How to Create a Momentum Trading Algorithm in Python

Algorithmic trading is a popular trading strategy that involves the use of complex computer programs called algorithms — sets of instructions that analyze incoming data and make pre-planned decisions based exclusively on that data. As a result, algorithmic trading strategies are ideal for removing emotion from investing and reacting incredibly quickly to changing market environments. 

Yet, in order to get the most out of the algorithms that drive this type of trading, you need to know how to write the computer code they run on. You must understand how to use a programming language to create algorithms and program them to act as you wish. 

Today, most trading algorithms are written in Python, so understanding algo trading also means understanding Python. 

Why is Python so Popular for Algorithmic Trading?          

So what is it about Python that made this particular programming language the favorite of algo trading programmers? Python is popular for three main reasons: It's accessible, simple, and powerful.

Accessibility

Python is an open-source programming language. In more direct terms, it's a free software platform that anyone can download and use. This open-source nature also means that using Python for commercial purposes doesn't create any additional problems for those who use it to make money.

Simplicity

Python was designed to be easy to use, and it's often cited as one of the best places to start if you've never encountered a programming language before. It's easily readable and widely adopted, which means it's often quite easy to troubleshoot code whenever there's a problem.

Power

Despite Python being a simple-to-use programming language and one that's widely available, there are no trade-offs when it comes to how powerful it is. It's a speedy and responsive language thanks to strong integration and text processing capabilities, it's widely compatible with multiple platforms, and it has a large collection of libraries and frameworks to pull from when necessary.

Pros and Cons of Python for Algo Trading

Many of the core characteristics of Python make it ideal for algorithmic trading. The fact that it's open-source and that all of its packages are free for commercial use are perhaps the most important benefits of using Python for algo trading. In addition, programmers enjoy using Python because it's easy to build data connectors and execution mechanisms, makes backtesting efficient, and aids in risk management and order management, to name just a few advantages.

Python does have some cons for algo trading, however. No single programming language is perfect, after all. These drawbacks include issues with memory management because of the way Python is designed. Without careful attention to detail, it's possible for performance bottlenecks and memory leaks to occur when using large data sets. With high-volume algo trading so commonplace, these issues can occur with some regularity if steps aren't taken to address the memory problems native to Python.

Still, Python is considered a high-level programming language. Although it is more user-friendly than other, similar languages, that doesn't mean you won't benefit from identifying some key concepts about how it functions.

First, Python is designed for source-code analysis. The programming language receives source code and analyzes the syntax to ensure it's compatible. If there are any incorrect lines, the program is stopped, and error codes are generated for programmers to pinpoint the problem.

Next, if the source code doesn't trigger any errors, byte-code generation occurs, which transforms the code into something called an abstract syntax tree (AST). Python then loads its virtual machine, its runtime engine, which loads that AST and converts that byte code into executable code. Then, Python prints the results. Again, if there are problems with this code, the program will stop and generate error codes for programmers to investigate and fix the issue.

Related: Easy Trading Algorithms for Beginners >

The Basics of Python for Algo Trading

Python requires some strong foundational understanding before you can begin to use it for algorithmic trading. These "basics" aren't necessarily basic when it comes to their complexity, but they are considered the skills that any entry-level Python developer would need to be effective at creating algorithms destined for automating financial market trading. These skills include:

Minimum core expertise

At the very least, you need to know how to program in Python. This includes how file and exception handling works, iterators and generators, different data types and variables, and how data structures work within the Python ecosystem.

Knowledge of necessary web frameworks

Like many other programming languages, Python uses web frameworks for simplicity and efficiency. These are collections of packages or modules that automate low-level details like managing protocols, sockets, or processes and threads. The most popular web frameworks include Flask and Django.

Familiarity with object-relational mapping techniques

Python is what's called an object-oriented programming language. These languages convert data between two otherwise incompatible systems using object-relational mapping (ORM). ORM is used to create virtual object databases in any programming language, including Python, so you'll need knowledge of the relevant techniques.

General data science skills

Algo trading is, by definition, a practical application of data science. The better you understand the fundamentals of data science, the more you'll be able to leverage those fundamentals in creating algorithms. More specifically, data science is all about extracting meaningful insights for business from large data sets, and it's a multidisciplinary approach.

Python makes heavy use of libraries — collections of related modules packaged together and designed to work in concert with one another. Knowing which pre-existing libraries you should use is obviously helpful, so here's a short list to get you started:

• Libraries for data: yfinance, Zipline

• Libraries for visualization: Matplotlib, seaborn

• Libraries for technical analysis: backtrader

• Libraries for trading analytics: FinTA, Freqtrade, CCXT

Building Your First Trading Strategy in Python

Algo trading is all about designing an algorithm that lets you automate a specific trading strategy. This requires a lot of trial and error on your part, as you'll have to learn how to test, optimize and deploy your finished algorithm. 

Testing: Most algo testing is done in a safe environment using historical market data. This is called backtesting, as it lets your algorithm loose on real-world data without any negative consequences if something goes wrong. A number of online platforms, such as PyPI, offer backtesting services to put your algorithm through its paces.

Optimizing: There's always room for improvement when it comes to algo performance. Optimizing your algorithm involves looking for portions of the code that can be redesigned to eliminate possible issues. With Python, this often means hunting down places where streamlining your code will reduce bottlenecking and eliminate memory overflow errors, letting your algo run more smoothly and without problems.

Deploying: Once you're ready for prime time, you'll have to deploy your algorithm in a live environment. Again, there are a number of platforms that will allow you to import your code and execute it. Be sure to monitor your algorithm closely — even if you've backtested and optimized your algo to perdition and back, live markets can be unpredictable. 

Related: 9 Examples of the Best Algorithmic Trading Strategies >

Using Composer to Fast-Track Algo Trading            

Speaking of ideal trading platforms for beginners, Composer can help fast-track your algo trading career. Python is a great way to create momentum trading strategies, but might not be suitable for those investors who don't have a strong technical background or don't already know how to code. The key is that Composer uses a unique no-coding approach that allows you to harness the power of algorithmic trading with little or no technical knowledge of Python. It's ideal for beginners in the world of algorithmic trading and offers an innovative approach that you won't find anywhere else.

Algorithmic trading is a popular approach but leveraging it to its full potential involves understanding the intricacies of Python. This programming language is one of the most popular choices for algorithm development, and learning Python will help you get started on the road to success as an algo trader.