Metadata-Version: 2.1
Name: fundamentos
Version: 1.5
Summary: Download Bovespa Stock Market fundamentals with Python.
Home-page: https://github.com/NathanMoura/fundamentos
Author: Nathan de Moura Vieira
Author-email: nathanmoura194@gmail.com
License: MIT
Download-URL: https://github.com/NathanMoura/fundamentos/archive/v1.5.tar.gz
Description: 
        # fundamentos
        
        [![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://github.com/NathanMoura/fundamentos/blob/master/LICENSE.txt)
        [![PyPI version](https://badge.fury.io/py/fundamentos.svg)](https://badge.fury.io/py/fundamentos)
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        [![Downloads](https://pepy.tech/badge/fundamentos)](https://pepy.tech/project/fundamentos)
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        `fundamentos` is a tiny, threaded, package that allows you to quickly download historical data from the Brazilian Stock Market, both annualy and quarterly.
        
        The sources from where it downloads data are
        - [ADVFN](https://br.advfn.com/bolsa-de-valores/bovespa "ADVFN") for [fundamentals](#the-get_fundamentos-function)
        - [fundamentus](https://www.fundamentus.com.br/detalhes.php "fundamentus") for [balance sheets](#the-get_balanco-function) and [income statements](#the-get_dre-function).
        
        ## Instalation
        
        Install `fundamentos` using pip:
        
        ```sh
        $ pip install fundamentos
        ```
        
        
        ## Quick Start
        
        ### The get_fundamentos function
        
        Get some fundamentals!
        
        From default, the results are grouped by year, but if you want to, you can specify a quarter to download data from. Additionally, if you want to be even more specific, you can specify the year and the quarter from which you want to download.
        
        ```python
        import fundamentos as ftos
        
        # Downloading data from Itaú Unibanco SA
        
        # Downloads all historical fundamentals, annually
        df = ftos.get_fundamentos('ITUB4')
        
        # Downloads all historical fundamentals on third quarters
        df = ftos.get_fundamentos('ITUB4', quarter=3)
        
        # Downloads fundamentals from first quarter of 2013
        df = ftos.get_fundamentos('ITUB4', year=2013, quarter=1)
        ```
        
        The output is a `pandas.DataFrame` and its columns are hierarchically ordered by topics, which makes it easier to filter the data. However, if you need a regular index of columns you can specify that by passing `separated=False` as a parameter.
        
        Topics are
        
        
        - Mercado - _Market_
        - Resultados - _Income_
        - Patrimônio - _Net Worth_
        - Caixa - _Cash_
        - Dívida - _Debt_
        - Liquidez e Solvência - _Solvency and Liquidity_
        - Fluxo de Caixa - _Cash Flow_
        - Investimentos - _Investments_
        - Dividendos - _Dividends_
        
        
        So, for example, cash indicators could be accessed separately with
        
        ```python
        df['Caixa']
        ```
        
        **quick tip:** _if you can't understand the acronyms of the indicators you can use `ftos.get_schema()`, which is a function that returns a `pandas.DataFrame` with the full name versions of each indicator_
        
        ### The get_tickers function
        
        This function returns a `pandas.DataFrame` with every company listed on the Brazilian Stock Market, their respective corporate names and codes
        
        ```python
        import fundamentos as ftos
        
        tickers = ftos.get_tickers()
        ```
        
        ### The get_balanco function
        
        Get some balance sheets!
        
        From default, the results are grouped by year, but if you want to, you can download them quarterly by using `quarterly=True`
        
        ```python
        import fundamentos as ftos
        
        # Downloads all historical balance sheets, annually
        df = ftos.get_balanco('ITUB4')
        
        # Downloads all historical balance sheets, quarterly
        df = ftos.get_balanco('ITUB4', quarterly=True)
        ```
        As with `get_fundamentos`, the output is also a `pandas.DataFrame` with columns hierarchically ordered by topics. You can also deactivate that by passing `separated=False` as an argument.
        
        Topics are
        
        - Ativo Total - _Total Assets_
        - Ativo Circulante - _Current Assets_
        - Ativo Não Circulante - _Non-current Assets_
        - Passivo Total - _Total Liabilities_
        - Passivo Circulante - _Current Liabilities_
        - Passivo Não Circulante - _Non-current Liabilities_
        - Patrimônio Líquido - _Net Worth_
        
        ### The get_dre function
        
        Get some income statements!
        
        The parameters are pretty similar to thoses in `get_balanco`
        
        ```python
        import fundamentos as ftos
        
        # Downloads all historical income statements, annually
        df = ftos.get_dre('ITUB4')
        
        # Downloads all historical income statements, quarterly
        df = ftos.get_dre('ITUB4', quarterly=True)
        ```
        
        ### The python help function
        
        I tried to be as descriptive as I could on the `docstrings`, so if you need more information about what each function does you can use
        
        ```python
        help(function)
        ```
        
        
        ## License
        
        `fundamentos` is distributed under the **MIT License**. See the [LICENSE.txt](https://github.com/NathanMoura/fundamentos/blob/master/LICENSE.txt) file in the release for details.
        
Keywords: pandas,finance,fundamentals,bovespa
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Topic :: Office/Business :: Financial
Classifier: Topic :: Office/Business :: Financial :: Investment
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Description-Content-Type: text/markdown
