Six of The Best Python Libraries for finance

Six of The Best Python Libraries for Finance

In this section, We discuss six of the Six Best Financial Libraries. There are the popular libraries Numpy, Scipy, Matplotlib, Scikit Learning, Pandas and Quant lab. In this case, we discuss this library on how it can be used in finance.

Python is a popular language for finance. But there is not much to do with the core language. To help you out, 50 modules inbuilt into the language. For example, if you want to calculate the discount curve, you need exponential and logarithmic functions, which can be built into the 'Math' module in Python. 

However, this is less than what financial analysis requires for data analysis, banking, option pricing or machine learning. Serious work requires you to download thousands of open-source third-party libraries available for Python. List of some of the most popular Python libraries.



1. NumPy


NumPy is the starting point for financial Pythonistas, and you will struggle to have Python installation. Kdnugget says it will be the 7th most popular library in 2018. The NumPy library allows you to convert arrays and matrices, as well as to use random number generating functions, which requires some optimization techniques such as boosting and bagging. Most of the main code, in particular, is written in C, which causes a relative slowdown of Python.






2. SciPy


SciPy builds on the basic functions that NumPy provides.

It has a broad range of important functions and financial data to cover technologies such as Linear Algebra, Signature Processing, Statistics, Interpolation, and Optimization.



3. Matplotlib


Once you have your data, you may want to look it up. This is where Matplotlib comes in. This is a visualization module from which you can plot an accessible graph or chart in 2 or 3 dimensions. Be careful - it takes a while to use Matplotlib's API. 

The interface deliberately mimics the Matlab functionality, which is very annoying for native Python programmers. There are actually two APIs to fix this Matplotlib, but this is misleading. However, it is worth learning how to use this powerful package.




4. Pandas

Pandas is built on SciPy and NumPy and is a widely used library for data processing and analysis. Ideally, to convert time series data, it is therefore absolutely necessary to analyze price movements in financial markets. Of course, before the release of Pandas as an open-source project, the main reserve Fund. Fund was created by developers within the AQR.

Numpy, Scipy, Matplotlib and Pandas are part of the 'Numpy / Scipy Stack'. Due to multiple dependencies, installing this stack can be a bit wasteful. One solution to this is Anaconda Distribution, which installs these packages (and more), and allows you to use them in a virtual environment that includes a popular Jupiter notebook that allows you to use Python in your web browser.



However, Anaconda installs more than 200 libraries, so it is not easy. Using a Python-based system for business or risk management on a cloud computer, or an expensive business cluster. One option is a more minimalist miniconda '.



5. Scikit learning

Another module that comes with Anaconda's distribution is Scikit-Learn, Python's most popular machine learning package for 2008. Performs standard machine learning techniques, including classification and clustering.



6. QuantLib


A large number of multi-millionaire employees are still working on construction and testing. Fortunately, the Python-wrapped version of the Quantlib C ++ library is the most widely used type in assessing and determining financial risk.

It is much faster because C ++ is at its center. Because Quantlib is not a native Python library and no Python documentation, it has a learning curve to work with. The Python pure derivative library, however, was too slow in demanding heavier values.