With the rise of digital transformation, more and more investors and asset managers are seeking automated and intelligent investment management software to improve operational efficiency. Github marketplace, as the world’s leading open source community, offers some great options for investors looking for customizable and cost-effective solutions. By leveraging the power of open source technology and community wisdom, investment management software on Github helps investors optimize their portfolio faster and gain valuable insights for better decision making.
ZVT – Auto Data Collection and Factoring for Investment Management
ZVT is an open-source project focusing on investment data collection, factor generation and back-testing. It automatically collects data from multiple sources, computes factors using pandas and SQL, and provides back-testing capability with visualization. With modular design, ZVT makes it easy to customize data collection, define new factors and optimize strategies. Its scalability also allows fast back-testing on large datasets.
Quantdom – Python Framework for Trading Strategy Analysis
Quantdom is a Python-based open source framework for analyzing financial markets and back-testing trading strategies. It integrates seamlessly with other libraries like NumPy, SciPy, Pandas to enable fast development. Quantdom also utilizes cutting-edge machine learning techniques to empower strategy optimization and risk management. With Quantdom, investors can quickly build and iterate trading models, evaluate performance through back-testing, and identify profitable signals in a scientific approach.
QuantSoftware Toolkit – Modular Framework for Portfolio Management
QuantSoftware Toolkit is an open-source Python framework designed for portfolio construction and management. It incorporates professional risk models like Barra to support optimization techniques. The toolkit also provides ready-to-use components for real-time trading, interactive visualization, transaction cost modeling etc. By connecting to market data feeds, QuantSoftware Toolkit allows investors to seamlessly transition from research to paper/live trading. Its modular design also makes it convenient to customize and scale up.
BT – Python Library for Flexible Backtesting
BT is an open-source Python library focused on backtesting for trading strategy evaluation. It supports vectorized backtesting for faster computation using NumPy and Pandas. BT allows flexible configuration and extension to customize datasets, trading logic, commissions etc. And its event-driven engine can simulate complex real-world enviroments for reliable backtesting. As an easy-to-use and well-documented library, BT lowers the barrier for investors to analyze and optimize their trading strategies.
Github open source software provides great options for investment management automation and analytics. Projects like ZVT, Quantdom, QuantSoftware Toolkit and BT offer customizable solutions for data collection, factor modeling, portfolio optimization, backtesting and more. By combining modular design, powerful analytics and active community support, Github investment management software enables investors to boost efficiency and returns.