Awesome GitHub RepositoriesTrading and Backtesting

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

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

This library provides a unified interface for interacting with hundreds of global cryptocurrency exchanges. It serves as a standardized framework for building automated trading systems, allowing developers to fetch real-time market data, manage account balances, and execute orders across multiple financial platforms through a single, predictable set of methods. The project distinguishes itself by abstracting the complexities of diverse exchange-specific application programming interfaces into a consistent internal schema. It includes a modular authentication layer that automatically handles c

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

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

Zipline is a Python-based algorithmic trading library designed for the development and backtesting of investment strategies. It functions as a quantitative finance engine that processes historical market data to simulate trading interactions and evaluate strategy performance through custom metrics. The platform provides a modular, event-driven framework that manages portfolio state transitions based on time-series data streams. Beyond its core trading capabilities, the system includes a comprehensive financial data analysis toolkit for manipulating large-scale market datasets to support syste

This project is a comprehensive framework for engineering financial data pipelines, designed to automate the collection, cleaning, and synchronization of large-scale market datasets. It functions as a quantitative trading data engine, providing the infrastructure necessary to manage historical and real-time asset pricing information for research and machine learning workflows. The system distinguishes itself through a configuration-driven approach to orchestration, allowing users to manage complex data acquisition tasks across multiple financial providers. It features resilient middleware tha

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

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

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

StockSharp is an algorithmic trading platform and quantitative framework used for developing and deploying trading robots across stock, forex, and cryptocurrency markets. It functions as a multi-asset trading gateway and a dedicated development environment for building, debugging, and scheduling automated strategies. The platform includes a visual strategy workflow editor that maps logic blocks to executable code and a simulation engine that replays historical tick data to validate trading logic. It utilizes a plugin-based broker integration system to normalize diverse exchange protocols into

This project is a suite of machine learning and statistical tools designed for stock price prediction, financial time series forecasting, and the execution of algorithmic trading strategies. It provides a collection of deep learning and statistical models used to forecast asset prices and market trends. The system includes a market scenario simulator that uses Monte Carlo sampling to generate potential price paths and estimate financial risk. It further features a portfolio optimization tool for calculating asset distributions to maximize returns based on historical volatility, as well as a m

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

VectorBT is a vectorized trading strategy backtesting framework that simulates thousands of strategy configurations in a single pass over historical price data. It operates as a parameter optimization engine, a portfolio performance analyzer, a technical indicator calculator, and a financial data fetcher, all built around a DataFrame-centric data model that uses NumPy broadcasting for signal alignment and compiled code acceleration for performance. The framework distinguishes itself through its ability to run large-scale parameter sweeps by constructing every combination of strategy parameter

QuantStats is an open-source Python library that calculates risk and return metrics from a portfolio return series and generates comprehensive HTML tear sheets. It computes dozens of financial statistics—including Sharpe ratio, drawdown, and volatility—in a single pass over the input data, using vectorized pandas operations for efficiency. The library distinguishes itself by combining portfolio performance analysis with Monte Carlo simulation, which models thousands of random return paths to estimate the probability of reaching financial targets or hitting loss thresholds. It produces self-co

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

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

pyalgotrade is a Python algorithmic trading library designed for developing, backtesting, and executing automated trading strategies. It provides a comprehensive framework for financial strategy backtesting, a technical analysis library for computing mathematical indicators, and connectors for cryptocurrency exchange integration. The project distinguishes itself by supporting sentiment-based trading through the integration of real-time social media feeds and keyword streams. It features a quantitative trading visualization tool for plotting price action and portfolio equity curves, along with

hftbacktest 是一个高频交易回测框架和 3 级市场数据引擎。它利用限价订单簿模拟器来模拟精确的市场微观结构和流动性，为加密货币交易机器人和做市策略提供仿真环境。 该系统以高保真仿真组件为特色，包括用于预测成交时间的队列位置建模，以及用于模拟网络和交易所处理延迟的延迟感知执行。它从 2 级和 3 级数据重构订单簿状态，并使用原始交易所交易和报价流，而非聚合的 K 线数据。 该框架涵盖了广泛的功能，包括基于 Tick 的事件模拟、限价订单生命周期管理以及市场微观结构分析。它还包含投资组合统计跟踪工具，用于根据持仓状态和定价计算策略指标。