valinvest 0.0.2

A value investing tool based on Warren Buffett, Joseph Piotroski and Benjamin Graham thoughts

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

pip install valinvest

2. Calculate score

>>> import valinvest
>>> aapl = valinvest.Fundamental('AAPL', YOUR_API_KEY)
>>> aapl.fscore()
6.8

Table of contents :books:

• Introduction
• Methodology description
  • Growth
  • Profitability
  • Debts
  • Market sensibility
  • Investment
• Installation
• Examples
  • Starbucks Corporation (SBUX)
  • Apple Inc. (AAPL)
• License
• 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). 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

>>> 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

>>> import valinvest
>>> aapl = valinvest.Fundamental('AAPL')
>>> aapl.fscore()
6.8

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Credits

This software uses code from several open source packages:

• pandas
• numpy
• requests