A library to download data from EOD historical data https://eodhistoricaldata.com/ using:
$ pip install git+https://github.com/femtotrader/python-eodhistoricaldata.gitor
$ git clone https://github.com/femtotrader/python-eodhistoricaldata.git
$ python setup.py installEnvironment variable EOD_HISTORICAL_API_KEY should be defined using:
export EOD_HISTORICAL_API_KEY="YOUR_API"You can download data simply using
In [1]: import pandas as pd
In [2]: pd.set_option("max_rows", 10)
In [3]: from eod_historical_data import get_eod_data
In [4]: df = get_eod_data("AAPL", "US")
In [5]: df
Out[1]:
Open High Low Close Adjusted_close \
Date
2000-01-03 3.7455 4.0179 3.6317 3.9978 3.9978
2000-01-04 3.8661 3.9509 3.6138 3.6607 3.6607
2000-01-05 3.7054 3.9487 3.6786 3.7143 3.7143
2000-01-06 3.7902 3.8214 3.3929 3.3929 3.3929
2000-01-07 3.4464 3.6071 3.4107 3.5536 3.5536
... ... ... ... ... ...
2017-05-26 154.0000 154.2400 153.3100 153.6100 153.6100
2017-05-30 153.4200 154.4300 153.3300 153.6700 153.6700
2017-05-31 153.9700 154.1700 152.3800 152.7600 152.7600
2017-06-01 153.1700 153.3300 152.2200 153.1800 153.1800
2017-06-02 153.6000 155.4500 152.8900 155.4500 155.4500
Volume
Date
2000-01-03 133949200.0
2000-01-04 128094400.0
2000-01-05 194580400.0
2000-01-06 191993200.0
2000-01-07 115183600.0
... ...
2017-05-26 21927600.0
2017-05-30 20126900.0
2017-05-31 24451200.0
2017-06-01 16274200.0
2017-06-02 25163841.0
[4382 rows x 6 columns]but if you want to avoid too much data consumption, you can use a cache mechanism.
In [1]: import datetime
In [2]: import requests_cache
In [3]: expire_after = datetime.timedelta(days=1)
In [4]: session = requests_cache.CachedSession(cache_name='cache', backend='sqlite', expire_after=expire_after)
In [5]: df = get_eod_data("AAPL", "US", session=session)See tests directory for example of other API endpoints.
- Idea to create this project came from this issue (Thanks @deios0 )
- Code was inspired by pandas-datareader
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