alpha\_vantage
==============
|Build Status| |PyPI version| |Documentation Status|
*Python module to get stock data from the Alpha Vantage API*
Alpha Vantage delivers a free API for real time financial data and most
used finance indicators in a simple json format. This module implements
a python interface to the free API provided by Alpha Vantage
(http://www.alphavantage.co/). It requires a free API, that can be
requested on http://www.alphavantage.co/support/#api-key.
Install
-------
To install the package use:
.. code:: shell
pip install alpha_vantage
If you want to install from source, then use:
.. code:: shell
git clone https://github.com/RomelTorres/alpha_vantage.git
pip install -e alpha_vantage
Usage
-----
To get data in a python, simply import the library and call the object
with your api key and get ready for some awesome free realtime finance
data.
.. code:: python
from alpha_vantage.timeseries import TimesSeries
ts = TimesSeries(key='YOUR_API_KEY')
# Get json object with the intraday data and another with the call's metadata
data, meta_data = ts.get_intraday('GOOGL')
Internally there is a retries counter, that can be used to minimize
connection errors (in case that the api is not able to respond in time),
the default is set to 5 but can be increased or decreased whenever
needed.
.. code:: python
ts = TimesSeries(key='YOUR_API_KEY',retries='YOUR_RETRIES')
Finally the library supports giving its results as json dictionaries
(default) or as pandas dataframe, simply pass the parameter
output\_format='pandas' to change the format of the output for all the
api calls.
.. code:: python
ts = TimesSeries(key='YOUR_API_KEY',output_format='pandas')
Plotting
--------
Using pandas support we can plot the intra-minute value for 'MSFT' stock
quite easily:
.. code:: python
from alpha_vantage.timeseries import TimesSeries
import matplotlib.pyplot as plt
ts = TimesSeries(key='YOUR_API_KEY', output_format='pandas')
data, meta_data = ts.get_intraday(symbol='MSFT',interval='1min', outputsize='full')
data['close'].plot()
plt.title('Intraday Times Series for the MSFT stock (1 min)')
plt.show()
Giving us as output: |alt text|
The same way we can get pandas to plot technical indicators like
Bolliger Bands®
.. code:: python
from alpha_vantage.techindicators import TechIndicators
import matplotlib.pyplot as plt
ti = TechIndicators(key='YOUR_API_KEY', output_format='pandas')
data, meta_data = ti.get_bbands(symbol='MSFT', interval='60min', time_period=60)
data.plot()
plt.title('BBbands indicator for MSFT stock (60 min)')
plt.show()
Giving us as output: |alt text|
Finally we can also plot sector performance just as easy:
.. code:: python
from alpha_vantage.sectorperformance import SectorPerformances
import matplotlib.pyplot as plt
sp = SectorPerformances(key='YOUR_API_KEY', output_format='pandas')
data, meta_data = sp.get_sector()
data['Rank A: Real-Time Performance'].plot(kind='bar')
plt.title('Real Time Performance (%) per Sector')
plt.tight_layout()
plt.grid()
plt.show()
Giving us as output: |alt text|
Tests
-----
In order to run the tests you have to first export your API key so that
the test can use it to run.
.. code:: shell
export API_KEY=YOUR_API_KEY
cd alpha_vantage
nosetests
Documentation
-------------
To find out more about the available api calls, visit the alpha-vantage
documentation at http://www.alphavantage.co/documentation/
Coming soon:
------------
1. [STRIKEOUT:Add basic functionality: 0.0.1]
2. [STRIKEOUT:Add retry in order to allow the calls to be retried in
case of failure: 0.0.2]
3. [STRIKEOUT:Implement all functions described in the alpha vantage
documentation 0.0.3]
4. [STRIKEOUT:Re-factor functions to have an unified method for
accessing the api 0.1.0]
5. [STRIKEOUT:Add pandas support through decorators 0.1.2]
6. [STRIKEOUT:Publish on pipy 0.1.3]
7. Add logging to tests to store api call duration 1.0.1
8. Add unit tests for all the function parameters in the module 1.0.2
.. |Build Status| image:: https://travis-ci.org/RomelTorres/alpha_vantage.png?branch=master
:target: https://travis-ci.org/RomelTorres/alpha_vantage
.. |PyPI version| image:: https://badge.fury.io/py/alpha_vantage.svg
:target: https://badge.fury.io/py/alpha_vantage
.. |Documentation Status| image:: https://readthedocs.org/projects/alpha-vantage/badge/?version=latest
:target: http://alpha-vantage.readthedocs.io/en/latest/?badge=latest
.. |alt text| image:: images/docs_ts_msft_example.png?raw=True
.. |alt text| image:: images/docs_ti_msft_example.png?raw=True
.. |alt text| image:: images/docs_sp_rt_example.png?raw=True
==============
|Build Status| |PyPI version| |Documentation Status|
*Python module to get stock data from the Alpha Vantage API*
Alpha Vantage delivers a free API for real time financial data and most
used finance indicators in a simple json format. This module implements
a python interface to the free API provided by Alpha Vantage
(http://www.alphavantage.co/). It requires a free API, that can be
requested on http://www.alphavantage.co/support/#api-key.
