jgrusewski 210798baa8 cleanup: declarative rewrites for deferred-work TODOs in ml crate
- ml/Cargo.toml: describe why ndarray's blas feature stays disabled
  (CI compile pool has no libopenblas-dev; GPU cuBLAS handles the
  hot path) rather than labelling it a TODO.
- dbn_sequence_loader.rs: Wave C regime-detection branch emits zeros
  when only that flag is enabled; the live feed lives under WaveD.
  Reword from "TODO Wave C" to a description of that superseding.
- ensemble/adapters/{liquid,tggn,tlob,xlstm}.rs: checkpoint loading
  currently constructs fresh GpuLinear weights and logs a runtime
  warning so the ignored checkpoint path is visible. No new
  functionality, just reword the repeated TODO.
- ensemble/model_adapter.rs: the neutral-prediction adapter is
  guarded by the ensemble's confidence threshold (0.0 = filtered),
  making it a no-op stub used for end-to-end wiring. Describe that
  contract explicitly.
- hyperopt/adapters/tft.rs: the input_dim=51 line is load-bearing
  (5 static + 10 known + 36 unknown matches the CUDA layout). Drop
  the "should be 42" aside.
- trainers/tft/trainer.rs: initialize_optimizer returns Err until
  GpuAdamW is wired; the `let _ = &self.optimizer;` anchor in the
  training loop keeps the migration target visible.
- trainers/tlob.rs: save_checkpoint / serialize_model both surface
  errors until GpuVarStore safetensors serialisation lands. Mark
  the gap declaratively rather than as a TODO.
- transformers/mod.rs: only AttentionMask is implemented in the
  attention submodule; drop the aspirational re-export list.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-23 08:41:44 +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%