How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
The main features of dgunning/edgartools are: Data Pipelines, Market Data Sources.
Projects with overlapping indexed features include: ranaroussi/yfinance — This library is a Python-based tool for retrieving historical and real-time financial market data from public sources.… akfamily/akshare — This project is a Python library designed for the programmatic retrieval and analysis of diverse financial datasets.… ricequant/rqalpha — RQAlpha is a Python-native quantitative trading backtesting framework and live trading execution system. It provides… edtechre/pybroker — pybroker is a Python algorithmic trading framework and quantitative technical analysis library designed for… backtrader/backtrader — Backtrader is a Python backtesting framework and algorithmic trading platform. It provides a toolkit for developing… yutiansut/quantaxis — Quantaxis is a quantitative trading framework designed for building, backtesting, and executing automated strategies…
This library is a Python-based tool for retrieving historical and real-time financial market data from public sources. It functions as a programmatic interface for downloading stock prices, dividends, financial statements, and corporate calendars, allowing users to perform automated research and analysis on various market assets. The project distinguishes itself by structuring retrieved financial time series directly into tabular data frames, which facilitates mathematical analysis and manipulation of market metrics. It supports efficient data retrieval through multi-threaded batch downloadin
This project is a Python library designed for the programmatic retrieval and analysis of diverse financial datasets. It functions as a comprehensive toolkit for quantitative research, providing a unified interface to fetch historical and real-time market data across asset classes including equities, futures, bonds, cryptocurrencies, and foreign exchange. By abstracting complex network requests into simple, parameter-driven functions, it enables users to integrate financial data into research workflows and automated trading systems. The library distinguishes itself through its scraper-based ag
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
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