With the rapid development of financial technology, investment management software has become an indispensable tool for investors to efficiently track, analyze and optimize their investment portfolios. Choosing the right software can help investors save time, reduce errors, and make data-driven decisions. Here we summarize the top 5 best open source investment management software platforms available on Github, including their key features, main advantages and limitations.
1. QuantConnect – Comprehensive algorithmic trading platform
QuantConnect is one of the most popular open source algorithmic trading platforms on Github. It offers free access to historical and real-time financial data, as well as a cloud-based algorithmic trading engine to backtest trading strategies. Users can code trading algorithms in Python or C# and optimize them with QuantConnect’s built-in machine learning capabilities. The platform also supports live trading by connecting to brokers and executing algorithms in real-time. However, certain features like portfolio optimization are only available in the paid version.
2. Zipline – Pythonic trading library from Quantopian
Zipline is an open source Python library for backtesting trading strategies. It was developed by Quantopian and provides fast and efficient backtesting capabilities. Zipline integrates seamlessly with Pandas and Numpy for data analysis and visualization. It also includes built-in support for common financial metrics like risk analytics and performance attribution. However, Zipline focuses mainly on US equities and lacks support for other asset classes. It also does not support live trading out of the box.
3. Backtrader – Feature-rich framework for strategy development
Backtrader is an open source Python framework tailored for developing and backtesting trading strategies. It provides a clean and straightforward API for strategy development, freeing up time for research. Backtrader is highly extensible and supports custom indicators, analyzers, data feeds and brokers. However, it has a steep learning curve compared to other platforms and lacks some common features like portfolio optimization.
4. Moonshot – Vectorized backtester for Quantitative Research
Moonshot is an open source library that enables fast and efficient vectorized backtesting in Python. It builds on top of Pandas for speed and QuantRocket for brokerage integration. Moonshot emphasizes research, analysis and visualization to facilitate strategy improvement. However, Moonshot focuses exclusively on backtesting instead of live trading. It also requires basic knowledge of vectorization to take full advantage of its speed.
5. Algotrader – Java-based algorithmic trading platform
Algotrader is an open source Java-based trading platform for strategy development, backtesting and automated trading. It includes market data adapters, risk management components and connectivity to different brokers. Algotrader focuses on robustness and performance for intraday trading strategies. However, Java coding can be more complex compared to Python-based platforms. Algotrader also lacks some research and visualization features offered by other platforms.
In summary, QuantConnect, Zipline, Backtrader and Moonshot are great open source platforms for Python-based trading and quantitative research, while Algotrader offers a Java alternative. Consider factors like features, flexibility, asset class support and ease of use when choosing the platform that best fits your needs.