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 est une plateforme d'échange décentralisée et un protocole de trading par grille déployé sur les blockchains Aptos et Sui. Il fournit une infrastructure de trading qui combine des carnets d'ordres on-chain avec des hubs de liquidité de teneurs de marché automatisés (AMM) et un moteur de trading cross-chain. Le système se distingue par ses capacités de trading par grille automatisé, qui exécutent des ordres d'achat et de vente à des intervalles de prix linéaires ou géométriques pour capturer la volatilité du marché. Il prend également en charge le trading sans spread via le routage d'ordres post-only et un modèle hybride qui intègre des carnets d'ordres à limite centrale avec des teneurs de marché automatisés. Le protocole inclut une suite complète d'outils pour la gestion de la liquidité, utilisant la garde de coffres on-chain pour les dépôts de jetons. Il incorpore un système de gouvernance avec un mécanisme de timelock pour les changements administratifs, ainsi que des analyses on-chain pour surveiller les métriques du protocole et la performance des traders. L'accès programmatique aux opérations de trading, aux historiques d'ordres et aux données de grille est fourni via un ensemble de points de terminaison API standardisés.

Ce projet fournit une documentation technique et des guides de référence pour le trading au comptant (spot trading), incluant des spécifications pour les protocoles REST, WebSocket et FIX. Il sert de ressource complète pour l'intégration avec les endpoints de trading au comptant afin d'exécuter des transactions, d'interroger les données de compte et de récupérer les statistiques de marché. Le projet se distingue par la prise en charge d'une connectivité de qualité institutionnelle via le standard Financial Information eXchange (FIX) et un encodage binaire simple pour réduire la latence et la taille de la charge utile. Il inclut également un environnement sandbox dédié pour valider la logique et les stratégies de trading sans risque financier. La documentation couvre un large éventail de capacités, notamment le streaming de données de marché en temps réel, la gestion complète du cycle de vie des ordres et des transactions, et la surveillance des comptes. Elle détaille également les types d'ordres complexes, le routage intelligent des ordres et les règles de trading strictes concernant la validation des prix et des quantités. Le dépôt contient des références API détaillées, un guide d'intégration du protocole FIX et une spécification de données de marché WebSocket pour orienter les développeurs lors de l'implémentation.

tqsdk-python est un SDK et framework de trading quantitatif conçu pour développer des stratégies automatisées pour les contrats à terme, les options et les actions en utilisant Python. Il fonctionne comme un moteur de trading algorithmique et une API de données de marché financier, fournissant les outils nécessaires pour backtester des stratégies, analyser des données historiques et exécuter des trades en direct à travers plusieurs comptes de courtage. Le projet se distingue par une bibliothèque d'analyse d'options spécialisée qui calcule les Grecs, la volatilité implicite et les surfaces de volatilité en utilisant le modèle Black-Scholes. Il prend en outre en charge des modèles d'exécution d'ordres complexes, tels que TWAP, Iceberg et POV, pour minimiser l'impact sur le marché lors de l'entrée et de la sortie de position. Le SDK couvre une large surface de capacités, y compris la récupération de données de marché en temps réel et historiques, la gestion des risques quantitatifs et le suivi de portefeuille. Il intègre un modèle d'exécution asynchrone pour le streaming de données et la planification de tâches, parallèlement à des outils pour la simulation de trading multi-actifs et l'analyse de performance. La bibliothèque fournit une interface graphique basée sur le web pour le suivi de stratégie et la visualisation de données.

pyalgotrade est une bibliothèque Python de trading algorithmique conçue pour développer, backtester et exécuter des stratégies de trading automatisées. Elle fournit un framework complet pour le backtesting de stratégies financières, une bibliothèque d'analyse technique pour calculer des indicateurs mathématiques, et des connecteurs pour l'intégration d'échanges de cryptomonnaies. Le projet se distingue par sa prise en charge du trading basé sur le sentiment grâce à l'intégration de flux de réseaux sociaux en temps réel et de flux de mots-clés. Il propose un outil de visualisation de trading quantitatif pour tracer l'évolution des prix et les courbes d'équité du portefeuille, ainsi qu'une optimisation parallèle des paramètres pour identifier les réglages de stratégie les plus efficaces. La bibliothèque couvre un large éventail de capacités, incluant la construction de pipelines de données financières pour l'ingestion et le rééchantillonnage des données de marché, la gestion du cycle de vie des ordres pour le trading réel et simulé (paper trading), et l'analyse de performance quantitative pour calculer les rendements ajustés au risque et les drawdowns. Elle inclut également des outils d'analyse technique, tels que les bandes de volatilité et les indicateurs de momentum, ainsi que des simulations prenant en compte le slippage des ordres et les commissions de transaction.

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