Have you ever thought of investing in stocks but don't know where to start, or you probably have consulted a stock broker but still not convinced. You just so curious you really wanna know where to put your money, you so sure you really wanna know the skeletons of the stock market plaforms of interest to you....cool!.
You want every infomation about each company on the S&P 500 list, you also want to pull financial data from wiki, SEC website and the rest but don't know how to go about, never mind because this project will guide you through.
The question is Can we use machine learning to analyze public company (stocks) fundamentals (things like price/book ratio, P/E ratio, Debt/Equity ... etc), and then classify the stocks as either out-performers compared to the market (labeled as 1's), or under-performers (labeled as 0's).
https://pythonprogramming.net/static/downloads/machine-learning-data/intraQuarter.zip..... download link to the data, This data is straight HTML source code for the S&P 500 index of companies over a decade from Yahoo Finance, the data also need some parsing, coutesy pythonprogramming.net
Each file with it's description and every indented block of code has is commented out for illustration purpose
The csv file named "YAHOO-INDEX_GSPC" is the yahoo index S & P 500 data over a long period of time that will be compared with the file above after parsing and extracting features.
Gathered from the book - python machine learning_chap_2 - code examples by Sebastian Raschka
Five Case Studies for the Data Scientist — Danish Haroon