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https://github.com/alvarobartt/investpy

Financial Data Extraction from Investing.com with Python
https://github.com/alvarobartt/investpy

financial-data hacktoberfest historical-data investing investing-api investing-com investing-python

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Financial Data Extraction from Investing.com with Python

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## :warning: `investpy` is not working fine currently due to some Investing.com changes in their APIs, so please use [`investiny`](https://github.com/alvarobartt/investiny) in the meantime as I'm actively updating it and adding more and more features of some temporary solutions while we fix `investpy`. Thanks!



Financial Data Extraction from Investing.com with Python



investpy is a Python package to retrieve data from [Investing.com](https://www.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 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 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.



[![Python Version](https://img.shields.io/pypi/pyversions/investpy.svg)](https://pypi.org/project/investpy/)

[![PyPI Version](https://img.shields.io/pypi/v/investpy.svg)](https://pypi.org/project/investpy/)

[![Package Status](https://img.shields.io/pypi/status/investpy.svg)](https://pypi.org/project/investpy/)

[![Build Status](https://github.com/alvarobartt/investpy/workflows/run_tests/badge.svg)](https://github.com/alvarobartt/investpy/actions?query=workflow%3Arun_tests)

[![Documentation Status](https://readthedocs.org/projects/investpy/badge/?version=latest)](https://investpy.readthedocs.io/)



**If you want to support the project, you can buy the developer a coffee. More information at: [buy-me-a-coffee](https://github.com/alvarobartt/buy-me-a-coffee)**



Buy Me A Coffee



---



## :hammer_and_wrench: Installation



To get this package working you will need to **install it via pip** (with a Python 3.6 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 install it from source** with the following command:



``$ pip install git+https://github.com/alvarobartt/investpy.git@master``



**The master branch ensures the user that the most updated version will always be working and fully operative** so 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: Usage



Even though some investpy usage examples are presented on the [docs](https://investpy.readthedocs.io/usage.html), 

some basic functionality will be sorted out with sample Python code blocks. Additionally, more usage examples 

can be found under [examples/](https://github.com/alvarobartt/investpy/tree/master/examples) directory, which 

contains a collection of Jupyter Notebooks on how to use investpy and handle its data.



:pushpin: __Note that `investpy.search_quotes` is 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 Retrieval



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 a stock is retrieved. 



```python

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())

```

```{r, engine='python', count_lines}

             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

```



To 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 Data



**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 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:



```python

import investpy



search_result = investpy.search_quotes(text='apple', products=['stocks'],

                                       countries=['united states'], n_results=1)

print(search_result)

```

```json

{"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 a `list` of `investpy.utils.search_obj.SearchObj` class instances, unless

`n_results` is set to 1, when just a single `investpy.utils.search_obj.SearchObj` class 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 at [Search Engine Documentation](https://investpy.readthedocs.io/search_api.html). 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:



```python

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 Retrieval



Cryptocurrencies 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).



```python

import investpy



data = investpy.get_crypto_historical_data(crypto='bitcoin',

                                           from_date='01/01/2014',

                                           to_date='01/01/2019')



print(data.head())

```

```{r, engine='python', count_lines}

             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: Documentation



You can find the **complete investpy documentation** at [Documentation](https://investpy.readthedocs.io/).



---



## :sparkles: Contribute



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](https://github.com/alvarobartt/investpy/issues) where 

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 named __GitHub 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 at [Discussions](https://github.com/alvarobartt/investpy/discussions), 

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 projects



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 functionality based on investpy data**, 

is presented. Note that **anyone can contribute to this section** by 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](https://github.com/alvarobartt/pyrtfolio/): is a Python package to generate stock portfolios.

- [trendet](https://github.com/alvarobartt/trendet/): is a Python package for trend detection on stock time-series data.

- [pypme](https://github.com/ymyke/pypme): is a Python package for PME (Public Market Equivalent) calculation



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: Citation



When citing this repository on your scientific publications please use the following **BibTeX** citation:



```bibtex

@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 Canto

```



You 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 Information



You 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/alvarobartt



Or via email at [email protected].



---



## :warning: Disclaimer



This Python package has been made for **research purposes** 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 **investpy 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".