investpy 0.9.14

investpy — a Python package for financial historical data extraction from Investing

Financial Historical Data Extraction with Python

investpy is a Python package to retrieve historical data from Investing. It provides historical 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. Basically, investpy allows you to download historical data from almost all the financial products indexed in Investing.com. All the data that can be retrieved includes data from all over the world, from countries such as: United States, France, India, Spain, Russia or Germany, amongst many others. Therefore, investpy is intended to wrap up all the available data from Investing.com, so that it can be retrieved via Python for its further usage and/or analysis.

investpy seeks to be one of the most complete Python packages when it comes to historical data extraction of financial products in order to stop relying on public/private APIs, as 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 of financial data retrieval.

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Installation

In order to get this package working you will need to install investpy using pip on the terminal by typing:

$ pip install investpy==0.9.14

Every package used is listed in requirements.txt file, which can also be installed via pip:

$ pip install -r requirements.txt

Usage

Even though some investpy usage examples are shown on the docs, some basic functionality will be sorted out with sample Python code blocks.

Recent/Historical Data Retrieval

As the main functionality is based on historical data retrieval, the usage of stock data retrieval functions will be explained so to ease the use of investpy, which is mainly intended for historical data extraction, which means that every other function is additional.

import investpy

df = investpy.get_stock_recent_data(stock='BBVA',
                                    country='spain')
print(df.head())

             Open   High    Low  Close    Volume Currency
Date                                                     
2019-08-13  4.263  4.395  4.230  4.353  27250000      EUR
2019-08-14  4.322  4.325  4.215  4.244  36890000      EUR
2019-08-15  4.281  4.298  4.187  4.234  21340000      EUR
2019-08-16  4.234  4.375  4.208  4.365  46080000      EUR
2019-08-19  4.396  4.425  4.269  4.269  18950000      EUR

import investpy

df = investpy.get_stock_historical_data(stock='BBVA',
                                        country='spain',
                                        from_date='01/01/2010',
                                        to_date='01/01/2019')
print(df.head())

             Open   High    Low  Close  Volume Currency
Date                                                   
2010-01-04  12.73  12.96  12.73  12.96       0      EUR
2010-01-05  13.00  13.11  12.97  13.09       0      EUR
2010-01-06  13.03  13.17  13.02  13.12       0      EUR
2010-01-07  13.02  13.11  12.93  13.05       0      EUR
2010-01-08  13.12  13.22  13.04  13.18       0      EUR

Search Data

As financial data is really complex and sometimes both the product name/symbol and the country are unknown for the user, in terms of what does investpy expect, every financial product listed in investpy (which currently includes stocks, funds, etfs, indices, currency crosses, bonds and commodities) has its own search function. Search functions allow the user to search among all the available stocks for example, whenever just one field is known (even though it is not the exact match). So on, a basic example on stock search by the ISIN code is presented below:

import investpy

search_results = investpy.search_stocks(by='isin', value='ES0113211835')

print(search_results.head())

          country  name                             full_name          isin  currency symbol  
0          mexico  BBVA    Banco Bilbao Vizcaya Argentaria SA  ES0113211835       MXN   BBVA  
1          mexico  BBVA  Banco Bilbao Vizcaya Argentaria S.A.  ES0113211835       MXN   BBVA  
2         belgium  BBVA    Banco Bilbao Vizcaya Argentaria SA  ES0113211835       EUR   BBVA  
3           spain  BBVA  Banco Bilbao Vizcaya Argentaria S.A.  ES0113211835       EUR   BBVA  
4  united kingdom  BBVA    Banco Bilbao Vizcaya Argentaria Sa  ES0113211835       EUR   BVAB

Note that additionally the Investing search engine is completely integrated with investpy so that any available quote as indexed in Investing can be easily retrieved just using the following piece of code:

import investpy

search_results = investpy.search(text='gold')

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 visit: investpy search docs.

Crypto Currencies Data Retrieval

It has recently been included crypto currency data retrieval functions. All the crypto currencies that are available in Investing for data retrieval are, so on, available in investpy.

So to ease investpy's usage, some samples will be presented below on how to retrieve the past 5 years of data from Bitcoin:

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

Yes, retrieving historical data from any available crypto currency is really that easy!

Additional Data

As Investing.com provides more data besides the historical one, some of that additional data can be fetched via investpy. Currently, as the package is under-development, some additional functions have been created in order to retrieve more data as indexed in Investing.com.

and much more!

All the functions definitions and usage can be found in the Documentation!

Utilities

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 this package. So on, the ones already created are going to be presented, since they are intended to be used combined with investpy:

• investpy_portfolio: is a Python package to generate stock portfolios.
• trendet: is a Python package for trend detection on stock time series data.

Contribute -

As this is an open source project it is open to contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas.

Also there is an open tab of issues where anyone can contribute opening 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 issues, this does not mean that issues can't be discussed, but if new issues are reported, a new issue should be open so to keep a structured project management.

Additionally, you can triage issues on investpy CodeTriage so you can provide issues so the package can grow and improve as the issues solves bugs, problems or needs, and maybe provide new ideas to improve package functionality and efficiency.

Frequent Asked Questions - FAQs

Where can I find the reference of a function and its usage?

Currently the docs/ are still missing a lot of information, but they can be clear enough so that users can get to know which functions can be used and how. If you feel that any functionallity or feature is not clear enough, please let me know in the issues tab, so that I can explain it properly for newcomers, so that answers are more general and help more users than just the one asking it. Docs can be found at: Documentation

What do I do if the financial product I am looking for is not indexed in investpy?

As it is known, investpy gathers and retrieves data from Investing.com which is a website that contains a lot of financial information. Since investpy relies on Investing data, some of it may not be available in Investing, which will mean that it will not be available in investpy either. Anyways, it can be an investpy problem while retrieving data, so on, there is a search function (investpy.search(text, n_results, filters)) that can be used for searching financial products that are available in Investing but they can not be retrieved using investpy main functions.

I am having problems while installing the package.

If you followed the Installation Guide, you should be able to use investpy without having any problem, anyways, if you are stuck on it, open an issue at investpy issues tab so to let the developers know which is your problem in order to solve it as soon as possible. If you were not able to complete the installation, please check that you are running Python 3.5 at least and that you are installing the latest version available, if you are still having problems, open an issue.

How do I contribute to investpy?

Currently I am not admitting any Pull Request since investpy is under development, and so to keep a clean structure, I will be developing new functionalities until code is clean enough to let newcome contributors help. Anyways, the most effective tool you have in order to contribute to investpy are issues where you can give me new ideas or some functionallity you would like to see implemented in investpy. You can also use issues in order to report bugs or problems so to help investpy's development and consistency.

How do I reference investpy?

Since investpy is an open source Python package, whenever you use it, would be nice from you to mention or comment where does the data comes from. This way, investpy can be spread among more users which will consequently improve package usage since more users can contribute to it due to the increasing reach to newcome developers. A sample reference is presented below:

investpy - a Python package for Financial Historical Data Extraction developed by Álvaro Bartolomé del Canto @ alvarob96 at GitHub

Disclaimer

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 in order to develop this package was "mention the source where data is retrieved from".