Install
-------
To install the package use:
.. code:: shell
pip install alpha_vantage
If you want to install from source, then use:
.. code:: shell
git clone https://github.com/RomelTorres/alpha_vantage.git
pip install -e alpha_vantage
Usage
-----
To get data in a python, simply import the library and call the object
with your api key and get ready for some awesome free realtime finance
data.
.. code:: python
from alpha_vantage.timeseries import TimesSeries
ts = TimesSeries(key='YOUR_API_KEY')
# Get json object with the intraday data and another with the call's metadata
data, meta_data = ts.get_intraday('GOOGL')
Internally there is a retries counter, that can be used to minimize
connection errors (in case that the api is not able to respond in time),
the default is set to 5 but can be increased or decreased whenever
needed.
.. code:: python
ts = TimesSeries(key='YOUR_API_KEY',retries='YOUR_RETRIES')
Finally the library supports giving its results as json dictionaries
(default) or as pandas dataframe, simply pass the parameter
output\_format='pandas' to change the format of the output for all the
api calls.
.. code:: python
ts = TimesSeries(key='YOUR_API_KEY',output_format='pandas')
Plotting
--------
Using pandas support we can plot the intra-minute value for 'MSFT' stock
quite easily:
.. code:: python
from alpha_vantage.timeseries import TimesSeries
import matplotlib.pyplot as plt
ts = TimesSeries(key='YOUR_API_KEY', output_format='pandas')
data, meta_data = ts.get_intraday(symbol='MSFT',interval='1min', outputsize='full')
data['close'].plot()
plt.title('Intraday Times Series for the MSFT stock (1 min)')
plt.show()
Giving us as output: |alt text|
The same way we can get pandas to plot technical indicators like
Bolliger Bands®
.. code:: python
from alpha_vantage.techindicators import TechIndicators
import matplotlib.pyplot as plt
ti = TechIndicators(key='YOUR_API_KEY', output_format='pandas')
data, meta_data = ti.get_bbands(symbol='MSFT', interval='60min', time_period=60)
data.plot()
plt.title('BBbands indicator for MSFT stock (60 min)')
plt.show()
Giving us as output: |alt text|
Finally we can also plot sector performance just as easy:
.. code:: python
from alpha_vantage.sectorperformance import SectorPerformances
import matplotlib.pyplot as plt
sp = SectorPerformances(key='YOUR_API_KEY', output_format='pandas')
data, meta_data = sp.get_sector()
data['Rank A: Real-Time Performance'].plot(kind='bar')
plt.title('Real Time Performance (%) per Sector')
plt.tight_layout()
plt.grid()
plt.show()
Giving us as output: |alt text|
Tests
-----
In order to run the tests you have to first export your API key so that
the test can use it to run.
.. code:: shell
export API_KEY=YOUR_API_KEY
cd alpha_vantage
nosetests
Documentation
-------------
To find out more about the available api calls, visit the alpha-vantage
documentation at http://www.alphavantage.co/documentation/
Coming soon:
------------
1. [STRIKEOUT:Add basic functionality: 0.0.1]
2. [STRIKEOUT:Add retry in order to allow the calls to be retried in
case of failure: 0.0.2]
3. [STRIKEOUT:Implement all functions described in the alpha vantage
documentation 0.0.3]
4. [STRIKEOUT:Re-factor functions to have an unified method for
accessing the api 0.1.0]
5. [STRIKEOUT:Add pandas support through decorators 0.1.2]
6. [STRIKEOUT:Publish on pipy 0.1.3]
7. Add logging to tests to store api call duration 1.0.1
8. Add unit tests for all the function parameters in the module 1.0.2
.. |Build Status| image:: https://travis-ci.org/RomelTorres/alpha_vantage.png?branch=master
:target: https://travis-ci.org/RomelTorres/alpha_vantage
.. |PyPI version| image:: https://badge.fury.io/py/alpha_vantage.svg
:target: https://badge.fury.io/py/alpha_vantage
.. |Documentation Status| image:: https://readthedocs.org/projects/alpha-vantage/badge/?version=latest
:target: http://alpha-vantage.readthedocs.io/en/latest/?badge=latest
.. |alt text| image:: images/docs_ts_msft_example.png?raw=True
.. |alt text| image:: images/docs_ti_msft_example.png?raw=True
.. |alt text| image:: images/docs_sp_rt_example.png?raw=True
3 maintainers
Metadata
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