Plattform für algorithmischen Handel und Backtesting

aiquanttrade is an AI-driven quantitative trading platform that enables the development, backtesting, and deployment of trading strategies powered by machine learning and artificial intelligence. It provides a complete local environment for quantitative research, simulation, and automated live trading through brokerage APIs, supporting both historical backtesting and real-time paper trading without capital risk. The platform distinguishes itself through a modular, event-driven architecture that separates strategy logic from execution, allowing rule-based and machine learning models to be co

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

Backtrader is a Python backtesting framework and algorithmic trading platform. It provides a toolkit for developing automated trading rules and simulating investment strategies using historical financial time-series data. The system functions as a quantitative analysis tool, combining a simulation engine for testing trading rules with a financial data visualizer that generates price action charts. It allows for the calculation of technical indicators and the evaluation of portfolio performance through risk-adjusted returns. The platform covers live trading integration via brokerage APIs and

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

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

This project is an algorithmic trading engine designed for the automated execution of cryptocurrency strategies. It provides a modular execution core that connects to multiple centralized and decentralized exchanges, allowing users to deploy rule-based trading logic across various spot and futures markets. The platform serves as a comprehensive environment for the entire trading lifecycle, from initial strategy development to live market operations. What distinguishes this platform is its integrated suite for quantitative analysis and predictive modeling. It features a robust backtesting engi

VeighNa is an event-driven, modular platform designed for the development, backtesting, and execution of automated financial trading strategies. It provides a comprehensive suite of tools that includes a centralized trading terminal for monitoring portfolios and market conditions, alongside a robust algorithmic trading engine that manages real-time data processing and order execution. The platform distinguishes itself through a highly decoupled architecture that isolates algorithmic logic from market connectivity, allowing for independent strategy development and testing. It utilizes a dynami

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

Jesse is a Python algorithmic trading framework used for developing, backtesting, and executing quantitative trading strategies. It functions as a trading strategy backtester and a machine learning trading platform, providing an environment to train predictive models on historical market data and deploy them into live strategies. The framework features a standardized crypto exchange connectivity layer that allows for the execution of automated spot and futures trades across multiple cryptocurrency exchanges via an exchange-agnostic interface. It includes a quantitative risk analysis toolset t

Stock is an algorithmic trading framework designed for the development, backtesting, and execution of automated investment strategies. It provides a comprehensive environment for quantitative market analysis, enabling users to build systems that connect to brokerage interfaces for order placement based on predefined technical rules. The platform distinguishes itself through integrated data acquisition and analysis capabilities, including a financial data collection engine that utilizes proxy rotation and session persistence to maintain stable connectivity and bypass rate limits. It supports h

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

Quantaxis is a quantitative trading framework designed for building, backtesting, and executing automated strategies across global equities, futures, and cryptocurrencies. It integrates an event-driven backtesting engine, a multi-market execution gateway for order routing, and a quantitative data pipeline for ingesting and storing multi-asset market data. The system features a Rust-accelerated financial library that utilizes Apache Arrow for high-performance technical indicator calculation and zero-copy data processing. It provides a containerized infrastructure model designed for orchestrati

FinRL is a reinforcement learning framework designed for the development, training, and backtesting of automated trading strategies. It functions as a quantitative finance toolkit that integrates deep learning algorithms with financial market simulations to address complex portfolio management and asset allocation tasks. The platform provides an end-to-end pipeline for transforming raw market data into actionable trading models. The project distinguishes itself through a layered, modular architecture that separates data processing, environment simulation, and agent training. This design allow

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

QuantMuse is an algorithmic trading platform and quantitative trading framework that integrates large language models with mathematical analysis to automate market insights and trading strategies. It functions as a system for building, backtesting, and executing strategies using both historical and real-time market data. The framework is distinguished by its use of large language models for financial analysis and sentiment extraction from news and social media. It utilizes autonomous agents with chain-of-thought reasoning to generate market intelligence and strategic reports, while employing

This project is an algorithmic trading platform designed to automate financial market analysis and the execution of investment strategies. It provides an end-to-end environment for processing real-time market data through automated decision models, allowing for the triggering of financial transactions based on predefined quantitative signals and risk parameters without manual intervention. The platform distinguishes itself through a modular pipeline architecture that decouples data ingestion, signal generation, and trade execution, facilitating the iterative refinement of investment models. I

Abu is an algorithmic trading framework designed for the development, backtesting, and optimization of automated trading strategies. It functions as a quantitative financial analysis library that processes time-series data to identify market trends, volatility patterns, and key price levels. The platform distinguishes itself through a modular architecture that integrates diverse financial data sources and a rule-based engine for automated risk management. It enables users to construct complex trading signals by layering technical indicators and machine learning models, while simultaneously en

TradeMaster is a reinforcement learning trading framework and algorithmic trading simulator designed for designing and testing quantitative trading strategies. The system provides a platform for developing reinforcement learning agents, managing quantitative portfolios, and optimizing trade execution using financial market data. The project features specialized components for multi-modality data preprocessing, a high-fidelity market environment simulation for strategy backtesting, and a quantitative portfolio manager for capital reallocation across multiple assets. It includes a trade executi

FinRL is a financial reinforcement learning framework and quantitative trading library. It provides a specialized system for developing, training, and simulating autonomous agents designed to automate financial trading and portfolio management. The project serves as an automated portfolio optimizer and financial market simulator. It enables the creation of decision-making policies to balance asset allocations, maximize potential returns, and minimize financial risk through reinforcement learning. The framework includes capabilities for financial market data engineering, algorithmic trading s

This project is a comprehensive platform for quantitative investment research, machine learning, and algorithmic trading. It provides an end-to-end environment for developing, testing, and executing financial strategies, supporting the entire lifecycle from data ingestion and feature engineering to model training and backtesting. The system is distinguished by its configuration-driven workflow orchestration, which allows researchers to automate complex pipelines and manage experiments through declarative files. It features a high-performance data infrastructure that utilizes custom binary for

Vibe-Trading is a system for automated financial trading and algorithmic market research. It uses autonomous agents to manage financial assets and execute trades based on predefined rules and logic. The project features a multi-agent collaborative workflow that coordinates specialized agents to perform joint research and risk reviews. It utilizes large language model orchestration to map natural language prompts to executable data loaders and backtesting functions. The platform includes capabilities for quantitative strategy backtesting and alpha benchmarking using information coefficients t

Gekko is a Node.js trading platform and automated Bitcoin trading bot designed to execute buy and sell orders across multiple cryptocurrency exchanges. It functions as an algorithmic trading system that uses a standardized exchange integration gateway to connect with various external trading platforms. The system includes a backtesting engine that simulates trading strategies against historical market data to evaluate performance before live deployment. It employs an adapter-based integration model to normalize diverse exchange API responses into a consistent internal format. The platform pr

FinRL-Library is a reinforcement learning trading framework and algorithmic trading library used to develop and backtest automated financial trading strategies. It functions as a quantitative trading pipeline and financial market simulator, allowing users to build decision policies that optimize asset trading across various financial markets. The framework features a modular integration system for swapping reinforcement learning algorithms through a consistent API. It utilizes a standardized environment wrapper to encapsulate market dynamics into a state-action-reward interface, facilitating

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

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

QuantStart.com - QSTrader backtesting simulation engine.

Systematic Trading in python