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# investpy — Python package for stock historical data extraction from Investing
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# investpy — a Python package for financial historical data extraction from Investing
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[![Python Version](https://img.shields.io/pypi/pyversions/investpy.svg)](https://pypi.org/project/investpy/)
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[![PyPi Version](https://img.shields.io/pypi/v/investpy.svg)](https://pypi.org/project/investpy/)
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## Introduction
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Since [**Investing**](https://es.investing.com/) does not have an API, I decided to develop this Python scraper in order to retrieve historical data from the companies that integrate the **Continuous Spanish Stock Market**. The scraper is a Python package everyone can use through PyPi (Python Package Installer) via [investpy](https://pypi.org/project/investpy/).
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investpy is a Python package to retrieve real-time historical data from [Investing](https://www.investing.com/) mainly
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of spanish financial products, but it is intended to be scalable and so on, work with world financial products such as
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equities, funds, ETFs or currencies.
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The main purpose of developing this package was to use it as the **Data Extraction** tool for its namesake section, for my Final Degree Project at the University of Salamanca titled "**Machine Learning for stock investment recommendation systems**". The package end up being so consistent, reliable and usable that it is going to be used as the main Data Extraction tool by another students in their Final Degree Projects named "*Recommender system of banking products*" and "*Robo-Advisor Application*".
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To conclude this section, I am in the need to specify that this is not the final version of the package, this is just a beta version of it that will keep going while I develop a consistent Python package for financial data extraction.
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investpy seeks to be one of the most used Python packages when it comes to historical data extraction from financial products, so to stop depending on public/private APIs, as investpy is **free** and has **no limitations**, features that lead investpy to be one of the most strong and consistent packages of financial data retrieval.
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## Installation
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* [**lxml 4.3.3**](https://pypi.org/project/lxml/)
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* [**unidecode 1.0.23**](https://pypi.org/project/unidecode/)
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## Documentation
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## Usage
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[investpy - Read the Docs](https://investpy.readthedocs.io/)
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Eventhough some investpy usage examples are shown on the [docs](https://investpy.readthedocs.io/equities.html), some basic functionality will be sorted out with sample Python code blocks.
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## Contribute to investpy - [![Open Source Helpers](https://www.codetriage.com/alvarob96/investpy/badges/users.svg)](https://www.codetriage.com/alvarob96/investpy)
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### Recent/Historical Data
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As this is an open source project it is open to contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas.
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As the main functionality is based on historical data retrieval, the usage of every function will be explained so to ease the user the use of investpy, which is mainly intended for historical data extraction, which means that every other function is additional.
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Also there is an open tab of [issues](https://github.com/alvarob96/investpy/issues) where anyone can contribute opening new issues if needed or navigate through them in order to solve them or contribute to its solving.
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#### Equity Data Retrieval
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Additionally, you can triage issues on [investpy CodeTriage](https://www.codetriage.com/alvarob96/investpy) 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.
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```python
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import investpy
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Feel free to contact package administrator via [email]([email protected])!
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df = investpy.get_recent_data(equity='bbva', as_json=False, order='ascending')
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print(df.head())
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## License
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>>>
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Close High Low Open Volume
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Date
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2019-07-12 4.897 4.985 4.897 4.952 22930000
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2019-07-15 4.926 4.941 4.873 4.915 14830000
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2019-07-16 4.971 5.008 4.913 4.947 30730000
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2019-07-17 4.905 4.965 4.900 4.952 22410000
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2019-07-18 4.856 4.894 4.812 4.879 35820000
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[MIT License](https://github.com/alvarob96/investpy/blob/master/LICENSE)
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df = investpy.get_historical_data(equity='bbva', start='01/01/2018', end='12/08/2019', as_json=False, order='ascending')
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print(df.head())
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## Additional Information
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>>>
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Close High Low Open Volume
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Date
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2018-01-02 7.082 7.169 7.050 7.139 15960000
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2018-01-03 7.094 7.120 7.055 7.113 13320000
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2018-01-04 7.221 7.274 7.104 7.113 20790000
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2018-01-05 7.253 7.282 7.203 7.259 13580000
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2018-01-08 7.235 7.293 7.220 7.274 13420000
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```
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#### Fund Data Retrieval
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```python
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import investpy
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df = investpy.get_fund_recent_data(fund='bbva plan multiactivo moderado pp', as_json=False, order='ascending')
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print(df.head())
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>>>
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Close High Low Open
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Date
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2019-07-12 1.128 1.128 1.128 1.128
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2019-07-15 1.130 1.130 1.130 1.130
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2019-07-16 1.130 1.130 1.130 1.130
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2019-07-17 1.129 1.129 1.129 1.129
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2019-07-18 1.126 1.126 1.126 1.126
