Python For Finance

Posted : admin On 1/26/2022
Intro and Getting Stock Price Data - Python Programming for Finance p.1
Handling Data and Graphing - Python Programming for Finance p.2

In this introductory chapter, we will explore the aspects of Python in order to judge its suitability as a programming language in finance. Notably, Python is widely practiced in various financial sectors, such as banking, investment management, insurance, and even in real estate for building tools that help in financial modeling, risk management, and trading. Why is Python a great programming language for finance professionals to learn? Python is a high-level programming language, meaning that it abstracts away and handles many of the technical aspects of programming, such as memory management, that must be explicitly handled in other languages. Take part in our online Python for Finance course to rapidly develop programming skills for financial calculations and data analysis. Gain a comprehensive understanding of the programming concepts, as well as extensive practical experience in some of the core data analysis libraries used in Python, with a particular focus on PyCharm. Python is rapidly gaining traction in the quant finance world. Many of the top quant forums contain more and more questions every day about how Python can be used in quantitative finance. This article will present a list of textbooks that are suitable for learning Python from the ground up to an intermediate level. Python for Finance Investments Fundamentals by Udemy This is one of the best sellers and popular courses in Finance professionals. It is a well-constructed course for all who want to learn the programming using Python and conduct the financial analysis using Python.

Basic stock data Manipulation - Python Programming for Finance p.3
Finance
More stock manipulations - Python Programming for Finance p.4
Automating getting the S&P 500 list - Python Programming for Finance p.5
Getting all company pricing data in the S&P 500 - Python Programming for Finance p.6
Combining all S&P 500 company prices into one DataFrame - Python Programming for Finance p.7
Creating massive S&P 500 company correlation table for Relationships - Python Programming for Finance p.8
Preprocessing data to prepare for Machine Learning with stock data - Python Programming for Finance p.9
Creating targets for machine learning labels - Python Programming for Finance p.10 and 11
Machine learning against S&P 500 company prices - Python Programming for Finance p.12
Testing trading strategies with Quantopian Introduction - Python Programming for Finance p.13
Placing a trade order with Quantopian - Python Programming for Finance p.14
Scheduling a function on Quantopian - Python Programming for Finance p.15
Quantopian Research Introduction - Python Programming for Finance p.16
Quantopian Pipeline - Python Programming for Finance p.17
Alphalens on Quantopian - Python Programming for Finance p.18
Back testing our Alpha Factor on Quantopian - Python Programming for Finance p.19
Analyzing Quantopian strategy back test results with Pyfolio - Python Programming for Finance p.20
Strategizing - Python Programming for Finance p.21
Finding more Alpha Factors - Python Programming for Finance p.22
Combining Alpha Factors - Python Programming for Finance p.23
Portfolio Optimization - Python Programming for Finance p.24
Python
Zipline Local Installation for backtesting - Python Programming for Finance p.25

Python For Finance Udemy

Zipline backtest visualization - Python Programming for Finance p.26
Custom Data with Zipline Local - Python Programming for Finance p.27

Python For Finance Book

Custom Markets Trading Calendar with Zipline (Bitcoin/cryptocurrency example) - Python Programming for Finance p.28