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Python is the most popular coding language for people who work in quantitative finance. Investors, asset managers, and investment bankers use it for tasks that range from high-frequency trading to rebalancing portfolios. While users may prefer Python because it is easy to read and understand, it also has numerous libraries that can be imported and used to perform a variety of finance-related functions, analyses, and data manipulation.
Python has seven libraries that are excellent resources for those working in quantitative finance. Firstly, it is the industry standard for professionals and traders to have access to the stack – NumPy, SciPy, pandas, and Matplotlib. These intertwined libraries have functionalities that work together to provide a wide array of tools to people working with the market. Tools that can be incorporated into the decision-making process.
In practice, Python libraries enable its users to do backtesting, data analysis, option pricing, and machine learning. These libraries are all open-source, third-party modules that provide Python users with pre-programmed functions that simplify coding. They also give professionals the output, visual images, and data analyses needed to successfully make prompt, efficient, well-reasoned decisions about the market and its price movements.
Besides giving Python coders the power to create and adjust their trading algorithms based on activity in the market, these libraries make it possible to keep the programming and decision-making in-house. Firms using Python and its libraries don’t need to outsource their financial analysis to third-parties. The ability to operate without intermediaries means that firms can make changes in their operations faster, operate more efficiently, and better understand the market. Ultimately, Python enables firms and finance professionals to create, modify, test, reevaluate, and backtest their decision-making models.
To become proficient with Python, follow our best advice and check out the best Python libraries for quantitative finance we’ve found for you.
Python’s Best Libraries for Quantitative Finance
NumPy is a must-have library that is an industry standard. It contains the basic mathematical functions needed to do basic data processing in quantitative finance. Its functions can be used to create and manipulate matrices and arrays. In addition, it has random number generating functions and mathematical functions that can be used with integers and floating numbers.
SciPy is almost always imported with NumPy. It is like an extension of NumPy. SciPy has more advanced data manipulation functions than NumPy.
This library’s tools are needed to incorporate numerical integrals, differential equations with matrices, linear algebra, signal processing, statistics, interpolation, and optimization into data analysis and decision-making processes. It also has databases for SciPyclasses and routines, and pre-programmed functions like NPV, IRR, IPMY, and PPMT.
Other more advanced functions in the library are:
● Optimization functions
● Linear algebra functions
● Image processing
● Signal processing
● Ordinary differential equation solvers
● Partial differential equation solvers
Matplotlib is used to visualize data in 2- and 3-dimensional charts and graphs. This library gives you the tools to create charts for hard copy formats and interactive platform environments. The downside of this library is that it is not native to Python and lacks Python documentation. Therefore, it can be difficult for Python users to learn and become comfortable using it. The effort it takes to conquer the steep learning curve that goes with mastering this library is more than worth it. After you realize what you can do with it and how well it can communicate your ideas and market observations in great visuals and without lots of time-consuming effort, you’ll be glad that you did it.
pandas is the last library in the SciPy stack that most Python users in quantitative finance find to be essential to their work. The SciPy stack contains NumPy, SciPy, Matplotlib, and pandas. All the libraries work with one another to provide basic and advanced data processing, analysis, manipulation, and visual depictions.
pandas contributions to the stack are tools for data manipulation and analysis. It’s ideal for manipulating time and series data, and almost a necessity when analyzing price movements in the market. One of its primary uses is creating and manipulating data tables and creating and manipulating time series data.
Scikit-Learn is designed to manage most machine learning techniques (e.g., clustering, classification). It is used for processing data, classifying information, and eliminating data clusters in data science analytics. Moreover, it is a basic platform for numerous processing interfaces that are used to monitor production against stock levels and payments to vendors and suppliers. To function properly, Scikit-Learn must be able to interact with the NumPy, Matplotlib, and SciPy libraries.
For professionals working in the areas of structuring and valuation in quantitative finance, QuantLib provides the tools required to value and calculate the risks associated with financial derivatives.
QuantLib, like Matplotlib, is not written in Python. It doesn’t have any Python documentation, and it also isn’t native to Python. On account of these things, Python coders are likely to have to overcome a steep learning curve before they can use it.
This library provides a suite of tools that make it possible for users to export data and create well-formatted .xlsx files. Technical and non-technical people will find this to be an excellent resource for creating resource material that cannot be created using only Python scripts.
Other Libraries of Interest
This data science library is useful for paper trading and real-time assessment of trading markets. It is perfect for retrieving results with little effort because the results are based on historical data and patterns.
Pyfolio’s tools are used to analyze finance portfolio risks. In addition, users can create plotting and time series charts and graphs via its statistical functions.
A library perfectly designed for backtesting and live trading. Its finance module removes any bias when its processing cost and order delays. Moreover, the library is regularly updated by Quantopian. Regular updates translate into users having access to the most up-to-date finance analytics.
Finmarketpy is a library that is used for market analysis and trading strategies via use of a database interface with pre-installed templates for quick, efficient, processing. It is advantageous when users want to run strategies during a specific time in the market, which can improve comprehension of seasonal market changes and performance. Other benefits include using it for doing surveys and data observation with specified parameters.
There are seven libraries that almost everyone working with quantitative finance will want to import and access during their coding. Still, there are other libraries that may be useful to you and your firm, depending on what you want Python to do for you. When deciding on which libraries to use, you should research which other libraries are needed to support it and whether the library is native to Python and has Python documentation.
The key here is that the benefits of some of the best Python libraries cannot be accessed until the user has overcome a steep learning curve and mastered the skills needed to produce the desired outcomes. Overall, the range of functions and algorithms available to professionals in quantitative finance using Python are so vast and growing that it is likely to continue to be the industry favorite for some time.