Nautilus Trader is a production-grade, Rust-native trading engine designed for multi-asset, multi-venue trading systems. It features a deterministic event-driven architecture that enables research-to-live parity, allowing strategies to be developed, backtested, and deployed live with consistent behavior. The system comprises a core Rust engine with Python bindings for strategy development, modular adapters for over 20 exchanges (including Binance, Coinbase, Kraken, and DeFi platforms), and infrastructure for backtesting, live trading, risk management, and analysis. Key components include a CLI tool for system management, a LiveNode for production trading, and a BacktestNode for historical strategy evaluation. The architecture emphasizes modularity, with separate crates for model, core, execution, trading, risk, analysis, persistence, serialization, and exchange-specific adapters, all communicating via an actor-based message bus.
TqSdk (天勤量化) is a comprehensive Python SDK for quantitative trading in Chinese futures, options, and stock markets. It provides real-time market data via WebSocket, multi-broker account management (CTP, simulation, backtest), order execution, position management, and a full backtesting engine with replay capabilities. The architecture follows a client-server diff protocol: the SDK maintains an in-memory snapshot of business data (quotes, orders, positions) and processes incremental updates delivered via an async event loop. Users write strategies as Python scripts using `TqApi`, `wait_update()`, and helper classes like `TargetPosTask`. The SDK includes a rich set of demo strategies, technical indicators (60+), options pricing (Black-Scholes Greeks), risk management rules, scenario trials, and a web GUI for visualization. It also provides AI skill packs for integration with coding assistants. While primarily an application library, it contains strong data engineering components for time-series handling and backtesting.
This repository is a Chinese futures algorithmic trading system centered on the CTP (Commodity Trading Platform) native protocol. It comprises a C++ pybind11 bridge for low-latency market data and order execution, Python trading strategies (e.g., channel breakout, experimental transformer-based models), and a Django web dashboard for monitoring positions, orders, signals, performance, and correlations. The architecture is hybrid: the native module handles real-time CTP communication, while Python strategies run on an in-process event bus with Redis-like pub/sub. The dashboard visualizes trading metrics with ECharts and provides admin CRUD via Django admin. The system is production-oriented but exhibits several maintenance concerns: hardcoded secrets, outdated frontend libraries (Bootstrap 3, jQuery 3.1), duplicate config loading, and an incomplete ML integration. Despite these, the core CTP bridge and strategy framework are well-constructed and demonstrate solid engineering for the niche domain of Chinese futures trading.