With the rise of fintech and open source development, there are now many API and open source frameworks available for beginners to get started with algorithmic trading and investment strategy development. By leveraging these resources, beginners can focus more on strategy rather than infrastructure. In this article, we will explore some of the most popular open source frameworks like FinRL, Backtrader, Zipline etc. that provide end-to-end pipeline for trading strategy research, development, and backtesting. We will also look at how to combine different frameworks to create a customized workflow.
Leverage Open Source Frameworks to Focus on Strategy
The key benefit of open source frameworks like FinRL, Backtrader, Zipline is that they provide all the scaffolding required for systematic trading. This includes APIs for downloading and cleaning market data, building trading environments for backtesting, implementing reinforcement learning algorithms, backtesting with transaction costs and slippage modeling, and analyzing performance etc. By using these frameworks, beginners can directly start focusing on strategy research rather than building infrastructure from scratch. For example, FinRL provides implementations of state-of-the-art deep reinforcement learning algorithms like DDPG, PPO, SAC etc. that can be readily applied for stock trading. Similarly, Backtrader allows quick strategy implementation with its event-driven backtesting engine.
Combine Frameworks for Customized Workflow
While single frameworks provide an end-to-end pipeline, sometimes it is useful to combine frameworks for a customized workflow. For example, we can use Zipline for its powerful pipeline API to screen stocks and generate signals. These signals can then be fed into Backtrader which allows flexible strategy definition for entry, exit and position sizing. The backtested results from Backtrader can be analyzed in Pyfolio for risk analysis. We can also mix open source and proprietary systems, for example integrating Zipline for backtesting with QuantConnect for live trading. This enables us to leverage the strengths of different frameworks in each part of the workflow.
Active Community Support for Learning
Most open source frameworks have an active community and resources for learning. FinRL, Zipline, Backtrader all have detailed documentation and tutorials available. There are also many tutorials and code examples from community members sharing how they have used the frameworks. Active forums allow beginners to get help quickly when facing any issues. The open source nature also means users can look under the hood and understand how the frameworks have been built. Overall, this provides a conducive environment for beginners to learn and experiment rapidly.
Open Source Strategies as Inspiration
In addition to frameworks, GitHub also contains many open source trading strategies built using these frameworks. These serve as a great starting point for beginners to learn from and build upon. For example, there are number of trend following and mean reversion strategies implemented in Backtrader and shared on GitHub. We can start with these baseline strategies, understand the logic, and then enhance and optimize them further. Studying open source strategies helps beginners learn the right approach to designing and evaluating systematic trading strategies. And open sourcing your own strategies can also allow you to get feedback from other developers.
In summary, leveraging open source frameworks like FinRL, Backtrader, Zipline can accelerate the learning curve for beginners in algorithmic trading and investment strategy development. Combining multiple frameworks allows creating a customized research and trading workflow. Active community support and open source strategies provide ample resources to learn and get inspired for coming up with your own strategies.