Some current capabilities: Portfolio class that can import daily returns from Yahoo, Calculation of optimal weights for Sharpe ratio and efficient frontier, and event profiler
vollib - vollib is a python library for calculating option prices, implied volatility and greeks using Black, Black-Scholes, and Black-Scholes-Merton.pyfin - Pyfin is a python library for performing basic options pricing in python.statistics - This is a built-in Python library for all basic statistical calculations.Quant DSL is a functional programming language for modeling derivative instruments. quantdsl - Quand DSL is domain specific language for quantitative analytics in finance and trading.Pandas focus is on the fundamental data types and their methods, leaving other packages to add more sophisticated statistical functionality pandas - The pandas library provides high-performance, easy-to-use data structures and data analysis tools for the Python programming language.It is also used intensively for scientific and financial computation based on Python It is a Python-based ecosystem of open-source software for mathematics, science, and engineering.
scipy - SciPy supplements the popular Numeric module, Numpy.NumPy specializes in basic array operations. It is a first-rate library for numerical programming and is widely used in academia, finance, and industry. numpy - NumPy is the fundamental package for scientific computing with Python.This article provides a list of the best python packages and libraries used by finance professionals, quants, and financial data scientists. If you search on Github, a popular code hosting platform, you will see that there is a python package to do almost anything you want. Python also has a very active community which doesn’t shy from contributing to the growth of python libraries. Python has been gathering a lot of interest and is becoming a language of choice for data analysis. Finance professionals involved in data analytics and data science make use of R, Python and other programming languages to perform analysis on a variety of data sets.
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