Awesome GitHub RepositoriesOrder Execution Engines

Backtrader is a Python framework designed for the development, backtesting, and live execution of algorithmic trading strategies. It provides a comprehensive environment for quantitative finance, allowing users to simulate trading logic against historical market data or connect directly to brokerage platforms for automated real-time trading. The project distinguishes itself through a unified event-driven architecture that treats backtesting and live trading with the same API. This consistency is supported by a flexible data-feed abstraction layer that normalizes diverse financial sources, ena

Nautilus Trader is a high-performance algorithmic trading framework built in Rust, designed for the development, backtesting, and live execution of automated trading strategies. It provides a comprehensive platform for managing multi-asset portfolios and interacting with diverse financial markets through a standardized connectivity suite. The system is engineered to handle high-frequency data processing and complex order execution while maintaining precise numerical accuracy across various asset classes. The framework distinguishes itself through an architecture centered on deterministic even

Hummingbot is an open-source framework designed for building, backtesting, and deploying autonomous trading agents and algorithmic strategies across centralized and decentralized cryptocurrency exchanges. It provides a modular environment where users can orchestrate containerized bots to execute complex market-making, grid trading, and arbitrage operations. The platform distinguishes itself through a skill-based architecture that integrates large language models, enabling users to monitor market conditions and control trading operations via natural language commands. It features a unified con

Lean is an algorithmic trading engine and quantitative finance platform designed for the development, backtesting, and live execution of automated trading strategies. It provides a comprehensive framework for processing time-series market data, managing multi-asset portfolios, and conducting quantitative research across diverse financial markets. The platform distinguishes itself through a modular, event-driven architecture that decouples strategy logic from data ingestion and brokerage connectivity. By utilizing standardized interfaces for data providers and brokerage abstractions, it enable

Easytrader is a quantitative trading automation framework and brokerage API wrapper designed to programmatically execute buy and sell orders across trading terminals. It functions as a system for linking quantitative strategy logic to brokerage clients, providing the necessary infrastructure to automate stock trading and execute strategy-driven signals. The system distinguishes itself by offering a remote trading execution server that decouples strategy logic from trade execution, allowing orders to be triggered on distant machines via a web server or command-line interface. It includes speci

This project is a futures algorithmic trading system designed to execute high-performance trading strategies through direct API integrations and low-latency message routing. It features a strategy execution engine that automates order placement and manages trade flows based on predefined logic and API triggers. The system utilizes a native trading API bridge and a low-latency message bus to interface internal logic with external exchange APIs while minimizing execution delays. Monitoring is handled through a web-based trading dashboard for real-time activity tracking and remote management. B

Zenbot is an automated cryptocurrency trading bot designed to execute trades on exchanges based on technical analysis and predefined risk parameters. It functions as a technical analysis engine that processes market data through mathematical indicators to generate actionable trade signals. The system includes a genetic algorithm strategy optimizer to automatically discover the most profitable parameter configurations. It provides multiple simulation environments, including a trading strategy backtester for replaying historical data and a paper trading simulator for testing strategies against

python-binance is a Python client library that provides programmatic access to the Binance cryptocurrency exchange through both REST and WebSocket APIs. It serves as a comprehensive toolkit for automated trading, account management, and market data retrieval, enabling developers to build trading bots, portfolio management tools, and data analysis applications that interact directly with the exchange. The library distinguishes itself through a dual-client architecture that separates synchronous REST calls from persistent WebSocket streams, allowing concurrent execution without blocking. It inc

RQAlpha is a Python-native quantitative trading backtesting framework and live trading execution system. It provides an event-driven engine for simulating trading strategies against historical market data, with realistic transaction costs, slippage models, and corporate action handling. The platform supports multi-asset class trading including stocks, futures, options, and REITs, with separate sub-accounts for different asset types and configurable margin requirements. The framework distinguishes itself through a plugin-based extensible architecture that allows users to swap out core componen

OctoBot is an open-source automated trading platform that connects to over 15 cryptocurrency exchanges, enabling users to deploy grid, dollar-cost averaging, market-making, and AI-driven trading strategies. It functions as a unified multi-exchange trading platform, a TradingView alert executor, and a crypto trading bot, all within a single system. The platform is built on an event-driven trading loop with a plugin-based strategy engine, an exchange-agnostic connector layer, and a cloud-synced profile store for multi-device consistency. What distinguishes OctoBot is its integration of large la

This project is an automated cryptocurrency trading platform for the Binance exchange. It functions as a technical analysis trading tool and grid trader, executing strategies and managing assets without manual intervention. The platform is distinguished by its multi-service containerized architecture, which orchestrates a listener, cache, and database. It utilizes a secure web dashboard for monitoring active trades and adjusting bot parameters, protected by password and token-based authentication. The system covers a broad range of trading capabilities, including grid and trailing order auto

Sea Protocol es una plataforma de intercambio descentralizado y protocolo de trading de rejilla (grid trading) desplegado en las blockchains Aptos y Sui. Proporciona una infraestructura de trading que combina libros de órdenes on-chain con hubs de liquidez de creadores de mercado automatizados (AMM) y un motor de trading cross-chain. El sistema se distingue por sus capacidades de grid trading automatizado, que ejecutan órdenes de compra y venta a intervalos de precio lineales o geométricos para capturar la volatilidad del mercado. También soporta trading sin spread mediante enrutamiento de órdenes post-only y un modelo híbrido que integra libros de órdenes de límite central con creadores de mercado automatizados. El protocolo incluye un conjunto completo de herramientas para la gestión de liquidez, utilizando custodia de bóveda on-chain para depósitos de tokens. Incorpora un sistema de gobernanza con un mecanismo de timelock para cambios administrativos, así como analíticas on-chain para monitorizar métricas del protocolo y el rendimiento de los traders. El acceso programático a operaciones de trading, historiales de órdenes y datos de rejilla se proporciona a través de un conjunto de endpoints de API estandarizados.

