TinyEarn 0.0.1

Simple selenium webscaper to pull earnings data from zacks.com

TinyEarn - Webscraper for Zacks.com

TinyEarn is an simple selenium-based webscaper to pull earnings data from zacks.com. It navigates to zacks.com/stock/research/{TICKER}/earnings-announcements and scrapes the earnings data from the present table and the table on the sales tab.

Requirements:

• Python3
• Firefox Browser

Packages:

• pandas>=0.24
• numpy>=1.15.4
• selenium>=3.3.0
• requests>=2.23
• beautifulsoup4>=4.9.0

The above packages should auto-install when you install TinyEarn. If you are downloading from github, you should be able to run the following code to install dependencies if you run into any issues. pip install -r requirements.txt

Get Started

Simply install the package using pip in your command line.

pip install TinyEarn

Usage

There is one public function in the TinyEarn() Class: get_earnings(). It stores no private variables so the same TinyEarn() class can be used across mutliple tickers.

get_earnings() - Scrapes zacks.com/stock/research/{TICKER}/earnings-announcements to get earnings data. NaN values are filled in for missing data. Dollar values and percentages are expressed as floating point decimals.

Parameters:

• ticker (str): The stock ticker for the company you'd like to pull data for.
• start (datetime.date or str): Only pull data from earnings reported after this date.
• end (datetime.date or str): Only pull data from earnings reported before this date. Defaults to the current date.
• pandas(bool, optional): If true, this function returns a pandas dataframe. If False, it returns a dictionary. Defaults to True.
• delay (int): Time to wait (in seconds) inbetween page changes. Defaults to 1.

Returns: Returns data from each earnings report by the specificied company within the specified date range. Each row or key represents an earnings call with the following attributes:

• Period Ending: The month that marks the last month of the quarter being reported on. ie, 3/2017 is refering to the Q1 2017 earnings report.
• Reported_EPS: Earnings Per Share reported by the company for thar quarter.
• Estimated_EPS: The consensus estimated Earnings Per Share.
• Surprise_EPS: The surprise in EPS. The difference between the estimated EPS and the reported one.
• Surprise_%_EPS: The surprise expressed as a percentage.
• Reported_Revenue: Total Revenue reported by the company for that quarter.
• Estimated_Revenue: The consensus estimated Revenue.
• Surprise_Revenue: The surprise in Revenue. The difference between the estimated Revenue and the reported one.
• Surprise_%_Revenue: The surprise expressed as a percentage.

Examples

A few examples of how this class can be used:

import TinyEarn as ty

scraper = ty.TinyEarn()
tsla = scraper.get_earnings('TSLA', start = '04/23/2017', pandas=True, delay=0) # Get earnings from April 23rd 2017 to today.
tsla[['Period Ending','Estimated_EPS','Reported_EPS','Surprise_EPS','Estimated_Revenue','Reported_Revenue','','']]

	Period Ending	Estimated_EPS	Reported_EPS	Surprise_EPS	Estimated_Revenue	Reported_Revenue
2020-04-29	2020-03-01	-0.22	1.24	1.46	5374.87	5985.00
2020-01-29	2019-12-01	1.62	2.14	0.52	7046.82	7384.00
2019-10-23	2019-09-01	-0.15	1.86	2.01	6517.00	6303.00
2019-07-24	2019-06-01	-0.54	-1.12	-0.58	6375.49	6349.68
2019-04-24	2019-03-01	-1.21	-2.90	-1.69	5778.73	4541.46
2019-01-30	2018-12-01	2.08	1.93	-0.15	7139.45	7225.87
2018-10-24	2018-09-01	-0.55	2.90	3.45	5666.67	6824.41
2018-08-01	2018-06-01	-2.78	-3.06	-0.28	3802.96	4002.23
2018-05-02	2018-03-01	-3.37	-3.35	0.02	3169.77	3408.75
2018-02-07	2017-12-01	-3.19	-3.04	0.15	3298.70	3288.25
2017-11-01	2017-09-01	-2.45	-2.92	-0.47	2916.96	2984.68
2017-08-02	2017-06-01	-1.94	-1.33	0.61	2548.22	2789.56
2017-05-03	2017-03-01	-0.55	-1.33	-0.78	2561.14	2696.27

tsla.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 13 entries, 2020-04-29 to 2017-05-03
Data columns (total 9 columns):
Period Ending         13 non-null datetime64[ns]
Estimated_EPS         13 non-null float64
Reported_EPS          13 non-null float64
Surprise_EPS          13 non-null float64
Surprise_%_EPS        13 non-null float64
Estimated_Revenue     13 non-null float64
Reported_Revenue      13 non-null float64
Surprise_Revenue      13 non-null float64
Surprise_%_Revenue    13 non-null float64
dtypes: datetime64[ns](1), float64(8)
memory usage: 1.0 KB

import TinyEarn as ty

scraper = ty.TinyEarn()
msft = scraper.get_earnings('MSFT', start = '01/01/2018',end='01/23/2019', delay=0)
msft[['Reported_EPS','Reported_Revenue']]

	Reported_EPS	Reported_Revenue
2018-10-24	1.14	29084.0
2018-07-19	1.13	30085.0
2018-04-26	0.95	26819.0
2018-01-31	0.96	28918.0

import TinyEarn as ty

scraper = ty.TinyEarn()
JPM = scraper.get_earnings('JPM', start = '04/23/2017', pandas=False, delay=0) #Testing Return as Dict
print(JPM)

{Timestamp('2020-04-14 00:00:00'): 
	{'Period Ending': Timestamp('2020-03-01 00:00:00'), 
	'Estimated_EPS': 1.7, 
	'Reported_EPS': 0.78, 
	'Surprise_EPS': -0.92, 
	'Surprise_%_EPS': -0.5412, 
	'Estimated_Revenue': 29152.0, 
	'Reported_Revenue': 28251.0, 
	'Surprise_Revenue': -901.0, 
	'Surprise_%_Revenue': -0.030899999999999997},
Timestamp('2020-01-14 00:00:00'): 
	{'Period Ending': Timestamp('2019-12-01 00:00:00'), 
	'Estimated_EPS': 2.32, 
	'Reported_EPS': 2.57, 
	'Surprise_EPS': 0.25, 
	'Surprise_%_EPS': 0.10779999999999999, 
	'Estimated_Revenue': 27261.0, 
	'Reported_Revenue': 28331.0, 
	'Surprise_Revenue': 1070.0, 
	'Surprise_%_Revenue': 0.0393}, 
Timestamp('2019-10-15 00:00:00'): 
	{'Period Ending': Timestamp('2019-09-01 00:00:00'), 
	'Estimated_EPS': 2.44, 
	'Reported_EPS': 2.68, 
	'Surprise_EPS': 0.24, 
	'Surprise_%_EPS': 0.0984, 
	'Estimated_Revenue': 28403.0, 
	'Reported_Revenue': 29341.0, 
	'Surprise_Revenue': 938.0, 
	'Surprise_%_Revenue': 0.033}, 
Timestamp('2019-07-16 00:00:00'): 
	{'Period Ending': Timestamp('2019-06-01 00:00:00'), 
	'Estimated_EPS': 2.5, 
	'Reported_EPS': 2.59, 
	'Surprise_EPS': 0.09, 
	'Surprise_%_EPS': 0.036000000000000004, 
	'Estimated_Revenue': 28416.5, 
	'Reported_Revenue': 28832.0, 
	'Surprise_Revenue': 415.5, 
	'Surprise_%_Revenue': 0.0146}}