Get Free Financial Data w/ Python (Fundamental Ratios-From

A simple script to scrape fundamental ratios from 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 and get the base url for the quote page.

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 ):
    	url = r'{}'\
        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:
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.

How to get Free Intraday Stock Data from Netfonds

Daily stock data is everywhere for free. Yahoo, Google, and Quandl all provide useful daily stock prices for basic number crunching. However computational analysis for intraday stock data is much harder to find. In fact, Intraday stock data can be very expensive. So what is a cost conscious quant supposed to do?

The Norwegian website provides free intraday data on stocks and ETF's on the NYSE, Nasdaq, and Amex exchanges. They provide up to 5 days of trade/bid/offer data. I wrote some code to grab that data easily and quickly that I will share with the Python community.

Before I paste the code below let me give a h/t to Yves Hilpisch who wrote the excellent 'Python for Finance' book where I borrowed some of the following code. 

I've embedded the entire script using Nike ( NKE ). Feel free to ask questions and/or add your own touches. 


Here are the resulting sample plots.