investpy is a Python package to retrieve data from Investing.com, which provides data retrieval from up to: 39952 stocks, 82221 funds, 11403 etfs, 2029 currency crosses, 7797 indices, 688 bonds, 66 commodities, 250 certificates and 2812 cryptocurrencies.
investpy allows the user to download both recent and historical data from all the financial products indexed at Investing.com. It includes data from all over the world, from countries such as: United States, France, India, Spain, Russia or Germany, amongst many others.
investpy seeks to be one of the most complete Python packages when it comes to financial data extraction in order to stop relying on public/private APIs, since investpy is FREE and has NO LIMITATIONS. These are some of the features that currently lead investpy to be one of the most consistent packages when it comes to financial data retrieval.
If you want to support the project, you can buy the developer a coffee. More information at: buy-me-a-coffee
In order to get this package working you will need to install it via pip (with a Python3.5 version or higher) on the terminal by typing:
$ pip install investpy
Additionally, if you want to use the latest investpy version instead of the stable one, you can just use the following command:
$ pip install git+https://github.com/alvarobartt/investpy.git@developer
The developer branch ensures the user that the most updated version will always be working and fully operative so as not to wait until the stable release on the master branch comes out (which eventually may take some time depending on the amount of issues to solve).
You can find the complete investpy documentation at Documentation.
Notes: documentation is hosted on Read the Docs and generated using sphinx with the theme sphinx_rtd_theme which is the standard Read the Docs theme for sphinx.
Even though some investpy usage examples are presented on the docs, some basic functionality will be sorted out with sample Python code blocks. Additionally, more usage examples can be found under examples/ directory, which contains a collection of Jupyter Notebooks on how to use investpy and handle its data.
investpy allows the user to download both recent and historical data from any financial product indexed (stocks, funds, etfs, currency crosses, certificates, bonds, commodities indices and cryptos). In the example presented below, the historical data from the past years of an stock is retrieved.
import investpy
df = investpy.get_stock_historical_data(stock='AAPL',
country='United States',
from_date='01/01/2010',
to_date='01/01/2020')
print(df.head()) Open High Low Close Volume Currency
Date
2010-01-04 30.49 30.64 30.34 30.57 123432176 USD
2010-01-05 30.66 30.80 30.46 30.63 150476160 USD
2010-01-06 30.63 30.75 30.11 30.14 138039728 USD
2010-01-07 30.25 30.29 29.86 30.08 119282440 USD
2010-01-08 30.04 30.29 29.87 30.28 111969192 USD
So as to get to know all the available recent and historical data extraction functions provided by investpy, and also, parameter tuning, please read the docs.
Investing.com search engine is completely integrated with investpy, which means that any available financial product (quote) can be easily found. The search function allows the user tune the parameters in order to adjust the search results to their needs, where both product types and countries from where the products are, can be specified. All the search functionality can be easily used, for example, as presented in the following piece of code:
import investpy
search_results = investpy.search_quotes(text='apple',
products=['stocks'],
countries=['united states'],
n_results=10)Retrieved search results will be a list of investpy.utils.search_obj.SearchObj class instances. In order to get to know which are the available functions and attributes of the returned search results, please read the related documentation at Search Engine Documentation. So on, those search results let the user retrieve both recent and historical data from that concrete product, its information, etc., as presented in the piece of code below:
for search_result in search_results[:1]:
print(search_result)
search_result.retrieve_historical_data(from_date='01/01/2019', to_date='01/01/2020')
print(search_result.data.head()){"id_": 6408, "name": "Apple Inc", "symbol": "AAPL", "country": "united states", "tag": "apple-computer-inc", "pair_type": "stocks", "exchange": "NASDAQ"}
Open High Low Close Volume
Date
2019-01-02 154.89 158.85 154.23 157.92 37039736
2019-01-03 143.98 145.72 142.00 142.19 91312192
2019-01-04 144.53 148.55 143.80 148.26 58607072
2019-01-07 148.70 148.83 145.90 147.93 54777764
2019-01-08 149.56 151.82 148.52 150.75 41025312
Crypto currencies support has recently been included, so as to let the user retrieve data and information from any available crypto at Investing.com. Please note that some crypto currencies do not have available data indexed at Investing.com so that it can not be retrieved using investpy neither, even though they are just a few, take it into consideration.
As already presented previously, historical data retrieval using investpy is really easy. The piece of code presented below shows how to retrieve the past years of historical data from Bitcoin (BTC).
import investpy
data = investpy.get_crypto_historical_data(crypto='bitcoin', from_date='01/01/2014', to_date='01/01/2019')
print(data.head()) Open High Low Close Volume Currency
Date
2014-01-01 805.9 829.9 771.0 815.9 10757 USD
2014-01-02 815.9 886.2 810.5 856.9 12812 USD
2014-01-03 856.9 888.2 839.4 884.3 9709 USD
2014-01-04 884.3 932.2 848.3 924.7 14239 USD
2014-01-05 924.7 1029.9 911.4 1014.7 21374 USD
Since investpy is intended to retrieve data from different financial products as indexed in Investing.com, the development of some support modules which implement an additional functionallity based on investpy data, is presented. Note that anyone can contribute to this section by creating any package, module or utility which uses investpy. So on, the ones already created are going to be presented, since they are intended to be used combined with investpy:
- pyrtfolio: is a Python package to generate stock portfolios.
- trendet: is a Python package for trend detection on stock time series data.
If you developed an interesting/useful project based on investpy data, please open an issue in order to let me know so as to include it on this section.
As this is an open source project it is open to contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas. There is an open tab of issues where anyone can open new issues if needed or navigate through them in order to solve them or contribute to its solving. Remember that issues are not threads to describe multiple problems, this does not mean that issues can't be discussed, but so to keep a structured project management, the same issue should not describe different problems, just the main one and some nested/related errors that may be found.
When citing this repository on your publications please use the following BibTeX citation:
@misc{investpy,
author = {Alvaro Bartolome del Canto},
title = {investpy - Financial Data Extraction from Investing.com with Python},
year = {2018-2020},
publisher = {GitHub},
journal = {GitHub Repository},
howpublished = {\url{https://github.com/alvarobartt/investpy}},
}
This Python package has been made for research purposes in order to fit the needs that Investing.com does not cover, so this package works like an Application Programming Interface (API) of Investing.com developed in an altruistic way.
Conclude that this package is not related in any way with Investing.com or any dependant company, the only requirement specified by Investing.com in order to develop this package was "mention the source where data is retrieved from".