dylkyn / ic_vc2_screen

VC2_Screen

Goal

• Provide a strategy / screen that quickly determines whether a company is undervalued / overvalued.

Strategy

1. Calculate following ratios for each stock:
   • Price to Book Value
   • Price to Sales
   • EBITDA to EV
   • Price to Cashflow
   • Price to Earnings
   • Shareholder Yield
2. Give each stock a percentile rank 1 - 100 for each of the ratios. Example: if AAPL has the best price to sales out of all the stocks you are looking at, it would get a 1 for Price to Sales bc it is in the top 1% for P / S. If a ratio can't be calculate (like negative P / E) give it a neutral score of 50
3. Add up all the ratio scores for each stock
4. Give the stocks a percentile rank based on the combined scores
5. Each stock will have a number 1 - 100. 1 is the most undervalued out of your universe of stocks, while 100 is the most overvalued

Flow

get_datasets.py -> calc_ratios.py -> rank.py -> output.py

Input and Output

• Will take in an excel sheet of stock tickers
• Will output excel sheet with VC2 score for every stock in the input csv

How to Use get_datasets.py

• get_datasets.py creates a .csv file called company_metrics.csv
• To request data of different stocks, edit the beginning of the file and add / change the list of stocks (make sure you are using the stock ticker)
• To run get_datasets.py, navigate into the vc2_screen project folder (which has all the proj files, you can check with ls). Run the file with python get_datasets.py
• It will take about 30 sec to run, and after you should see a file in the vc2_screen folder called company_metrics.csv. The csv contains all the data needed for each company for each of the next parts of the project

How to use the dataset (.csv) in other files

• Make sure you have pandas installed
• After running get_datasets.py, you can easily access the dataset (company_metrics.csv) in your file by using pandas
• Run df = df.read_csv("company_metrics.csv") to load the csv as a pandas dataframe in your folder
• You can now print what the data frame looks like with print(df.head())
• df.loc["aapl"].tolist() will give you all the stats for aapl in a normal python list, you can replace aapl with any ticker