Synthetic ETF Data Generation (Part-2) - Gaussian Mixture Models

Synthetic ETF Data Generation (Part-2) - Gaussian Mixture Models

This post is a summary of a more detailed Jupyter (IPython) notebook where I demonstrate a method of using Python, Scikit-Learn and Gaussian Mixture Models to generate realistic looking return series. In this post we will compare real ETF returns versus synthetic realizations.

Read More

Get Free Financial Data w/ Python (Fundamental Ratios-From Finviz.com)

A simple script to scrape fundamental ratios from Finviz.com. This basic code can be tailored to suit your application.


"""IPython 3.1, Python 3.4, Windows 8.1"""

import pandas as pd
import urllib as u
from bs4 import BeautifulSoup as bs

"""
First visit www.Finviz.com and get the base url for the quote page.
example: http://finviz.com/quote.ashx?t=aapl

Then write a simple function to retrieve the desired ratio. 
In this example I'm grabbing Price-to-Book (mrq) ratio
"""

def get_price2book( symbol ):
	try:
    	url = r'http://finviz.com/quote.ashx?t={}'\
        				.format(symbol.lower())
        html = u.request.urlopen(url).read()
        soup = bs(html, 'lxml')
        # Change the text below to get a diff metric
        pb =  soup.find(text = r'P/B')
        pb_ = pb.find_next(class_='snapshot-td2').text
        print( '{} price to book = {}'.format(symbol, pb_) )
        return pb_
    except Exception as e:
        print(e)
        
"""
Construct a pandas series whose index is the list/array
of stock symbols of interest.

Run a loop assigning the function output to the series
"""
stock_list = ['XOM','AMZN','AAPL','SWKS']
p2b_series = pd.Series( index=stock_list )

for sym in stock_list:
	p2b_series[sym] = get_price2book(sym)

The function should produce the following:


XOM price to book = 1.89
AMZN price to book = 20.74
AAPL price to book = 5.27
SWKS price to book = 5.52

Very simple adaptable code, allowing you to spend more time analyzing the data and less time aggregating it.