Best Financial Python Tools for Trading & Analysis

Python is one of the most popular programming languages in the world. Its historic surge in popularity can be partially attributed to the wide range of libraries and tools developed using it as the core API framework.

Whether you’re a seasoned developer or just learning to code, there are a wide range of Python libraries available to help support you in your endeavors of financial analysis, algorithmic trading, and machine learning modeling. Below you’ll find a listing of some of the best Python tools for financial trading and analysis.

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This formerly crowd-sourced investment firm offered a wide range of free tools to do things like invest, trade, and train learning algorithms. Unfortunately, this company is no longer active but still offers some useful tools like their Zipline library, billed as a Pythonic algorithmic trading library.


This “feature-rich” toolkit is used to run robust stock analysis within the familiarity of a Python environment. It features 0-indexed collections, clean, object-oriented design, and powerful visualization tools to help keep track of where and what you’re tracking. Backtrader is excellent for beginners but can be used for larger-scale analysis as well.


Quantrocket is a powerful platform service that allows data-driven traders to test out their strategies on historical data. This platform is unique in its allowance, deep integration, or third-party analysis tools like Zipline, Moonshot, and standard Machine Learning packages. Of course, you’re free to use your tools as well!


Intrinio offers a range of useful market data products to help train trading algorithms. Their selection includes market fundamentals, real-time pricing data, and even SEC filing data. These data are accessible via super friendly APIs that make integration a breeze. The downside is that the majority of the data is not free.


Quandl is one of the largest data providers on Earth and offers a wide range of market-related data to power even the most byte-hungry algorithms. Quandl is an enterprise as they come and offer access to data through Python, Ruby, direct download, and standard formats such as CSV. They offer partial access to large portions of their data upon the creation of a free account.


This open-sourced tool developed by Quantopian was designed to work with other Quantopian tools like Zipline but can be used with plenty more. It’s a core “tear-sheet” analysis library to help analyze the performance of algorithmic trading tools. This tool will provide you with numbers like annual return, cumulative return, annual volatility, and several more niche performance metrics.


NumPy is one of the most popular Python libraries for working with arrays of data (think spreadsheets) and is integral to most Machine Learning workflows. It allows the declaration of custom data types, seamless integration with databases, and provides lower-level access to custom C/C++ toolchains for those that like to roll up their sleeves.


Pandas is similar to NumPy but more catered to the manipulation of data rather than the loading and unloading. This library was developed by a hedge fund and shines when manipulating numerical tabled data and/or time-series data. It provides automated consumption/formatting of things like DateTime objects, making for a super user-friendly workflow.

SciKit Learn

The defacto Machine Learning library for Python can help implement the core ML methods used for algorithmic trading and analysis. This library provides a wide range of ML models to work with and allows for such analysis as Linear Regression in so few lines of code it’s sometimes startling. Worth noting: this library makes use of both NumPy and Pandas and is deeply integrated with both.


TensorFlow is the brain-child of Google and, arguably, the most powerful open-source machine learning library in existence. This library isn’t necessarily developed with financial analysis, trading, or performance analysis in mind—but it can handle them all with ease. One thing that makes this Python library so attractive is its massive amount of documentation, focused learning resources, and ongoing development.

Final Thoughts

It’s a little mystery of how Python continues to evolve in popularity. It’s a wide-ranging ecosystem of libraries and APIs that help support an immense range of industries. Data scientists, Biologists, Linguists, and even Social Scientists all drive their studies with powerful open-source Python libraries. The libraries listed here are all capable of helping support stock trading, machine learning, and financial analysis—but don’t think this is all Python has to offer! There are plenty of other libraries out there that are designed to or can be catered to, algorithmic trading and financial analysis.

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