If you are a finance professional (or student, or fresher), then you might have noticed that there is a sudden frenzy in your peers to learn about coding (mostly python).
As a matter of fact, python has very rapidly become one of the most widely used technologies in the world. And now, because of its applications in financial modeling, it has also become one of the most important technologies in the world of finance.
Python for finance has emerged as one of the most important tools as it has massive capabilities in the field of data manipulation, visualization, and modeling.
In fact, Python can help you model your financial data quite efficiently and in way less time than your traditional softwares. (or MS-excel for that matter)
In any case, if you are someone who is pursuing a career that will require you to perform financial modeling, then Python is for you.
In this article, we illustrate some of the best and most useful python libraries and packages you can use to efficiently model your financial data
That is why some businesses grow in popularity in a short period of time. It’s largely due to the use of Python modules in their decision-making process in that case.
Best Python packages for financial modeling
Insurance, lending, and trading, as well as e-banking and other payment services, are all examples of financial technologies.
The modules listed below concentrate on quantitative finance applications that require programming tasks such as data importation and transformation, time series and risk analysis, trading and backtesting, excel integration, and data visualization.
Here are the Python modules with a reference to the financial world.
NumPy is yet another useful finance module based on mathematics and calculations. NumPy is primarily used in Python for mathematical and scientific functions. The first few versions established a long-term framework for financial purposes.
Some of the most basic operations like Array manipulations and data conversions can be achieved using Numpy. Here’s the complete documentation for the Numpy package.
It was recently updated to compute matrices that can be used to comprehend a variety of data science structures. As previously stated, its significance can be seen in how several of these finance modules are based on the NumPy module.
NumPy was created in 2005 by Travis Oliphant. It’s an open-source project that you can use for free. NumPy is an acronym for Numerical Python.
Pyfolio is the next module on the list. The data science python module aids in the analysis of risks in financial portfolios. Pyfolio was made public in 2015, along with an open-source reference library.
The module employs tear sheets based on returns and integrates transactions with Bayesian Analysis. Plotting and time series based on statistical functions are some of Pyfolio’s additional features.
Furthermore, PyPortfolioOpt is the library that implements portfolio optimization techniques such as mean-variance optimization and Black-Litterman allocation, as well as more recent developments in the field such as shrinkage and Hierarchical Risk Parity.
Here is the complete documentation.
Scikit-learn is up next on our list. This module’s functions are very diverse and fast, and their scope extends beyond the finance modules available in Python.
Scikit-learn is primarily used for data processing, information classification, and data cluster elimination to aid in complex data science analytics. The module serves as the foundation for many processing interfaces that are still used to track production against stock levels and to pay vendors and suppliers.
Scikit-learn is primarily concerned with machine learning tools, such as mathematical, statistical, and general-purpose algorithms that serve as the foundation for many machine learning technologies.
You can find the complete documentation here.
SciPy employs mathematical algorithms and functions to derive results. It is basically an extension of the well-known NumPy module. Numerical integrals and differential equations with sparse matrices are used by SciPy.
However, it is primarily a database of SciPy classes and routines. SciPy includes functions for calculating NPV, IRR, IPMY, and PPMT.
It can be better understood by the fact that SciPy is an abbreviation for Scientific Python. It includes additional utility functions for optimization, statistics, and signal processing.
SciPy, like NumPy, is open source and free to use. Here’s its documentation.
Zipline is another Python library that focuses on financial assistance. It is, however, event-based rather than algorithm-based. Backtesting and live trading are best done with Zipline. Its structure is comprised of Python Zipline code.
Furthermore, the finance module is in charge of removing any bias from each cost and order delay. It is overseen by Quantopian, which updates on a regular basis to assist users in diverting the most recent analytics for financial use.
Zipline is officially compatible with Python 2.7 and Python 3.5.
Here is the complete documentation.
Saving the best for the last!
Finmarketpy is another Python module that deals with finance. Because the module has a simple database interface with pre-installed templates for a quick approach, it is used in market analysis and trading strategies.
You can use the library to run strategies in a specific timeframe to better understand seasonal challenges.
Finmarketpy is also a great module for conducting surveys and observing data with different parameters. The Finmarketpy module is also dependent on the SciPy libraries.
In a nutshell, Finmarketpy is a Python-based library that allows you to analyze market data and backtest trading strategies using a simple API that includes prebuilt templates for defining backtests. It’s in the library. Trading strategies can be backtested using pre-built templates. You can learn more about it in its documentation.
More on Python
What can Python do for you? Some examples are:
- Machine learning and data analysis.
- Website creation
- Scripting or automation
- Prototyping and testing of software
- Routine tasks
Python isn’t just for coders and data scientists. Learning Python can open doors to new opportunities for those in less data-intensive professions such as journalists, small business owners, and social media marketers. Python can also help non-programmers simplify their daily lives.
The importance of Python as a financial tool can be gauged by the fact, that many businesses were able to detect market inefficiencies and flaws ahead of their competitors with the help of these finance modules.
These modules are ideal for forecasting and simplifying interpretations that would otherwise be complicated and time-consuming. Why not use computing technology when it is right at our fingertips? I hope you learned something new today.
Thanks for Reading!