75
- crates/ml/tests/gpu_per_integration_test.rs:125
No behavior change — the column carries zero data and no consumer
reads it pre-B1.1. B1.1 lands the producer kernel that fills with
-1/0/1 from the 30-bar price trajectory.
Process correction documented in docs/dqn-wire-up-audit.md
"B0.1 cascade-gap fix-up" subsection: future B-series audits must
run cargo check --workspace --tests before claiming cardinality
completeness.
Build: cargo check --workspace --tests clean.
Tests: cargo test -p ml --lib compiles + passes (GPU tests
#[ignore]-gated).
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.