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https://github.com/astro30/valinvest
A value investing tool based on Warren Buffett, Joseph Piotroski and Benjamin Graham thoughts
https://github.com/astro30/valinvest
buffett graham investing investment numpy pandas piotroski python value-investing
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A value investing tool based on Warren Buffett, Joseph Piotroski and Benjamin Graham thoughts
- Host: GitHub
- URL: https://github.com/astro30/valinvest
- Owner: astro30
- License: mit
- Created: 2020-02-07T15:49:10.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2023-07-25T21:13:06.000Z (over 1 year ago)
- Last Synced: 2024-03-25T07:02:22.126Z (10 months ago)
- Topics: buffett, graham, investing, investment, numpy, pandas, piotroski, python, value-investing
- Language: Python
- Homepage:
- Size: 74.2 KB
- Stars: 148
- Watchers: 4
- Forks: 28
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
Valinvest
A value investing tool based on Warren Buffett, Joseph Piotroski and Benjamin Graham thoughts
# Welcome to Valinvest
## 🔥 Important update 🔥
The Financial API used by valinvest to get financial data has been rewritten and now requires a API Key in order to make request. So, the first step now is to register on https://financialmodelingprep.com/login and get an API key. Keep it, you will need it to use valinvest ;)
## ✨tl;dr ✨
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.
1. Install it
```bash
pip install valinvest
```
2. Calculate score
```python
>>> import valinvest
>>> aapl = valinvest.Fundamental('AAPL', YOUR_API_KEY)
>>> aapl.fscore()
6.8
```
## Table of contents :books:
- [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.
> 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/)