Este proyecto proporciona documentación técnica y guías de referencia para el trading al contado (spot trading), incluyendo especificaciones para protocolos REST, WebSocket y FIX. Sirve como un recurso integral para integrarse con endpoints de trading al contado para ejecutar operaciones, consultar datos de cuenta y obtener estadísticas de mercado. El proyecto se distingue por admitir conectividad de grado institucional a través del estándar Financial Information eXchange (FIX) y codificación binaria simple para reducir la latencia y el tamaño de la carga útil. También incluye un entorno de sandbox dedicado para validar la lógica y las estrategias de trading sin riesgo financiero. La documentación cubre una amplia gama de capacidades, incluyendo streaming de datos de mercado en tiempo real, gestión integral del ciclo de vida de órdenes y operaciones, y monitoreo de cuentas. También detalla tipos de órdenes complejos, enrutamiento inteligente de órdenes y reglas de trading estrictas con respecto a la validación de precio y cantidad. El repositorio contiene referencias detalladas de API, una guía de integración del protocolo FIX y una especificación de datos de mercado WebSocket para orientar a los desarrolladores durante la implementación.

tqsdk-python es un SDK y framework de trading cuantitativo diseñado para desarrollar estrategias automatizadas para futuros, opciones y acciones utilizando Python. Funciona como un motor de trading algorítmico y API de datos de mercado financiero, proporcionando las herramientas necesarias para realizar backtesting de estrategias, analizar datos históricos y ejecutar operaciones en vivo a través de múltiples cuentas de corretaje. El proyecto se distingue por una librería de análisis de opciones especializada que calcula griegas, volatilidad implícita y superficies de volatilidad utilizando el modelo Black-Scholes. Además, admite patrones complejos de ejecución de órdenes, como TWAP, Iceberg y POV, para minimizar el impacto en el mercado durante la entrada y salida de posiciones. El SDK cubre una amplia superficie de capacidades, incluyendo la recuperación de datos de mercado en tiempo real e históricos, gestión de riesgos cuantitativa y monitoreo de carteras. Incorpora un modelo de ejecución asíncrono para la transmisión de datos y la programación de tareas, junto con herramientas para la simulación de trading multiactivo y análisis de rendimiento. La librería proporciona una interfaz gráfica basada en web para el monitoreo de estrategias y la visualización de datos.

pyalgotrade es una librería de trading algorítmico en Python diseñada para desarrollar, realizar backtesting y ejecutar estrategias de trading automatizadas. Proporciona un framework integral para backtesting de estrategias financieras, una librería de análisis técnico para calcular indicadores matemáticos y conectores para la integración con exchanges de criptomonedas. El proyecto se distingue por soportar trading basado en sentimiento mediante la integración de feeds de redes sociales en tiempo real y flujos de palabras clave. Incluye una herramienta de visualización de trading cuantitativo para graficar la acción del precio y curvas de capital de la cartera, junto con optimización de parámetros en paralelo para identificar la configuración de estrategia más efectiva. La librería cubre un amplio rango de capacidades, incluyendo la construcción de pipelines de datos financieros para la ingesta y remuestreo de datos de mercado, gestión del ciclo de vida de órdenes tanto para trading real como en papel (paper trading), y análisis de rendimiento cuantitativo para calcular retornos ajustados al riesgo y drawdowns. También incluye herramientas para análisis técnico, como bandas de volatilidad e indicadores de momentum, así como simulaciones que consideran el deslizamiento de órdenes (slippage) y comisiones de transacción.

This project is a high-performance C++ trading engine designed for automated cryptocurrency market making and high-frequency trading. It functions as a liquidity provision tool that executes rapid order adjustments and quote placements to capture the bid-ask spread. The system utilizes a fair-value pricing model to estimate target asset prices based on real-time exchange data. It features a self-hosted trading dashboard that provides a web interface for monitoring portfolio holdings, visualizing market metrics, and manually controlling automated trading instances. The software includes capab

pybroker is a Python algorithmic trading framework and quantitative technical analysis library designed for developing, testing, and optimizing trading strategies using historical market data. It functions as a trading strategy backtester and a financial performance evaluator, providing a structured environment to simulate trading rules and analyze their statistical reliability. The framework distinguishes itself through a market data integration layer that handles the fetching and caching of historical price data from external providers. It incorporates an event-driven backtesting engine and

quant-wiki is a comprehensive knowledge base and structured reference for quantitative finance, financial engineering, and algorithmic trading. It serves as a centralized library of documentation covering mathematical models, financial instruments, and systematic trading strategies. The project integrates AI-driven capabilities through a modular retrieval-augmented generation framework that extracts structured data from research papers and news. It features a multi-agent workflow engine designed to discover and validate predictive alpha factors, alongside tools for local large language model

Hikyuu is a quantitative trading framework designed for developing, backtesting, and executing systematic trading strategies. It functions as a high-speed system that combines a financial time-series library, a multi-factor analysis tool, and a quantitative backtesting engine to support comprehensive trading research. The framework is distinguished by its high-speed computing core, which utilizes multi-threaded execution to process large volumes of market data for technical indicator generation. It supports a modular strategy composition model where signal, risk, and fund management component