Metadata-Version: 2.1
Name: valinvest
Version: 0.0.1
Summary: A value investing tool based on Warren Buffett, Joseph Piotroski and Benjamin Graham thoughts
Home-page: https://github.com/astro30/valinvest
Author: Guillaume Rey
License: UNKNOWN
Description: <h1 align="center">
          <br>
          <strong>Valinvest</strong>
          <br>
          <br>
          <img src="https://imgs.xkcd.com/comics/technical_analysis_2x.png" />
        </h1>
        
        <h4 align="center">A value investing tool based on Warren Buffett, Joseph Piotroski and Benjamin Graham thoughts</h4>
        
        # Welcome to Valinvest <!-- omit in toc -->
        
        ## ✨tl;dr ✨ <!-- omit in toc -->
        For a given stock ticker, `valinvest` calculates a score from 0 to 9. The higher the score, the better the company is according to the scoring methodology.
        ```python
        >>> import valinvest
        >>> aapl = valinvest.Fundamental('AAPL')
        >>> aapl.fscore()
        6.8
        ```
        
        
        ## Table of contents :books: <!-- omit in toc -->
        
        - [Introduction](#introduction)
        - [Methodology description](#methodology-description)
            - [Growth](#growth)
            - [Profitability](#profitability)
            - [Debts](#debts)
            - [Market sensibility](#market-sensibility)
            - [Investment](#investment)
        - [Installation](#installation)
        - [Examples](#examples)
          - [Starbucks Corporation (SBUX)](#starbucks-corporation-sbux)
          - [Apple Inc. (AAPL)](#apple-inc-aapl)
        - [License](#license)
        - [Credits](#credits)
        
        ## Introduction
        
        The aim of the package is to evaluate a stock according to his fundamentals by setting a score and identify buy and sells opportunies through technical indicators.
        
        ## Methodology description
        
        The scoring methodology is based on Joseph Piotroski's study ([Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers](http://www.chicagobooth.edu/~/media/FE874EE65F624AAEBD0166B1974FD74D.pdf)). The F-Score is used to help financial investment decisions by finding the best value stocks on the market.<br>
        
        > The Piostroski score is calculated based on 9 criteria divided into 3 groups:
        > 
        > #### Profitability
        >
        > - Return on Assets (1 point if it is positive in the current year, 0 otherwise)
        > - Operating Cash Flow (1 point if it is positive in the current year, 0 otherwise)
        > - Change in Return of Assets (ROA) (1 point if ROA is higher in the current year compared to the previous one, 0 otherwise)
        > - Accruals (1 point if Operating Cash Flow/Total Assets is higher than ROA in the current year, 0 otherwise)
        >
        > #### Leverage, Liquidity and Source of Funds
        >
        > - Change in Leverage (long-term) ratio (1 point if the ratio is lower this year compared to the previous one, 0 otherwise)
        > - Change in Current ratio (1 point if it is higher in the current year compared to the previous one, 0 otherwise)
        > - Change in the number of shares (1 point if no new shares were issued during the last year)
        >
        > #### Operating Efficiency
        >
        > - Change in Gross Margin (1 point if it is higher in the current year compared to the previous one, 0 otherwise)
        > - Change in Asset Turnover ratio (1 point if it is higher in the current year compared to the previous one, 0 otherwise)
        > 
        
        This software calculates an alternate version of the F-Score as follows:
        #### Growth
        - Net Revenue
        - EBITDA
        - Earnings per share (EPS)
        
        #### Profitability
        - CROIC
        - ROIC
        
        #### Debts
        - EBITDA cover ratio
        - Debt coverage
        
        #### Market sensibility
        - Beta
        
        #### Investment
        - Equity buyback
        
        ## Installation
        
        > `pip install valinvest`
        
        ## Examples
        
        ### Starbucks Corporation (SBUX)
        
        |              | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | Score |
        | ------------ | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ----- |
        | REV_G        |      | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1     |
        | EBT_G        |      | 1    | 1    | 1    | 0    | 1    | 1    | 1    | 0    | 0    | 1    | 0.7   |
        | EPS_G        |      | 1    | 1    | 1    | 0    | 1    | 0    | 1    | 1    | 1    | 0    | 0.7   |
        | ROIC         | 0    | 0    | 1    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0.1   |
        | CROIC        | 1    | 1    | 1    | 1    | 1    | 0    | 1    | 1    | 1    | 1    | 1    | 0.9   |
        | 5YRS_BETA    |      |      |      |      |      |      |      |      |      |      |      | 1     |
        | EBITDA_COVER | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1     |
        | DEBT_COST    | 0    | 0    | 0    | 0    | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 0.7   |
        | EQ_BUYBACK   |      | 1    | 0    | 0    | 1    | 0    | 0    | 1    | 1    | 1    | 1    | 0.6   |
        | F-SCORE      |      |      |      |      |      |      |      |      |      |      |      | 6.7   |
        
        ```python
        >>> import valinvest
        >>> sbux = valinvest.Fundamental('SBUX')
        >>> sbux.fscore()
        6.7
        ```
        
        ### Apple Inc. (AAPL)
        
        |              | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | Score |
        | ------------ | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ----- |
        | REV_G        |      | 1    | 1    | 1    | 1    | 1    | 1    | 0    | 1    | 1    | 0    | 0.8   |
        | EBT_G        |      | 1    | 1    | 1    | 0    | 1    | 1    | 0    | 1    | 1    | 0    | 0.7   |
        | EPS_G        |      | 1    | 1    | 1    | 0    | 0    | 1    | 0    | 1    | 1    | 0    | 0.6   |
        | ROIC         | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1     |
        | CROIC        | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1     |
        | 5YRS_BETA    |      |      |      |      |      |      |      |      |      |      |      | 0     |
        | EBITDA_COVER | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1     |
        | DEBT_COST    | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1    | 1     |
        | EQ_BUYBACK   |      | 1    | 0    | 0    | 1    | 0    | 1    | 1    | 1    | 1    | 1    | 0.7   |
        | F-SCORE      |      |      |      |      |      |      |      |      |      |      |      | 6.8   |
        
        ```python
        >>> import valinvest
        >>> aapl = valinvest.Fundamental('AAPL')
        >>> aapl.fscore()
        6.8
        ```
        
        ## License
        
        This project is licensed under the MIT License - see the [LICENSE.md](https://github.com/astro30/valinvest/blob/master/LICENSE) file for details
        
        ## Credits
        
        This software uses code from several open source packages:
        
        - [pandas](http://pandas.pydata.org)
        - [numpy](http://numpy.pydata.org)
        - [requests](https://requests.readthedocs.io/en/master/)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