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df = investpy.get_fund_historical_data(fund='bbva plan multiactivo moderado pp', start='01/01/2018', end='12/08/2019', as_json=False, order='ascending')
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print(df.head())
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>>>
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Close High Low Open
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Date
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2018-02-15 1.105 1.105 1.105 1.105
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2018-02-16 1.113 1.113 1.113 1.113
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2018-02-17 1.113 1.113 1.113 1.113
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2018-02-18 1.113 1.113 1.113 1.113
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2018-02-19 1.111 1.111 1.111 1.111
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```
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#### ETF Data Retrieval
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```python
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import investpy
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df = investpy.get_etf_recent_data(etf='bbva accion dj eurostoxx 50', as_json=False, order='ascending')
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print(df.head())
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>>>
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Close High Low Open
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Date
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2019-07-12 35.795 35.870 35.825 35.825
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2019-07-15 35.855 35.930 35.760 35.875
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2019-07-16 36.040 36.085 35.835 35.845
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2019-07-17 35.830 36.080 35.810 35.965
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2019-07-18 35.640 35.785 35.515 35.515
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df = investpy.get_etf_historical_data(etf='bbva accion dj eurostoxx 50', start='01/01/2018', end='12/08/2019', as_json=False, order='ascending')
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print(df.head())
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>>>
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Close High Low Open
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Date
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2018-01-02 34.995 35.155 34.860 35.155
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2018-01-03 35.210 35.305 35.020 35.105
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2018-01-04 35.825 35.710 35.465 35.465
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2018-01-05 36.185 36.180 35.900 35.900
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2018-01-08 36.280 36.370 36.240 36.325
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```
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### Additional Data
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As Investing provides more data apart from historical data, some of that data is fetched via investpy if it is considered to be useful. So on, some additional information is retrieved for both equities and funds such as company profiles or inner basic information for equities and funds, respectively as shown below.
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#### Equity Company Profile Retrieval
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```python
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import investpy
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company_profile = investpy.get_equity_company_profile(equity='bbva', language='en')
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print(company_profile)
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>>> "Banco Bilbao Vizcaya Argentaria, S.A. (BBVA) is a diversified financial company engaged in retail banking ..."
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```
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#### Fund Additional Information Retrieval
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```python
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import investpy
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fund_information = investpy.get_fund_information(fund='bbva plan multiactivo moderado pp', as_json=True)
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print(fund_information)
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>>> {'Fund Name': 'Bbva Plan Multiactivo Moderado Pp',
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'Rating': '4',
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'1-Year Change': '-1,19%',
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'Previous Close': '1.103',
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'Risk Rating': '1',
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'TTM Yield': '0%',
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'ROE': '14,02%',
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'Issuer': 'BBVA Pensiones EGFP',
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'Turnover': 'N/A',
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'ROA': '4,97%',
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'Inception Date': '2012-10-16 00:00:00',
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'Total Assets': '1670000000',
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'Expenses': 'N/A',
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'Min Investment': '30',
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'Market Cap': '34820000000',
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'Category': 'Mixtos Euros Moderados PP'}
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```
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## Contribute - [![Open Source Helpers](https://www.codetriage.com/alvarob96/investpy/badges/users.svg)](https://www.codetriage.com/alvarob96/investpy)
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The package is currently in a development version, so please, if needed open an [issue](https://github.com/alvarob96/investpy/issues) to solve all the possible problems the package may be causing
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so I fix them as fast as I can. Also, any new ideas or proposals are welcome, and I will gladly implement them in the package if the are positive and useful.
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As this is an open source project it is open to contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas.
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For further information or any question feel free to contact me via email at [email protected]
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Also there is an open tab of [issues](https://github.com/alvarob96/investpy/issues) where anyone can contribute opening new issues if needed or navigate through them in order to solve them or contribute to its solving.
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You can also check my [Medium Publication](https://medium.com/research-studies-by-alvaro-bartolome/investpy-a-python-library-for-historical-data-extraction-from-the-spanish-stock-market-ad4d564dbfc5), where I upload weekly posts related to Data Science and some of them explain investpy functions and development in a deeper way.
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Additionally, you can triage issues on [investpy CodeTriage](https://www.codetriage.com/alvarob96/investpy) 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.
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## Disclaimer
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This Python Package has been made for research purposes in order to fit a 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 for developing this package was to mention the source where data is retrieved.
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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*".

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