A Command Line too to scrape stock fundamental data from finiviz.
make sure you have the required libraries install. Run pip install -r requirements.txt to install dependancies.
cd into scrapeStocks directory and run python3 scrapeStocks --help
$python3 scrapeStocks --help
usage: scrapeStocks.py [-h] [-c] [--timeout TIMEOUT] [--headers HEADERS [HEADERS ...]]
STOCK_SYMBOLS [STOCK_SYMBOLS ...]
positional arguments:
STOCK_SYMBOLS One or more stock symbol to retrieve data.
optional arguments:
-h, --help show this help message and exit
-c, --csv Create Comma-Seaperated Values (CSV) File from your selected stock symbols.
--timeout TIMEOUT Time (in seconds) to wait for response to requests a new stock symbol data. Default timeout is
0.25 seconds.A longer timeout may cause a long delay to gather all results.
--headers HEADERS [HEADERS ...]
Add aditional parameters to add to your dataset. Please choose from the following: 'ticker',
'Idx', 'PE', 'EPS_ttm', 'Insider_Own', 'Shs_Outstand', 'Perf_Week', 'Market_Cap',
'Forward_PE', 'EPS_next_Y', 'Insider_Trans', 'Shs_Float', 'Perf_Month', 'Income', 'PEG',
'EPS_next_Q', 'Inst_Own', 'Short_Float', 'Perf_Quarter', 'Sales', 'PS', 'EPS_this_Y',
'Inst_Trans', 'Short_Ratio', 'Perf_Half_Y', 'Booksh', 'PB', 'ROA', 'Target_Price',
'Perf_Year', 'Cashsh', 'PC', 'EPS_next_5Y', 'ROE', '52W_Range','Perf_YTD', 'Dividend', 'PFCF',
'EPS_past_5Y', 'ROI', '52W_High', 'Beta', 'Dividend_', 'Quick_Ratio', 'Sales_past_5Y',
'Gross_Margin', '52W_Low', 'ATR', 'Employees', 'Current_Ratio', 'Sales_QQ', 'Oper_Margin',
'RSI_14', 'Volatility', 'Optionable', 'DebtEq', 'EPS_QQ', 'Profit_Margin', 'Rel_Volume',
'Prev_Close', 'Shortable', 'LT_DebtEq', 'Earnings', 'Payout', 'Avg_Volume', 'Price', 'Recom',
'SMA20', 'SMA50', 'SMA200', 'Volume', 'Chg'
To get data for a single stock:
$ python3 scrapeStock.py aapl
Your requested headers are: Idx, Market_Cap, Income, Price
token Idx Market_Cap Income Price
0 AAPL DJIA S&P500 2219.57B 76.31B 130.21
To get data for multiple stocks:
$ python3 scrapeStocks.py aapl googl tsla
Your requested headers are: Idx, Market_Cap, Income, Price
token Idx Market_Cap Income Price
0 AAPL DJIA S&P500 2219.57B 76.31B 130.21
1 GOOGL S&P 500 1614.48B 51.36B 2351.93
2 TSLA S&P 500 645.38B 1.11B 672.37
Save the data as a CSV file with the --csv flag to your current working directory:
$ python3 scrapeStocks.py aapl googl tsla --csv
Your requested headers are: Idx, Market_Cap, Income, Price
token Idx Market_Cap Income Price
0 AAPL DJIA S&P500 2219.57B 76.31B 130.21
1 GOOGL S&P 500 1614.48B 51.36B 2351.93
2 TSLA S&P 500 645.38B 1.11B 672.37
CSV file saved to path: YOUR/CURRENT/WORKING/DIRECTORY/stock_data.csvSet wait time between scraping each stock. A shorter time may lead to higher chances of getting detected by bot detection.
$ python3 scrapeStocks.py aapl amzn --timeout 0.5Add additonal information to your dataset:
Additional headers can be selected from --headers flag.
For example, if you want aditional information such as the PE ration of a stock, Profit Margin, or wether or not a stock has dividends, you can add as parameters.
$ python3 scrapeStocks.py aapl googl tsla --headers PE Volume Profit_Margin Dividend_
Your requested headers are: Idx, Market_Cap, Income, Price, PE, Volume, Profit_Margin, Dividend_
token Idx Market_Cap Income Price PE Volume Profit_Margin Dividend_
0 AAPL DJIA S&P500 2219.57B 76.31B 130.21 29.22 78,339,673 23.50% 0.68%
1 GOOGL S&P 500 1614.48B 51.36B 2351.93 30.38 1,444,167 26.10% -
2 TSLA S&P 500 645.38B 1.11B 672.37 677.79 23,236,272 3.10% -more features to implement: allow fetching data through proxies retrieve stock chart data retrieve stock news data