Best Python Libraries For Algorithmic Trading (Examples)

ByOddmund Groette Python Trading Strategies

Libraries are an essential part of Python that makes programming faster and easier for developers. These two qualities are especially relevant in Algorithmic Trading.

That’s why, in this article, we will explore some of the best algorithmic trading libraries in Python, including those to download data, manipulate data, perform technical analysis, and backtest trading strategies.

What is a Python library?

Think of a Python library as a box of tools that contains different sets of ready-made code. This code can be used over and over again in various programs.

This makes it easier and more convenient for programmers because they don’t have to write the same code multiple times for different programs. 

Python libraries are really important, especially in areas like Machine Learning, Data Science, and Algorithmic Trading.

Python-related resources

We have written many articles about Python, and you might find these interesting:

Python Libraries for Downloading Stock Data

The most popular library to download data is yfinance. This library allows users to download the historical data of any stock or ETF from the Yahoo Finance website for free. Additionally, it includes fundamental data such as income statements, trading multiples, and dividends, among others.

It was developed by Ran Aroussi after the official API was taken down on May 15, 2017. If you want to learn more about the functionalities and how to use it check out our article called how to download data for your trading strategy from Yahoo!Finance with Python.

The Pandas Data-Reader library is another famous resource used to download data. It provides functions that extract data from various Internet sources, including Yahoo!Finance, the Federal Reserve Economic Data (FRED), Fama/French data, etc. We have previously covered how to build a trading strategy from FRED data in Python. The linked article shows you an example of how to download data from the FRED website.

We decided to focus on free resources, but keep in mind there are also premium APIs, such as Alpha Vantage and Quandl.

Python Libraries for Data Manipulation and Plotting

Pandas is probably the most popular library in Python (in general). It is a free tool for Python that many people use for data manipulation and analysis. It’s made on top of another library called Numpy, which provides help when dealing with numerical tables and time series. 

Pandas lets you bring in data from different types of files like comma-separated values, JSON, Parquet, SQL databases, and Microsoft Excel. With Pandas, you can also do things like putting data together, changing its shape, picking out specific parts, and making the data neater and more organized. It is kind of the Excel of Python.

This is, for example, a pandas data frame of the SPY historical data from Yahoo Finance:

Python Libraries for Backtesting trading Strategies

Another crucial aspect of data is its visualization, and that’s where Matplotlib enters the game. Matplotlib is a basic plotting library in Python. It allows you to create different charts, including line, scatter, 3-D, and polar plots, among others. For example, the following chart was created using matplotlib and the data frame mentioned above. It shows the daily adjusted close for the SPY since 1993:

Python library example

Python Libraries for Technical Analysis

TA-Lib, short for Technical Analysis Library, stands as an open-source toolkit widely employed for conducting technical analysis of financial data. The library offers over 150 technical indicators and trading functions to recognize trends, gauge momentum, and evaluate the comprehensive market strength and direction.

Another almost identical option is Pandas TA. The library was built on Pandas and Numpy and works similarly to TA-Lib.

Python Libraries for Backtesting

Lastly, we have to mention Python’s specific libraries for backtesting. Although performing a backtest using just pandas is possible, these libraries sometimes provide a deeper analysis of the strategy.

One of the most well-known is Backtrader. Backtrader is an open-source library used for backtesting, strategy visualization, and trading. The library provides many features that facilitate the backtesting process, having specific single lines of code for special functions.

Zipline is another Python library that supports both backtesting and live trading. Although Quantopian, zipline developers, went out of business, thanks to the power of open-source, the library keeps running without problems.

Best Python Libraries for Algorithmic Trading – Conclusion

To sum up, today you learned about the most popular Python libraries for algorithmic trading out there. Although there are hundreds of them, the ones we showed you today are more than enough to start your quant journey backtesting trading strategies.

I’ve got an Msc from Heriot-Watt University, Edinburgh (1996), in addition a to a business administration degree the Norwegian School of Management (BI – 1994). Did my mandatory military service in between.

After university, I worked two years as an auditor (1996-1998).

I co-founded Aksjeforum.com in 1998/99 - one of the first websites about trading and investing in Norway. It was later acquired by Digi.no in 2001.

I have written 4 books about trading (in Norwegian). One of them has sold 30,000 copies, a record for a financial book in Norway.

From 2001 until 2018 full-time independent prop trader (Series 7 in 2001) and investor.

2018-today: Investor, writer, analyst.

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