from finviz.screener import Screener
filters = ['exch_nasd', 'idx_sp500'] # Shows companies in NASDAQ which are in the S&P500
# Get the first 50 results sorted by price ascending
stock_list = Screener(filters=filters, order='price')
# Export the screener results to .csv
stock_list.to_csv()
# Create a SQLite database
stock_list.to_sqlite()
for stock in stock_list[9:19]: # Loop through 10th - 20th stocks
print(stock['Ticker'], stock['Price']) # Print symbol and price
# Add more filters
stock_list.add(filters=['fa_div_high']) # Show stocks with high dividend yield
# or just stock_list(filters=['fa_div_high'])
# Print the table into the console
print(stock_list)
#!/usr/bin/python3
from finviz.screener import Screener
# Get dict of available filters
# filters dict contains the corresponding filter tags
filters = Screener.load_filter_dict()
some_filters = [filters["PEG"]["Under 1"], filters["Exchange"]["AMEX"]]
stock_list = Screener(filters=some_filters, order="ticker")
print(stock_list)
# Use raw filter tags in a list
# filters = ['geo_usa']
filters = ["idx_sp500"] # Shows companies in the S&P500
print("Screening stocks...")
stock_list = Screener(filters=filters, order="ticker")
print(stock_list)
print("Retrieving stock data...")
stock_data = stock_list.get_ticker_details()
print(stock_data)
# Export the screener results to CSV file
stock_list.to_csv("sp500.csv")
# Create a SQLite database
# stock_list.to_sqlite("sp500.sqlite")
def main():
parser = argparse.ArgumentParser(description='scrap finviz screener')
parser.add_argument('-output', type=str, help='output file')
parser.add_argument('-output_prefix',
type=str,
default='../stock_data/raw_daily_finviz/finviz_',
help='prefix of the output file')
parser.add_argument('-use_bs4_scrapper',
type=bool,
default=True,
help='Use my old bs4 scraper')
parser.add_argument('-date',
type=str,
default=str(datetime.date.today()),
help='Specify the date')
parser.add_argument('-filter',
type=str,
action='append',
help='filters apply to the screener')
parser.add_argument('-tab',
type=str,
action='append',
help='tabs to the scrap')
parser.add_argument('-delay',
type=int,
help='delay in sec between each URL request')
parser.add_argument('-drop_col',
type=str,
action='append',
default=[],
help='remove columns')
args = parser.parse_args()
if args.filter is None:
args.filter = ['f=cap_microover', 'f=cap_microunder']
if args.delay is not None:
global scrap_delay
scrap_delay = args.delay
# check is the market closed today
if is_market_close(args.date):
print('The market is closed today')
return
if args.output is None:
filename = args.output_prefix + args.date + '.csv'
else:
filename = args.output
# scrap the data
if args.use_bs4_scrapper:
# use my old code
df_filters = []
for filter in args.filter:
df_filters.append(scrap_finviz(filter, args.tab))
df = pd.concat(df_filters)
else:
# use the finviz package
stock_list = Screener(filters=args.filter)
df = pd.read_csv(StringIO(stock_list.to_csv()))
df = df.loc[~df.index.duplicated(), ~df.columns.duplicated()]
df.drop(columns=['No.'] + args.drop_col, inplace=True)
df.insert(0, 'Date', args.date, True)
df.to_csv(filename)
#filters = ['geo_usa']
filters = ['fa_div_pos'] # Shows companies in the S&P500
print("Filtering stocks..")
stock_list = Screener(filters=filters, order='ticker')
print("Parsing every stock..")
stock_list.get_ticker_details()
#df = pd.DataFrame(data=stock_list)
#print(df.head())
# Export the screener results to CSV file
stock_list.to_csv(r'C:/Users/Jacob Steenhuysen/Downloads/all_world_yields6.csv')
df = pd.read_csv(r'C:/Users/Jacob Steenhuysen/Downloads/all_world_yields6.csv')
tickers_list = df['Ticker'].tolist()
data = pd.DataFrame(columns=tickers_list)
import yfinance as yf
#for ticker in tickers_list:
# data[ticker] = yf.download(ticker, period="5d", interval="1d") ["Close"]
for ticker in tickers_list:
data[ticker] = yf.download(ticker, start=datetime.now()-timedelta(days=366), end=date.today()) ["Adj Close"]
foldername = os.path.basename(dirpath)
print("Directory name is : " + foldername)
# Get the first 50 results sorted by price ascending
stk_overview_list = Screener(filters=filters,
order='-change',
table='Overview')
stk_performance_list = Screener(filters=filters,
order='-change',
table='Performance')
stk_technical_list = Screener(filters=filters,
order='-change',
table='Technical')
# Export the screener results to .csv
stk_technical_list.to_csv()
# Create a SQLite database
#stock_list.to_sqlite()
# Add more filters
#stock_list.add(filters=['fa_div_high']) # Show stocks with high dividend yield
# or just stock_list(filters=['fa_div_high'])
# Print the table into the console
#print(stk_overview_list)
#print(finviz.get_stock('AAPL'))
for stock in stk_overview_list[:5]: # Loop through 10th - 20th stocks
print(stock) # Print symbol and price
#!/usr/bin/python3
from finviz.screener import Screener
# Get dict of available filters
# filters dict contains the corresponding filter tags
filters = Screener.load_filter_dict()
some_filters = [filters['PEG']['Under 1'], filters['Exchange']['AMEX']]
stock_list = Screener(filters=some_filters, order='ticker')
# Use raw filter tags in a list
# filters = ['geo_usa']
filters = ['idx_sp500'] # Shows companies in the S&P500
print("Filtering stocks..")
