Description

After working with data from eodhistoricaldata.com for a while I made this to simplify fetching and aggregating from their different endpoints and returning it in a consistent format.

Usage

Set up

• Have python and pandas installed (I'm using Python 3.8.10 and pandas 1.3.1)
• Download the EOD_api.py file to your working directory

Market data - daily and intraday

You can download data for different stocks across different exchanges by just providing a list of tickers

import EOD_api as eod
import os
token = os.environ['EOD_TOKEN'] # Place your token here, i.e. token = '12ab3cd5efgh45.12345678'
eod.ohlcv( ['AAPL.US','BP.LSE'], token, '2020-10-13', '2020-10-15').retrieve_data()

eod.ohlcv_intraday( ['AAPL.US'], token, '2020-10-13', '2020-10-14',intraday_frec='5m').retrieve_data()

Fundamental data

The Income Statement, Balance Sheet, and Cash Flow data all in one pandas dataframe

eod.fundamental( ['AAPL.US','MSFT.US'], token, '2020-04-01', '2020-08-01').retrieve_data()

Extra

Some extra functions and methods to make data exploration easier from the start

Show

Get the list of available exchanges:

eod.get_exchange_list( token )

Get info on all the available tickers from a exchange:

eod.get_all_tickers_exchange('US',token) 

Find tickers by marketcap:

eod.stock_screener( 7, token, 'US', initial_offset = 0, mincap = None, maxcap = None)['code']

Some methods to make it easier to add or remove data incrementally instead of downloading all the data from the data provider at every step during data exploration:

data = eod.ohlcv( ['AAPL.US','BP.LSE'], token, '2020-10-13', '2020-10-15')
data.retrieve_data()

data.add_tickers(['MSFT.US']).retrieve_data()

data.truncate_dates('2020-10-13','2020-10-14').retrieve_data()

data.remove_tickers(['MSFT.US','BP.LSE']).retrieve_data()

Syntax

Show

Class ohlcv( tickers, token, start, end):

Downloads ohlcv data to a pandas dataframe

Arguments

• tickers : List of tickers to download
• token : String with your key from the data provider
• start : String with the initial date from which to download data
• end : String with the final date from which to download data

Methods

• retrieve_data() : Return the dataframe with the data
• add_tickers( tickers) : Download more tickers to the dataframe
  • tickers: List of tickers to download
• remove_tickers( tickers) : Remove tickers
  • tickers: List of tickers to remove
• truncate_dates( start, end) : Remove dates
  • start: String with the new initial date
  • end : String with the new final date

Class ohlcv_intraday( tickers, token, start, end, intraday_frec='5m'):

Downloads intraday data to a pandas dataframe

Arguments (same as ohlcv)

• intraday_frec: Frecuency of intraday data. '5m' and '1m'. Check the data provider for availability of data.

Methods (Same as ohlcv)

Class fundamental( tickers, token, start, end):

Downloads fundamental data to a pandas dataframe. The Income Statement, Balance Sheet, and Cash Flow data is aggregated into one dataframe. If any columns have duplicated names only one is kept.

Arguments (same as ohlcv)

Methods (Same as ohlcv)

Function get_exchange_list( token):

Returns a list of the available exchanges and some more info

Function get_all_tickers_exchange( exchange, token):

Returns a list of the available tickers for a exchange and some more info

Function stock_screener( n_stocks, token, exchange, initial_offset = 0, mincap = None, maxcap = None):

Finds first n stocks in exchange by marketcap

Arguments

• n_stocks: Int. Number of stocks to find
• exchange: String. Chosen exchange
• initial_offset : Number of stocks to skip
• mincap :Minimum market capitalization
• maxcap :Maximum market capitalization

More types of data

I don't plan to maintain this.
However, I created a EODdata class inside the EOD_api.py that you can subclass to add more types of data. This way you might download all of your data from eodhistoricaldata.com in a streamlined way. You can check all of the data types they offer in their website. For example you could include options, bonds, live data, financial news, insider trading, etc.
If you found any of this useful I would appreciate feedback.