reference with https://www.linkedin.com/pulse/best-python-librariespackages-finance-financial-data-majid-aliakbar/ for useful tools
numpy – NumPy is the fundamental package for scientific computing with Python. It is a first-rate library for numerical programming and is widely used in academia, finance, and industry. NumPy specializes in basic array operations.
scipy – SciPy supplements the popular Numeric module, Numpy. It is a Python-based ecosystem of open-source software for mathematics, science, and engineering. It is also used intensively for scientific and financial computation based on Python.
pandas – The pandas library provides high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Pandas focus is on the fundamental data types and their methods, leaving other packages to add more sophisticated statistical functionality
statistics – This is a built-in Python library for all basic statistical calculations https://www.linkedin.com/pulse/best-python-librariespackages-finance-financial-data-majid-aliakbar/
zipline – Zipline is a Pythonic algorithmic trading library. It is an event-driven system that supports both backtesting and live trading. http://www.zipline.io/?lipi=urn%3Ali%3Apage%3Ad_flagship3_pulse_read%3BnqFGIBCcT7qTXEY6GReXag%3D%3D
pyfolio – pyfolio is a Python library for performance and risk analysis of financial portfolios. It works well with the Zipline open source backtesting library. https://github.com/quantopian/pyfolio?lipi=urn%3Ali%3Apage%3Ad_flagship3_pulse_read%3BnqFGIBCcT7qTXEY6GReXag%3D%3D
empyrical – Common financial risk and performance metrics. Used by zipline and pyfolio. https://github.com/quantopian/empyrical?lipi=urn%3Ali%3Apage%3Ad_flagship3_pulse_read%3BnqFGIBCcT7qTXEY6GReXag%3D%3D