stock_list = Screener(filters=filters, order='ticker')
print("Parsing every stock..")
stock_list.get_ticker_details()
# Export the screener results to CSV file
stock_list.to_csv('sp500.csv')
# Create a SQLite database
# stock_list.to_sqlite('sp500.sqlite')
from finviz.screener import Screener
import pyrebase
filters = ['exch_amex', 'sh_relvol_o0.5'
] # Shows companies in NASDAQ which are in the S&P500
signal = 'ta_mostactive'
stock_list = Screener(
filters=filters, table='Performance',
signal=signal) # Get the performance table and sort it by price ascending
print(stock_list)
# Export the screener results to .csv
stock_list.to_csv("AMEX_stock.csv")
filters = ['exch_nasd', 'sh_relvol_o2.5'
] # Shows companies in NASDAQ which are in the S&P500
signal = 'ta_mostactive'
stock_list = Screener(
filters=filters, table='Performance',
signal=signal) # Get the performance table and sort it by price ascending
print(stock_list)
# Export the screener results to .csv
stock_list.to_csv("NASDAQ_stock.csv")
filters = ['exch_nyse',
'sh_relvol_o1'] # Shows companies in NASDAQ which are in the S&P500
signal = 'ta_mostactive'
stock_list = Screener(
filters=filters, table='Performance',
signal=signal) # Get the performance table and sort it by price ascending
print(stock_list)
# Export the screener results to .csv
AUTH_TOKEN = "--------------------------------"
#filters = ['exch_nasd','ta_change_u' ] # Shows companies in the S&P500
filters = ['ta_change_u'] # Shows companies in the S&P500
print("Filtering stocks..")
#stock_list = Screener(filters=filters,order='-volume,-change',signal='Most Volatile')
stock_list = Screener(filters=filters,
order='-volume',
signal='ta_mostvolatile')
stock_list.get_ticker_details()
# Export the screener results to CSV file
stock_list.to_csv('jimmy.csv')
result = [
'These are stocks based on highest % change since this morning, GREEN stocks only'
]
d = {}
def main():
with open('jimmy.csv', 'r') as read_obj:
head = [next(read_obj) for x in range(10)]
csv_reader = reader(head)
for row in csv_reader:
print(row[1], row[8], row[9], row[10])
result.append(row[1])
else: # init file
PanPrint.pprint("+ " + yahoo_file + "\tcreating new file")
f = open(yahoo_file, "w")
f.write(yahoo_csv_stocks.replace('\r\n', '\n'))
f.close()
return yahoo_file
##stock_filters = ['exch_nasd', 'idx_sp500'] # Shows companies in NASDAQ which are in the S&P500
# stock_filters = ['exch_nyse']
stock_filters = 'exch_nyse'
# Get the first 50 results sorted by price ascending
stock_orderby = 'pe' # 'pe' 'price'
stock_datetim = str(PanDateTime.datetime.now().strftime('%Y%m%d'))
stock_list = PanScreener(filters=stock_filters, order=stock_orderby)
stock_csv_ = stock_list.to_csv()
stock_file = set_file(stock_datetim, stock_filters, stock_orderby, stock_csv_)
PanPrint.pprint(stock_file)
dtype_dic= { 'No.':'int64', 'Ticker':'str', 'Company':'str', 'Sector':'str', 'Industry':'str', 'Country':'str',
'Market Cap':'str', 'P/E':'float64', 'Price':'float64', 'Change':'str', 'Volume':'float64'}
myDf = Pan.read_csv(stock_file, keep_default_na = False, dtype = {'Volume' : 'str'})
myDf = myDf.loc[myDf['P/E'] != '-']
myDf = myDf.loc[myDf['Market Cap'] != '-']
myDf['Volume'] = myDf['Volume'].str.replace(',', '')
myDf['Market Cap'] = (myDf['Market Cap'].replace(r'[KMB]+$', '', regex=True).astype(float) * myDf['Market Cap'].str.extract(r'[\d\.]+([KMB]+)', expand=False).fillna(1).replace(['K','M', 'B'], [10**3, 10**6, 10**9]).astype(int))
myDf = myDf.astype({'Market Cap' : 'int64' , 'P/E':'float64', 'Price':'float64', 'Volume': 'int64'})
PanPrint.pprint(myDf.head())
PanPrint.pprint(myDf.dtypes)
PanPrint.pprint(myDf.info())
PanPrint.pprint(myDf['P/E'].describe(include='all'))
PanPrint.pprint(myDf['Price'].describe(include='all'))
