With the rise of algorithmic and quantitative trading, there has been increasing interest among individual investors to develop and implement automated trading strategies. However, where to start can be daunting for beginners without a coding background. This article provides tutorials and resources for beginners to get started with algorithmic trading strategy development, with a focus on free and open-source tools.
Leverage open-source algorithmic trading libraries
One of the easiest ways to start is by using existing open-source algorithmic trading libraries. Frameworks like Backtrader and Zipline provide out-of-the-box functionality for strategy backtesting and live trading. Their Python-based APIs are easy to use even for those without extensive coding experience. Tutorials are available online to help users get started quickly. Pre-built example strategies can serve as templates for beginners to tweak and build upon. These libraries integrate with common data sources and broker APIs, minimizing the setup work needed.
Find strategy code examples on Github
In addition to tutorials, looking at open-source algorithmic trading projects on Github can provide useful code examples and templates to learn from. Repositories like AlgorithmicTrading and quantopian provide a variety of strategy examples in Python and other languages that demonstrate different techniques, such as trend following, mean reversion, pattern recognition, etc. Studying and running these examples help build an intuition on how strategies are formulated and implemented in code. The availability of code makes it easy for beginners to use them as starting points for experimentation and extension.
Learn from Kaggle competitions and datasets
Kaggle hosts a number of competitions and datasets related to algorithmic trading that are great resources for learning. Competitions like Two Sigma Financial Modeling challenge participants to develop trading signals from market data. Beginners can participate to get hands-on practice and learn from solutions and discussions shared by others. Apart from competitions, datasets like SP500 historical data allow experimenting with strategy backtesting across various time periods and market conditions. The availability of quality data removes the hurdle of data collection and cleaning.
Start simple and iterate
When starting out with little prior experience, it helps to start simple and gradually increase complexity. Begin with basic strategies like moving average crossovers rather than jumping straight into advanced machine learning algorithms. Test across different parameters and assets to gain intuition. Progressively incorporate real-world constraints like transaction costs, slippage and position sizing. This builds foundational knowledge before tackling more sophisticated techniques. Joining trading groups and forums helps accelerate learning from the experiences of others. With an iterative approach, beginners can steadily gain proficiency.
In summary, open-source libraries, Github code examples, Kaggle resources and an iterative approach help beginners get started with algorithmic trading strategy development at low cost. Leveraging these resources allows quickly gaining practical experience and proficiency in formulating and implementing automated trading strategies.