GET FREE FINANCIAL DATA W/ PYTHON (EARNINGS ESTIMATES-FROM YAHOO FINANCE)

Today I present a simple function to extract Earnings Estimates from Yahoo Finance. If you have any questions feel free to leave it in the comments. 

This code uses Python 3 on Windows 8.1 but could be easily adapted for Python 2 by changing the 'urllib' import. 

First we import the necessary packages into our programming environment. 

import pandas as pd
import urllib as u
from bs4 import BeautifulSoup as bs
import warnings
warnings.filterwarnings("ignore")

I also suppress warnings for the deprecation warning for the Pandas "Dataframe.convert_objects()"method within the scraper function that follows. 

This function takes the Yahoo Finance URL with our symbol of interest and uses BeautifulSoup to parse the resulting HTML. I also added some formatting code to clean up the readability of the headers. 

def _get_eps_estimates(url):
    try:
        html_source = u.request.urlopen(url).read()
        soup = bs(html_source, 'lxml')
        # 
        # table
        table = soup.find_all('table', attrs={'class': 'yfnc_tableout1'})
        header = [th.text for th in table[0].find_all(class_='yfnc_tablehead1')]
        header_title = header[0]
        header_cols = header[1:5]
        index_row_labels = header[-5:]
        body = [[td.text for td in row.select('td')] for row in table[0].find_all('tr')]
        body = body[1:]
        df = pd.DataFrame.from_records(body)
        df = df.ix[:, 1:]
        df.index = index_row_labels
        header_cols = pd.Series(header_cols)
        header_cols = header_cols.str.replace(
            'Year', 'Year ').str.replace('Qtr.', 'Qtr. ')
        df.columns = header_cols
        eps_est = df.convert_objects(convert_numeric=True)
    except Exception as e:
        print(e)
    return eps_est

Now let's test the function using the proper URL. I'm using the symbol 'SWKS' in this example.

symbol = 'SWKS'

base_url = r'http://finance.yahoo.com/q/ae?s={}+Analyst+Estimates'.format(symbol)
eps_est = _get_eps_estimates(base_url)
eps_est

Your output should appear like the following:

View fullsize

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.