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Best Python Libraries for Algorithmic Trading

In this article we help you to define which Python libraries work best if you are actively engaged with algorithmic trading with Python

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Table of Contents:

  1. Best Python Libraries for Algorithmic Trading
  2. PyAlgoTrade
  3. Keras
  4. Pandas
  5. Scikit-Learn
  6. Numpy
  7. TA-Lib
  8. Conclusion

Best Python Libraries for Algorithmic Trading

Python is inarguably the most popular programming language in finance, especially in algorithmic trading. That's primarily thanks to its simplicity and a versatile large collection of libraries that include everything a quant would need for data analysis, optimal pricing, or machine learning.

At its core, algorithmic trading involves data collation and analysis, machine learning, deep learning, explainable artificial intelligence, and natural language processing. Whether you're a beginner learning algorithmic trading or an expert looking to create your own Python bot, these are the best Python libraries to accomplish this.


PyAlgoTrade is a Python algorithmic trading library designed for backtesting trading strategies, and it supports paper and live trading for Market, Limit, Stop and Stop-Limit orders.

It is perhaps one of the most consequential Python libraries for algo traders since it evaluates trading ideas and maps out historical data.

It allows data access from any time series data CSV, including Yahoo Finance, Google Finance, Quandl, and NinjaTrader CSVs. Users can backtest various event-driven strategies, including real-time Twitter events and performance metrics.

Since PyAlgoTrade is fully integrated with Python's TA-Lib, traders have access to over 100 technical indicators. However, it doesn't support Pandas-object and pandas modules.


Keras is an open-source neural network library in the Python programming language. It is widely considered one of the best Python libraries for algo trading due to its sheer simplicity.

Generally, it provides the simplest way to program neural networks and other deep learning models. It has some of the best utilities for compiling models, processing data sets, and generating a visualization of graphs.

The central design principle of Keras is modularity, which makes it flexible and ideal for innovative research.

Traders can easily forecast prices with artificial neural networks by creating a trading algorithm on Keras with five simple steps:

  1. Define the various layers and their connections in the model - whether Functional or Sequential model - and then define the dataflow.
  2. Compile the network, preferably using Keras' model.compile() method.
  3. Fit the network - basically inculcate the model to the data.
  4. Evaluate the network to identify the errors.
  5. And finally, use model.predict() to make predictions with new data.

Neural networks can be created and configured with the abstract modules provided by Keras without much concern for the underlying backends.

By design, users can switch between different backends in Keras, from Theano, TensorFlow, Apache MXNet, CNTK (Microsoft Cognitive Toolkit), and PlaidML

Note that Keras also supports almost all types of neural networks from fully connected, folding, pooling, embedding, convolutional, recurrent, etc., which can be combined for more complex models.


Pandas is a machine learning library in Python. It derives its name from "panel data," a term for structured, multidimensional data, and it's primarily used for data analysis, modeling, and manipulation, particularly for numerical tables and time series.

The most distinctive feature of Pandas is its ability to simplify the computation of complex data with just one or two commands.

You can simplify data manipulations with dataframes like missing values, columns, etc. Data can be aggregated and merged easily with the groupby, agg, and merge functions. It also supports iteration, re-indexing, concatenations, aggregations, sorting, and visualizations.

Beyond simplifying complex data, Pandas is also easy to use and allows you to easily read data in different formats - CSV and text files, Microsoft Excel, and SQL databases.


Scikit-Learn is a machine learning library built on NumPy, SciPy, and Matplotlib. Some of its core algorithms are written in Cython to improve performance for various high-level operations.

Its popularity mainly stems from the ease of use and the several machine learning techniques it implements to perform supervised and unsupervised learning.

Scikit-Learn offers simple and efficient tools for predictive data analysis, which come in handy for algo trading.

It has a range of tools for machine learning and statistical modeling, and creating a predictive model can be done in only six steps:

  1. Classification to identify the category associated with the data.
  2. Regression involves creating a model that attempts to understand the relationship between input and output data.
  3. Clustering by automatically grouping similar objects into sets.
  4. Dimensionality reduction to decrease the number of random variables to be analyzed.
  5. Model selection with tools that compare, validate, and select the best parameters and models for your input.
  6. Preprocessing to standardize the data set.


Numerical Python (Numpy) is a fundamental library for performing numerical calculations with Python. It is invaluable in managing data arrays with a large number of functions to generate Ndarray objects. Several other machine learning and neural network libraries utilize Numpy in their operations.

In algorithmic trading, Numpy is used to maximize the speed of the core simulation logic. It provides a universal data structure that enables data analysis and exchange between different algorithms. The data structures it implements are multidimensional vectors and data-intensive arrays.

The library also provides a whole arsenal of functions to perform complex mathematical calculations such as trigonometric functions (np.sin(), np.arctan()...) or exponential and logarithmic functions (np.exp(), np.log()...).

Python is not optimized for numerical calculations; the default interpreter executes mathematical routines as non-optimized bytecode.

More complex calculations and computing large amounts of data are inefficient in Python. To compensate for this shortcoming, libraries such as NumPy are used. NumPy simplifies the use of arrays and enables multidimensional array operations.


As the name suggests, the Technical Analysis library (TA-Lib) is an open-source Python library dedicated to performing technical analysis on financial data using technical indicators.

The library is typically regarded as the golden standard for technical analysis since it contains over 150 technical indicators and has modules for candlestick pattern recognition.

Building a Python trading bot with TA-Lib is pretty simple. You'll need to import the necessary Python libraries - yFinance to download historical stock data, Pandas to load the data into a dataframe, and Matplotlib for charting.

After downloading and storing the data into a dataframe, you can create any technical indicator for the data.

The over 150 indicators can be categorized into seven groups - overlap studies, momentum indicators, volume indicators, volatility indicators, price transform, cycle indicators, and pattern recognition.


Algorithmic trading as we know it today wouldn't really exist without Python. It is the most preferred scripting language for complex data analysis and financial modeling.

Its vast and diverse libraries offer a rich set of ready-made code that makes it easy to build machine learning models without writing the code for any of these models from scratch.

It's ideal whether you want to create a simple technical indicator or to model complex technical and real-time fundamental news.

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