Уроки Python Programming for Finance

Welcome to a Python for Finance tutorial series. In this series, we're going to run through the basics of importing financial (stock) data into Python using the Pandas framework. From here, we'll manipulate the data and attempt to come up with some sort of system for investing in companies, apply some machine learning, even some deep learning, and then learn how to back-test a strategy. I assume you know the fundamentals of Python. If you're not sure if that's you, click the fundamentals link, look at some of the topics in the series, and make a judgement call. If at any point you are stuck in this series or confused on a topic or concept, feel free to ask for help and I will do my best to help.

• Intro and Getting Stock Price Data Handling Data and Graphing Basic Stock data Manipulation More stock manipulations Automating getting the S&
• P 500 list Getting all company pricing data in the S&
• P 500 Combining S&
• P 500 into one DataFrame S&
• P 500 company correlation table Preprocessing data for Machine Learning Creating machine learning target function Creating labels for Machine Learning Machine learning Algorithmic Trading with Python and Quantopian p. 1