Projects sharing features with Zipline

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

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

bt - flexible backtesting for Python

Awesome-quant is a curated directory of open-source software libraries and tools designed for quantitative finance, algorithmic trading, and financial data analysis. It serves as a central hub for discovering resources that support the entire lifecycle of financial modeling, from raw data ingestion to complex statistical research. The repository organizes specialized tools into categorized collections, enabling users to identify solutions for high-performance numerical computing, technical indicator calculation, and derivative pricing. It highlights frameworks that facilitate the construction

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

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

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

zvt is a quantitative trading framework designed for building, backtesting, and executing algorithmic trading strategies. It functions as a modular system that integrates a financial data pipeline for market data collection, an algorithmic backtesting engine for strategy evaluation, and an event-driven trading system to automate market executions. The project distinguishes itself through a hybrid approach to signal management, using a dynamic tagging system that combines automated quantitative logic with human intervention. It includes a quantitative analysis dashboard for visualizing researc

finmarketpy is a quantitative trading framework and financial market analysis tool. It provides a Python-based library for simulating trading strategies against historical market data, computing the value of options contracts, and extracting trends from financial datasets. The system includes specialized engines for financial options pricing using numerical calculations and a backtesting library to assess risk and performance before live deployment. It further enables the detection of market seasonality and the execution of event studies to measure asset price behavior around specific time wi

AI-Powered Quantitative Trading Engine — Evolve strategies with genetic algorithms

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

DataHub is a metadata management platform designed to unify technical, operational, and business context across diverse data ecosystems. By utilizing a graph-based metadata model and an event-driven ingestion architecture, it creates a centralized source of truth that maps complex data relationships, lineage, and ownership. This foundational framework enables organizations to maintain a synchronized view of their data landscape, supporting both human-led discovery and automated data operations. The platform distinguishes itself through its focus on grounding artificial intelligence and autono

This project is an AI agent orchestration platform that provides a visual environment for building, testing, and deploying complex automation workflows. It functions as a low-code development interface where users can chain discrete functional blocks into dependency-aware pipelines to integrate artificial intelligence with external data and services. The platform supports the creation of intelligent conversational agents, automated business processes, and multi-service API orchestrations within a unified workspace. The platform distinguishes itself through its event-driven integration engine,

This project is a feature-rich Go client library designed for interacting with Redis. It serves as a comprehensive interface for managing remote data stores, enabling developers to execute standard database commands, handle complex data structures, and perform asynchronous operations within Go applications. The library distinguishes itself through its support for advanced Redis capabilities, including connection pooling, pipelining, and transactional integrity. It provides specialized primitives for managing distributed clusters, including automated topology updates and request routing to sha

Aureuserp is a modular open-source enterprise resource planning system and business suite designed to integrate accounting, inventory, manufacturing, human resources, and project coordination into a single centralized database. It functions as a comprehensive platform for managing business operations, sales orders, and procurement. The system uses a modular architecture that allows for the installation or removal of specific feature modules to tailor application capabilities to business needs. It includes a localization layer to translate the user interface into multiple languages and employs

Ever Gauzy is an integrated business management suite providing an ERP and CRM framework for professional services automation. It functions as a multi-tenant SaaS platform that combines time tracking, billing, and human resource management into a unified system. The project is distinguished by its headless architecture, utilizing a REST and GraphQL API gateway to expose business operations. It features a Model Context Protocol server that allows AI assistants to interact with system data and execute functional tools for automated business workflows. The platform covers a broad operational su

This project provides a framework for managing multi-agent systems, designed to automate complex software development, infrastructure, and business workflows. It functions as a multi-agent workflow orchestrator that routes tasks to domain-specific workers while maintaining state persistence and infrastructure automation. By leveraging large language models, the system decomposes high-level objectives into actionable plans, ensuring that complex operations are executed with consistency and reliability. The framework distinguishes itself through its hierarchical agent registry and policy-driven

The Frappe HR Management System is a human resources platform built on the Frappe framework for managing employee lifecycles, payroll, and attendance. The system provides tools for payroll and tax automation, including the generation of salary structures, tax slab calculations, and automated payslips. It includes an attendance and leave manager with geolocation-based check-ins and configurable holiday calendars, as well as a performance appraisal framework for goal alignment and structured review cycles. Additional capabilities cover the full employee lifecycle from onboarding to exit, along

ToolsOfTheTrade is a comprehensive, curated directory designed to help developers and operational teams discover software services for a wide range of technical and business requirements. It functions as a centralized resource catalog, indexing tools that support the entire software development lifecycle, infrastructure management, and organizational productivity. The project distinguishes itself by providing a structured index of both hosted and self-hosted solutions, enabling users to identify specific utilities for tasks ranging from continuous integration and project planning to applicati

Dokploy is a self-hosted platform-as-a-service designed to simplify the deployment and management of containerized applications and databases. It provides a centralized control plane that decouples administrative management from application workloads, allowing users to oversee infrastructure across multiple server nodes through a unified web interface or a command-line tool. The platform distinguishes itself through an extensive library of pre-configured application templates, enabling the rapid deployment of databases, identity providers, and various productivity or development tools. It sup

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

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

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

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

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

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