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.
This repository contains the backend API for a trading platform, specifically QuantDinger. It handles strategies, indicators, and market data, and has various tests and scripts for ensuring the quality and functionality of the code.
OsEngine is a comprehensive, multi-exchange algorithmic trading platform for desktop (WPF/WinForms hybrid). It provides data downloading (OsData), backtesting (OsTester), walk-forward optimization (OsOptimizer), and live trading (OsTrader) with support for 30+ exchange connectors (MOEX, Binance, Bybit, OKX, Interactive Brokers, etc.). The platform includes a built-in technical indicator library (100+ indicators), a trading robot framework with attribute-based registration, and an MCP (Model Context Protocol) API for external AI/automation control. The architecture is modular: separate executables for different modes, a common server abstraction layer, and a consistent parameter system for bots and indicators. While the codebase is large and well-organized, it suffers from significant duplication across exchange connector entity classes and some copy-paste logic in trading robots. The UI mixes WPF and Windows Forms, and some patterns are legacy. Overall, it is a production-grade algorithmic trading platform with strong engineering practices and a rich feature set.
pyalgotrading is a Python library that provides an interface to the AlgoBulls algorithmic trading platform. It enables users to create, upload, and execute trading strategies for backtesting, paper trading, and live trading. The library includes abstractions for strategies, orders, and technical indicators, along with an API client for communicating with the AlgoBulls platform. Users define strategy classes that inherit from StrategyBase, implement required methods, and then use the AlgoBullsConnection class to deploy and manage their strategies on the platform. The library supports multiple order types, technical indicators via TA-Lib, and various candlestick chart formats.