Simple Scraper for Finviz stock data.
The function scrape_finviz accepts a list of tickers and returns a pandas dataframe. All data from the financial highlights table (72 variables) as well as the sector, sub-sector and country are returned. A progress bar is displayed for tracking (requires the progressbar library)
Extended version fetches Gurufocus' Piotroski F-Score, Altman Z-Score and Beneish M-Score along with two values of ROIC and WACC, appending them as last 5 columns in the resulting dataset.
The list of tickers may be in uppercase or lower case
# data is a pandas DataFrame
data=scrape_finviz(['FB','INGN'])the file user-specific-variables has following data:
json_file = '/Users/username/your_json_file'
sheet_key = 'your_sheet_key_taken_from_address_bar'
worksheet_title = 'your_worksheet_title'This code uses pygsheets library to access google sheets. Enable API and create a Service Account at https://console.developers.google.com/apis
Then, allow freshly created account to edit your sheet and download service key as JSON file.
To locate sheet's key, use long string from the address bar:
Multiprocessing is also implemented in multiprocessing_finviz.py. In this example tickers from the S&P500 are scraped, based on data from https://datahub.io/. To run open command prompt and enter:
python multiprocessing_finviz.py <<save_path>> where <<save_path>> is the location to save the results (default is C:/SnP500.csv).
The price data has a lag of 15-20 minutes. Certain financial ratios might be affected by the price. It is recommended to scrape after market close or before market open. Further cleaning of the scraped data might be required (e.g. Market Cap, Income and Sales are returned with B or M for Billion or Million respectively).