Package: investpy
Financial Data Extraction from Investing.com with Pythoninvestpy is a Python package to retrieve data fromInvesting.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 4697 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 to stop relying
on public/private APIs since investpy isFREEand hasNO 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:hammer_and_wrench: InstallationTo get this package working you will need toinstall it via pip(with a Python 3.6 version or higher) on the terminal by typing:$ pip install investpyAdditionally,if you want to use the latest investpy version instead of the stable one, you can install it from sourcewith the following command:$ pip install git+https://github.com/alvarobartt/investpy.git@masterThe master branch ensures the user that the most updated version will always be working and fully operativeso as not to wait until the
the stable release comes out (which eventually may take some time depending on the number of issues to solve).:computer: UsageEven though some investpy usage examples are presented on thedocs,
some basic functionality will be sorted out with sample Python code blocks. Additionally, more usage examples
can be found underexamples/directory, which
contains a collection of Jupyter Notebooks on how to use investpy and handle its data.:pushpin:Note thatinvestpy.search_quotesis the only function that ensures that the data is updated and aligned 1:1 with
the data provided by Investing.com!:chart_with_upwards_trend: Recent/Historical Data Retrievalinvestpy allows the user todownload 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 a stock is retrieved.importinvestpydf=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      USDTo get to know all the available recent and historical data extraction functions provided by
investpy, and also, parameter tuning, please read the docs.:mag: Search Live DataInvesting.com search engine is completely integratedwith investpy, which means that any available
financial product (quote) can be easily found. The search function allows the user to tune the parameters
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:importinvestpysearch_result=investpy.search_quotes(text='apple',products=['stocks'],countries=['united states'],n_results=1)print(search_result){"id_":6408,"name":"Apple Inc","symbol":"AAPL","country":"united states","tag":"/equities/apple-computer-inc","pair_type":"stocks","exchange":"NASDAQ"}Retrieved search results will be alistofinvestpy.utils.search_obj.SearchObjclass instances, unlessn_resultsis set to 1, when just a singleinvestpy.utils.search_obj.SearchObjclass instance will be returned.
To get to know which are the available functions and attributes of the returned search results, please read the related
documentation atSearch Engine Documentation. So on, those
search results let the user retrieve both recent and historical data, its information, the technical indicators,
the default currency, etc., as presented in the piece of code below:recent_data=search_result.retrieve_recent_data()historical_data=search_result.retrieve_historical_data(from_date='01/01/2019',to_date='01/01/2020')information=search_result.retrieve_information()default_currency=search_result.retrieve_currency()technical_indicators=search_result.retrieve_technical_indicators(interval='daily'):money_with_wings: Crypto Currencies Data RetrievalCryptocurrencies support has recently been included, to let the user retrieve data and information from any
available crypto at Investing.com. Please note that some cryptocurrencies do not have available data indexed
at Investing.com so that it can not be retrieved using investpy either, even though they are just a few,
consider it.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).importinvestpydata=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:open_book: DocumentationYou can find thecomplete investpy documentationatDocumentation.:sparkles: ContributeAs this is an open-source project it isopen to contributions, bug reports, bug fixes, documentation improvements,
enhancements, and ideas. There is an open tab ofissueswhere
anyone can open new issues if needed or navigate through them 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 not
be discussed, but so to keep structured project management, the same issue should not describe different
problems, just the main one and some nested/related errors that may be found.:question: Discussions (Q&A, AMA)GitHub recently released a new feature namedGitHub Discussions(still in beta). GitHub Discussions is a
collaborative communication forum for the community around an open source project.Check the investpy GitHub Discussions page atDiscussions,
and feel free to ask me (ar any developer) anything, share updates, have open-ended conversations, and follow along
on decisions affecting the community's way of working.:pushpin:Note. Usually I don't answer emails asking me questions about investpy, as we currently have the
GitHub Discussions tab, and I encourage you to use it. GitHub Discussions is the easiest way to contact me about
investpy, so that I don't answer the same stuff more than once via email, as anyone can see the opened/answered
discussions.:card_index_dividers: Related projectsSince investpy is intended to retrieve data from different financial products as indexed in Investing.com,
thedevelopment of some support modules which implement an additional functionality based on investpy data,
is presented. Note thatanyone can contribute to this sectionby creating any package, module, or utility that
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 to let me know to
include it in this section.:memo: CitationWhen citing this repository on your scientific publications please use the followingBibTeXcitation:@misc{investpy,author={Alvaro Bartolome del Canto},title={investpy - Financial Data Extraction from Investing.com with Python},year={2018-2021},publisher={GitHub},journal={GitHub Repository},howpublished={\url{https://github.com/alvarobartt/investpy}},}When citing this repository on any other social media, please use the following citation:investpy - Financial Data Extraction from Investing.com with Python developed by Alvaro Bartolome del CantoYou should also mention the source from where the data is retrieved, Investing.com; even though it's already
included in the package short description title.:man_technologist: Contact InformationYou can contact me at any of my social network profiles::briefcase: LinkedIn:https://linkedin.com/in/alvarobartt:bird: Twitter:https://twitter.com/alvarobartt:octocat: GitHub:https://github.com/alvarobarttOr via email atalvarobartt@yahoo.com.:warning: DisclaimerThis Python package has been made forresearch purposesto fit the needs that Investing.com does not cover,
so this package works like an Application Programming Interface (API) of Investing.com developed in analtruistic way.Conclude thatinvestpy is not affiliated in any way to Investing.com or any dependant company, the only
requirement specified by Investing.com to develop this package was to "mention the source where data is
retrieved from".