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Finance professionals often have to be involved in a wide variety of functions which involve data analytics and have to make use of programming languages in order to analyze various financial data sets. Python is a preferred mode because of the vast array of libraries that Python has to offer for almost every task. The simplicity of Python combined with its array of libraries for various purposes makes it a preferred language for data scientists. The following are some of the Python libraries which are beneficial for financial instruments-

Volib

Volib is a python library which can be employed to calculate option prices as well as the implied volatility in the market. In order to perform these tasks, volib uses analytical and numerical greeks which are used to obtain the pricing formula.

QuantPy

QuantPy is a library which is often used for performing tasks in quantitative finance. It can also perform functions of a nature which imports daily returns from Yahoo. Another important function that it can perform is that it can calculate optimal weights for Sharpe ratio.

Ffn

Like QuantPy, ffn is a library which can be of great assistance to those who work in the field of quantitative finance. The library is built by incorporating various functions of famous libraries such as Pandas, NumPy and SciPy. It provides for an array of functions such as graphing and data transformations.

Pynance

Pynance is an open source software which works in a manner that collects and analyses data from the stock and derivative markets and presents them in a visual form. It consists of tools which can be used to generate machine learning algorithms which can be used for stock market analysis.

Tia

Tia is a library which is specifically designed for ancillary tasks associated with the usage of programming in financial instruments such as access to Bloomberg data, technical analysis functionality and return analysis among others.

There are various other such Python libraries which are used in finance such as pyfin. In addition to this, various numerical and statistical libraries such as NumPy and SciPy are also used for programming software which performs an array of financial analysis.