The statsmodels package builds on these packages by implementing more advanced testing of different statistical models. SciPy provides a library of statistical tools that allow users to construct a model, and pandas makes it easy to implement. To perform a simple filtering operation, extracting the row that meets the logical condition:įurther examples can be found in the documentation here. In addition, the package provides functions for importing data from a variety of standard formats, and others for rapid plotting, retrieving basic statistics, or outputting data.ĭf_1 = pd.DataFrame() The most common manipulations, such as groupby, joining, merging, or filling, replacing and imputing null values, can be executed in a single line. It is based on the arrays that NumPy introduces, and is optimized for tabular, multidimensional, and heterogeneous data. The panda’s package, on the other hand, establishes an intuitive and easy-to-use data structure, a DataFrame, specifically designed for analysis and model building. NumPy and SciPy lay the mathematical groundwork. To demonstrate interpolation, I first use NumPy to create some data points with an arbitrary function, then compare different interpolation methods:į_nearest = interp1d(x, y, kind='nearest') These operations are essential when performing any type of data analysis, or building any type of predictive model. These include algorithms for interpolation, optimization, clustering, transformation, and integration of data. This package provides functions and algorithms critical to the advanced scientific computations needed to build any statistical model.
But in order to build sophisticated models based on this data, a repository of more advanced statistical tools and operations is needed. The NumPy package provides basic mathematical structures for manipulating and storing data. More information about how NumPy is used can be found here. Most of the higher-level Python packages for finance mentioned later in this list depend on NumPy.įor example, to create two 2×2 complex matrices and print the sum:Īnd to take the complex conjugate of one of them: Not only does it introduce n-dimensional arrays and matrices into Python, but also contains some basic mathematical functions to manipulate these data structures. NumPy is the most essential package for scientific and mathematical computing in Python. The first few packages I have in the list provide the framework to do so. I sample a few of the best packages for accomplishing each task.Īt base, all financial models rely on crunching numbers.
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This article focuses on applications specific to quantitative finance, which require programming tasks such as data importation and transformation, time series and risk analysis, trading and backtesting, excel integration, and data visualization. The field of financial technologies is vast, encompassing everything from insurance, lending and trading, to e-banking and other payment services. The Most Useful Python Packages for Finance
All of these packages (except quantlib) are available on the ActiveState Platform for inclusion in your runtime environment. In this article, I’ll highlight my top 10 packages for finance and financial modeling with a few basic examples. From crunching the raw numbers to creating aesthetically pleasing, yet intuitive Graphical User Interfaces (GUIs), a myriad of packages exist to help users build their own financial models. For more specific applications, the Python Package Index (PyPI) provides additional packages that extend the capabilities of Python to fit the needs of each domain.įor these reasons, Python has proven to be a formidable tool in developing novel financial technologies. This versatility is enabled by the extensive standard library that offers a range of facilities intended to enhance the functionality and portability of the language. In addition to the vast number of use cases in web and app development, Python provides the tools for building and implementing any type of scientific or mathematical model, regardless of the origin or type of data. The popularity of the Python programming language is due, at least in part, to the versatility that it offers.
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