Using the Jupyter notebook it is easy to experiment with the virtual_finance_api library.
# Setup a virtual environment
$ mkdir vfa
$ cd vfa
$ python3 -m venv venv38
$ . ./venv38/bin/activate
(venv38) feite@salmay:~/vfa$
$ pip install virtual_finance_api$ pip install git+https://github.com/hootnot/virtual-finance-API
With virtual_finance_api installed, it is directly available via the commandline:
$ virtual_finance_api --help
Usage: vfapi [OPTIONS] COMMAND [ARGS]...
Virtual Finance API commandline app.
Options:
--help Show this message and exit.
Commands:
financials
history
holders
profile Profile.... additional help on the history command:
vfapi history --help
Usage: vfapi history [OPTIONS] TICKER
Options:
--period [1d|5d|1mo|3mo|6mo|1y|2y|5y|10y|ytd|max]
--interval [1m|2m|5m|15m|30m|60m|90m|1h|1d|5d|1wk|1mo|3mo]
--csv
--help Show this message and exit.So, lets query for some history for IBM ...
$ vfapi history IBM
Open High Low Close Volume
2021-03-01 14:30:00 120.349998 122.320000 119.860001 120.739998 5714500
2021-03-02 14:30:00 120.739998 121.900002 120.260002 120.330002 4522200
2021-03-03 14:30:00 120.500000 122.629997 119.980003 122.360001 7396200
2021-03-04 14:30:00 122.000000 123.220001 118.760002 120.110001 8062100
2021-03-05 14:30:00 120.639999 123.750000 120.250000 122.830002 6944900
2021-03-08 14:30:00 122.989998 126.849998 122.879997 124.809998 7236600
2021-03-09 14:30:00 125.400002 126.430000 124.160004 124.180000 5608200
2021-03-10 14:30:00 125.050003 128.240005 124.610001 127.870003 7243500
2021-03-11 14:30:00 128.089996 128.639999 126.779999 127.139999 5145000
2021-03-12 14:30:00 127.190002 127.680000 126.610001 127.610001 4009600
2021-03-15 13:30:00 127.769997 128.750000 127.540001 128.580002 3420600
2021-03-16 13:30:00 128.279999 128.520004 127.339996 128.240005 4630400
2021-03-17 13:30:00 128.460007 129.490005 127.489998 129.029999 4244800
2021-03-18 13:30:00 128.940002 131.000000 127.790001 130.059998 5834600
2021-03-19 13:30:00 130.020004 130.440002 128.529999 128.899994 9830600
2021-03-22 13:30:00 128.500000 130.720001 127.889999 130.550003 4164900
2021-03-23 13:30:00 130.440002 131.559998 129.800003 130.460007 4356400
2021-03-24 13:30:00 130.949997 132.110001 130.570007 130.619995 4005000
2021-03-25 13:30:00 130.330002 133.240005 129.770004 133.070007 5554000
2021-03-26 13:30:00 133.289993 136.479996 133.119995 136.380005 5562500
2021-03-29 13:30:00 135.979996 137.070007 135.509995 135.860001 4620900The Virtual Finance API provides access to data from financial sites as if it is accessing a REST-API. Currently covered:
- yahoo 'endpoints' to get:
- financials
- history
- holders
- options
- profile
- screener
- screeners
- yahooindex
- business inisder 'endpoint':
- fetch ISIN code
- yfinance compatibility 'endpoints'
- standardized JSON 'endpoints'
With request-classes for these endpoints, getting data is as easy as:
>>> import json
>>> import virtual_finance_api as fa
>>> import virtual_finance_api.endpoints.yahoo as yh
>>> client = fa.Client()
>>> r = yh.Holders('IBM')
>>> rv = client.request(r)
# lets get the 'major' holders from that JSON response
>>> print(json.dumps(rv['major'], indent=2))
{
"0": {
"0": "0.13%",
"1": "58.58%",
"2": "58.66%",
"3": "2561"
},
"1": {
"0": "% of Shares Held by All Insider",
"1": "% of Shares Held by Institutions",
"2": "% of Float Held by Institutions",
"3": "Number of Institutions Holding Shares"
}
}With the extensions.stdjson endpoints this looks like:
>>> import virtual_finance_api.extensions.stdjson.endpoints as je
>>> client = fa.Client()
>>> r = je.Holders('IBM')
>>> rv = client.request(r)
# lets get the 'major' holders from that JSON response
>>> print(json.dumps(rv['major'], indent=2))
[
[
"0.13%",
"% of Shares Held by All Insider"
],
[
"58.25%",
"% of Shares Held by Institutions"
],
[
"58.33%",
"% of Float Held by Institutions"
],
[
"2696",
"Number of Institutions Holding Shares"
]
]It make more sense to group the information. The base classes simply pass the JSON data the way it is scraped from the source URL. Derived classes can be used to transform this data. The extension.stdjson performs this task.
There is a compatibility layer with Yfinance too. It provides requests derived from the base requests, extended with properties that give the same information as Yfinance does.
The Holders-example from above becomes:
>>> import json
>>> import virtual_finance_api as fa
>>> import virtual_finance_api.compat.yfinance.endpoints as yf
>>> client = fa.Client()
>>> r = yf.Holders('IBM')
>>> rv = client.request(r)
>>> # lets get the 'major' holders from that JSON response
>>> print(r.major)
0 1
0 0.13% % of Shares Held by All Insider
1 58.58% % of Shares Held by Institutions
2 58.66% % of Float Held by Institutions
3 2561 Number of Institutions Holding Shares
>>> # or, that same information from the dataframe in JSON
>>> # (dump, load, dump to 'pretty print')
>>> print(json.dumps(json.loads(r.major.to_json()), indent=2))
{
"0": {
"0": "0.13%",
"1": "58.58%",
"2": "58.66%",
"3": "2561"
},
"1": {
"0": "% of Shares Held by All Insider",
"1": "% of Shares Held by Institutions",
"2": "% of Float Held by Institutions",
"3": "Number of Institutions Holding Shares"
}
}
>>> print(r.institutional)
Holder Shares Date Reported % Out Value
0 Vanguard Group, Inc. (The) 73806391 2020-12-30 0.0826 9290748499
1 Blackrock Inc. 62271273 2020-12-30 0.0697 7838707845
2 State Street Corporation 51941856 2020-12-30 0.0581 6538440833
3 Geode Capital Management, LLC 13310817 2020-12-30 0.0149 1675565643
4 Charles Schwab Investment Management, Inc. 12571878 2020-12-30 0.0141 1582548002
5 Northern Trust Corporation 10652880 2020-12-30 0.0119 1340984534
6 Morgan Stanley 9853901 2020-12-30 0.0110 1240409057
7 Bank Of New York Mellon Corporation 9628160 2020-12-30 0.0108 1211992780
8 Norges Bank Investment Management 8865649 2020-12-30 0.0099 1116007896
9 Bank of America Corporation 8074146 2020-12-30 0.0090 1016373498See the https://virtual-finance-api.readthedocs.io/en/latest/?badge=latest for details.
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.