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 ist eine dezentrale Handelsplattform und ein Grid-Trading-Protokoll, das auf den Aptos- und Sui-Blockchains bereitgestellt wird. Es bietet eine Handelsinfrastruktur, die On-Chain-Orderbücher mit Liquiditäts-Hubs für automatisierte Market Maker und eine Cross-Chain-Handels-Engine kombiniert. Das System zeichnet sich durch seine automatisierten Grid-Trading-Funktionen aus, die Kauf- und Verkaufsaufträge in linearen oder geometrischen Preisintervallen ausführen, um Marktvolatilität zu nutzen. Es unterstützt zudem Zero-Spread-Trading durch Post-Only-Order-Routing und ein hybrides Modell, das zentrale Limit-Orderbücher mit automatisierten Market Makern integriert. Das Protokoll umfasst eine umfassende Suite an Tools für das Liquiditätsmanagement, die On-Chain-Vault-Verwahrung für Token-Einlagen nutzen. Es integriert ein Governance-System mit einem Timelock-Mechanismus für administrative Änderungen sowie On-Chain-Analysen zur Überwachung von Protokollmetriken und Trader-Performance. Der programmatische Zugriff auf Handelsoperationen, Order-Historien und Grid-Daten wird über eine Reihe standardisierter API-Endpunkte bereitgestellt.

Dieses Projekt enthält technische Dokumentationen und Referenzleitfäden für den Spot-Handel, einschließlich Spezifikationen für REST-, WebSocket- und FIX-Protokolle. Es dient als umfassende Ressource für die Integration mit Spot-Handels-Endpunkten, um Trades auszuführen, Kontodaten abzufragen und Marktstatistiken abzurufen. Das Projekt zeichnet sich dadurch aus, dass es Konnektivität auf institutionellem Niveau durch den Financial Information eXchange Standard und Simple Binary Encoding unterstützt, um Latenz und Payload-Größe zu reduzieren. Es enthält zudem eine dedizierte Sandbox-Umgebung zur Validierung von Handelslogik und Strategien ohne finanzielles Risiko. Die Dokumentation deckt eine breite Palette an Funktionen ab, einschließlich Echtzeit-Marktdaten-Streaming, umfassendem Order- und Trade-Lifecycle-Management sowie Kontomonitoring. Sie beschreibt zudem komplexe Ordertypen, Smart-Order-Routing und strikte Handelsregeln bezüglich Preis- und Mengenvalidierung. Das Repository enthält detaillierte API-Referenzen, einen Leitfaden zur FIX-Protokoll-Integration und eine WebSocket-Marktdatenspezifikation, um Entwickler während der Implementierung zu orientieren.

tqsdk-python ist ein quantitatives Trading-SDK und Framework, das für die Entwicklung automatisierter Strategien für Futures, Optionen und Aktien unter Verwendung von Python konzipiert ist. Es fungiert als algorithmische Trading-Engine und Finanzmarktdaten-API und bietet die notwendigen Tools, um Strategien zu backtesten, historische Daten zu analysieren und Live-Trades über mehrere Broker-Konten hinweg auszuführen. Das Projekt zeichnet sich durch eine spezialisierte Options-Analytics-Bibliothek aus, die Griechen, implizite Volatilität und Volatilitätsoberflächen unter Verwendung des Black-Scholes-Modells berechnet. Es unterstützt zudem komplexe Order-Ausführungsmuster wie TWAP, Iceberg und POV, um den Markteinfluss während des Einstiegs und Ausstiegs aus Positionen zu minimieren. Das SDK deckt ein breites Funktionsspektrum ab, einschließlich Echtzeit- und historischer Marktdatenabfrage, quantitativem Risikomanagement und Portfolio-Monitoring. Es integriert ein asynchrones Ausführungsmodell für Daten-Streaming und Task-Scheduling, neben Tools für Multi-Asset-Trading-Simulation und Performance-Analyse. Die Bibliothek bietet eine webbasierte grafische Oberfläche für Strategie-Monitoring und Datenvisualisierung.

pyalgotrade ist eine Python-Bibliothek für algorithmischen Handel, die für die Entwicklung, das Backtesting und die Ausführung automatisierter Handelsstrategien konzipiert ist. Sie bietet ein umfassendes Framework für das Backtesting von Finanzstrategien, eine Bibliothek für technische Analyse zur Berechnung mathematischer Indikatoren sowie Konnektoren für die Integration von Kryptobörsen. Das Projekt zeichnet sich durch die Unterstützung von sentimentbasiertem Handel durch die Integration von Social-Media-Feeds und Keyword-Streams in Echtzeit aus. Es bietet ein Visualisierungstool für quantitativen Handel zum Plotten von Preisverläufen und Portfolio-Equity-Kurven sowie parallele Parameteroptimierung zur Identifizierung der effektivsten Strategieeinstellungen. Die Bibliothek deckt ein breites Spektrum an Funktionen ab, darunter den Aufbau von Finanzdaten-Pipelines für die Aufnahme und Resampling von Marktdaten, das Order-Lifecycle-Management für Live- und Paper-Trading sowie quantitative Performance-Analysen zur Berechnung risikoadjustierter Renditen und Drawdowns. Sie enthält zudem Tools für technische Analysen, wie Volatilitätsbänder und Momentum-Indikatoren, sowie Simulationen, die Order-Slippage und Transaktionsgebühren berücksichtigen.

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