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
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
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
A Python package for aggregating and normalizing historical data from popular and free financial APIs.
The main features of theogognf/finagg are: Market Data Sources.
Open-source alternatives to theogognf/finagg include: edtechre/pybroker — pybroker is a Python algorithmic trading framework and quantitative technical analysis library designed for… ricequant/rqalpha — RQAlpha is a Python-native quantitative trading backtesting framework and live trading execution system. It provides… yutiansut/quantaxis — Quantaxis is a quantitative trading framework designed for building, backtesting, and executing automated strategies… backtrader/backtrader — Backtrader is a Python backtesting framework and algorithmic trading platform. It provides a toolkit for developing… alvarobartt/investpy — Financial Data Extraction from Investing.com with Python. akfamily/akshare — This project is a Python library designed for the programmatic retrieval and analysis of diverse financial datasets.…