def calc_ebita(tickers):
print('Fetching EBITA Data from Yahoo for 100 Tickers...')
total_data = {}
for index, ticker in enumerate(tickers):
if index % 1 == 0:
print('Fetching EBITA for %d out of %d' % (index, len(tickers)))
try:
yahoo_financials = YahooFinancials(ticker)
total_data[ticker] = yahoo_financials.get_ebit()
except KeyError:
print('Could not calc. PE for %s' % ticker)
total_data[ticker] = None
continue
return total_data
def fin_metrics(symbols):
"""symbols = tickers of the universe of required number of stocks"""
#sort the list of the symbols and assign it to tickers
tickers = sorted(symbols)
#create the YahooFinancials object as records
records = YahooFinancials(tickers)
#get the financial statements
statements = records.get_financial_stmts('annual',
['income', 'cash', 'balance'])
#get the ebit for each ticker from the financials
ebit = records.get_ebit()
#get the previous stock prices for each stock
share_pr = records.get_prev_close_price()
#create an empty dataframe to hold the final result
results_df = pd.DataFrame()
#create empty lists to hold the metrics
e_yield = []
ebita = []
fcfy = []
roce = []
b2m = []
#get the firt ticker in the list of tickers
first_sym = tickers[0]
#create the indexes for the dataframe of the various financial statments
bs_index = statements['balanceSheetHistory'][first_sym][0].values(
)[0].keys()
is_index = statements['incomeStatementHistory'][first_sym][0].values(
)[0].keys()
cf_index = statements['cashflowStatementHistory'][first_sym][0].values(
)[0].keys()
#create the empty dataframes to hold the various financial statements
bs_df = pd.DataFrame(index=bs_index)
is_df = pd.DataFrame(index=is_index)
cf_df = pd.DataFrame(index=cf_index)
for s in symbols:
#get the components of the various financial statements
bs_result = pd.DataFrame(
statements['balanceSheetHistory'][s][0].values()).T
is_result = pd.DataFrame(
statements['incomeStatementHistory'][s][0].values()).T
cf_result = pd.DataFrame(
statements['cashflowStatementHistory'][s][0].values()).T
#put the components of the various financial statement in the empty dataframes created for them
is_df = pd.concat([is_df, is_result], axis=1, join='outer')
bs_df = pd.concat([bs_df, bs_result], axis=1, join='outer')
cf_df = pd.concat([cf_df, cf_result], axis=1, join='outer')
#create a list of the dataframes of the various financial statemenmts
frames = [bs_df, is_df, cf_df]
#concatenate the dataframe of the various financial statements into a single dataframe
all_fins = pd.concat(frames).fillna(0)
#Assign the list of tickers as the label of the single dataframe columns
all_fins.columns = tickers
for t in all_fins:
#compute the required metrics and assign them to the dataframe created for them
ev = all_fins.loc['commonStock'][t] * share_pr[t] + all_fins.loc[
'shortLongTermDebt'][t] - all_fins.loc['cash'][t]
y = ebit[t] / float(ev)
fcff = all_fins.loc['totalCashFromOperatingActivities'][
t] - all_fins.loc['capitalExpenditures'][t]
fcfs = float(fcff) / all_fins.loc['commonStock'][t]
fcf_yield = float(fcfs) / share_pr[t]
ce = all_fins.loc['totalAssets'][t] - all_fins.loc[
'totalCurrentLiabilities'][t]
roc_employed = float(ebit[t]) / ce
cse = all_fins.loc['totalStockholderEquity'][t] - all_fins.loc[
'otherStockholderEquity'][t]
b2_market = float(cse) / all_fins.loc['commonStock'][t] * share_pr[t]
fcfy.append(fcf_yield)
e_yield.append(y)
ebita.append(ebit[t])
roce.append(roc_employed)
b2m.append(b2_market)
results_df['EBITA'] = ebita
results_df['Earnings Yield'] = e_yield
results_df['Free Cash Flow Yield'] = fcfy
results_df['Return on Capital Employed'] = roce
results_df['Book to Market'] = b2m
results_df.index = tickers
return results_df
import yahoo-finance
from yahoo_finance import Share
yahoo = Share('YHOO')
yahoo.get_price()
>>> print yahoo.get_open()
yahoo_finance.Share("SBI.NSE")
symbol = yahoo_finance.Share("GOOG")
google_data = symbol.get_historical("2019-06-01", "2019-06-30")
google_df = pd.DataFrame(google_data)
# Output data into CSV
google_df.to_csv("/home/username/google_stock_data.csv")
#
pip install yahoofinancials
from yahoofinancials import YahooFinancials
tech_stocks = ['AAPL', 'MSFT', 'INTC']
bank_stocks = ['WFC', 'BAC', 'C']
yahoo_financials_tech = YahooFinancials(tech_stocks)
yahoo_financials_banks = YahooFinancials(bank_stocks)
yahoo_financials_tech
ech_cash_flow_data_an = yahoo_financials_tech.get_financial_stmts('annual', 'cash')
bank_cash_flow_data_an = yahoo_financials_banks.get_financial_stmts('annual', 'cash')
banks_net_ebit = yahoo_financials_banks.get_ebit()
tech_stock_price_data = yahoo_financials_tech.get_stock_price_data()
daily_bank_stock_prices = yahoo_financials_banks.get_historical_price_data('2008-09-15', '2018-09-15', 'daily')
daily_bank_stock_prices
#https://pypi.org/project/yahoofinancials/
