There are a few programmes that traders use to back test their trading strategy and Python is a common one.
As with anything, all programmes have their pros and cons and when it comes to trading, Python is ideal for low to medium frequency trading but generally not suited to high frequency trading where trading is done in nanoseconds, as opposed to minutes.
What is Python?
Python is a free and open-source computer programming language that’s known for it’s ease of use, particularly if you’re a beginner. It’s a general-use programme that’s not created with any special problems in mind, but has a huge library and research environment that covers almost every situation possible. For example, the Python language has been used in Amazon’s recommendation engine while Uber, Netflix, Spotify and IBM are all high-level businesses that use Python.
And it’s one of the most popular languages in trading, partly because it’s great at calculating large quantities of data and also because it’s free.
But your choice of language very much depends on your business needs and what you’re trying to achieve. But if you’re a serious quant trader you’ll need to know a programming language.
Backtesting with Python
Backtesting is easily the most crucial part of any systematic trading strategy. Backtesting done well should expose any problems and miscalculations in the hypothesis. What’s more it will help you make a calculated guess on how much to invest.
When you’re backtesting Python, it’s considered unrestrictive, great at calculating large quantities of data and there are a number of testing frameworks already available in Python.
These include:
· PyAlgoTrade
· bt
· pythalesians
· QSTrader
· pysystemtrade
· Backtrader
· Zipline
These and more like them are what makes Python so suitable for low to medium frequency trading.
In general Python boasts reliable backtesting frameworks, and as mentioned, the language is so easy to learn that it’s easy to get a test strategy out the door. Even if you’re not a ‘hard’ programmer.
What’s more Python comes with brilliant additional extras with among the most comprehensive sets of data science, machine learning and statistical libraries of all languages.
Backtesting is Challenging Anyway
With a backtest you’re looking at the history of an asset to see how it might perform in the future. But it’s tricky. Trying to recreate all the factors that cause an asset to peak and trough is challenging. And of course, this is still no guarantee of how it will perform in the future.
Python is superb for backtesting research, that is, where you are researching which backtesting strategies to actually consider as the final framework.
Here it’s the pace of the development that’s key and as Python is such an easy language to write. Traders might use other languages for the strategy execution and where speed is more important, such as nanosecond frequencies, but when it comes to the execution for low frequency trade strategy, Python is still a top choice among traders.
Final Thoughts
Backtesting can be tough but it’s a crucial part of any systematic trading strategy. Python is one of the most widely used computer languages for testing trading strategies, thanks to its ease of use, the number of libraries and other applications such as machine learning.
Trading aside, Python is used by some of the world’s biggest companies because it’s such an easy and well supported language to use.
Before you start though make sure your preferred platform is supported.
Python is definitely one of the leading programming languages if you want to test the idea of a strategy and quickly too. And if you’re testing low or medium frequency trades, Python is still one of the most widely used back testing languages.
It’s not so effective if you’re trading in high frequency assets, however, and you should use a different language if you are trading in seconds or even nanoseconds.