jgrusewski 206ebd3558 fix(sp4): Task A1 review fix-ups — fingerprint seed + branch count + doc refresh
3 important + 1 optional findings from code quality review:

1. layout_fingerprint_seed() now lists all 40 SP4 slots and bumps
   `ISV_TOTAL_DIM=131` -> `ISV_TOTAL_DIM=171`. Without this, the fail-fast
   checkpoint-load guard would not detect the SP4 layout extension —
   a binary built against new code would falsely compare-equal to old
   checkpoints' fingerprints.

2. Added `SP4_BRANCH_COUNT=4` constant and `debug_assert!(branch <
   SP4_BRANCH_COUNT)` in `atom_pos_bound`. Test extended to verify
   atom_pos_bound max does not alias into WEIGHT_BOUND family.

3. Refreshed stale content in docs/isv-slots.md: ISV_TOTAL_DIM 96->171,
   fingerprint location [37..39)/[47..49) -> [115..117), table entries
   [94]/[95] -> [115]/[116].

4. Added `debug_assert!(group < SP4_PARAM_GROUP_COUNT)` to weight_bound,
   adam_m_bound, adam_v_bound, wd_rate accessors (symmetric with the
   atom_pos_bound change).

cargo check clean, cargo test slot_layout_is_contiguous_and_total_40 passes.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-30 22:02:41 +02:00

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.

Description
No description provided
Readme 849 MiB
Languages
Rust 88.2%
Cuda 7.7%
Python 1.3%
Shell 1.1%
PLpgSQL 0.8%
Other 0.8%