def calculate_ps_metrics(stock):
yahoo_stock = YahooFinancials(stock.symbol)
market_cup = yahoo_stock.get_market_cap()
if stock.target_avg_sales:
multiplier = FilteredStocks.get_multiplier(
stock.target_avg_sales[-1])
target_avg_sales = float(stock.target_avg_sales[:-1]) * multiplier
try:
stock.price_to_sell_ratio = round(
market_cup / target_avg_sales, 2)
stock.ps_to_growth_ratio = round(
stock.price_to_sell_ratio /
float(stock.target_sales_growth[:-1]), 2)
ev = (yahoo_stock.get_key_statistics_data()[stock.symbol]
["enterpriseValue"])
profit_margins = (yahoo_stock.get_key_statistics_data()[
stock.symbol]["profitMargins"])
stock.ev_to_sell_ratio = round(ev / target_avg_sales, 2)
stock.gross_margins = round(
yahoo_stock.get_gross_profit() /
yahoo_stock.get_total_revenue() * 100, 2)
ev_to_profit_ratio = round(
stock.ev_to_sell_ratio / profit_margins, 2)
print("gross_margins : " + str(stock.gross_margins))
print("ev sells : " + str(stock.ev_to_sell_ratio))
print("ev / earnings : " + str(ev_to_profit_ratio))
print("ps_to_growth : " + str(stock.ps_to_growth_ratio))
print("sales targets:" + str(stock.target_sales_growth[:-1]))
print("growth adujsted ev / sells: " + str(
round(
stock.ev_to_sell_ratio /
float(stock.target_sales_growth[:-1]), 2)))
except Exception as e:
print(str(e))
def getMarketData():
tickers = 'AAPL'
financials = YahooFinancials(tickers)
# company_stock_price = financials.get_stock_price_data() #gets stock price information
historical_stock_prices_data = financials.get_historical_price_data(
'2015-11-21', '2020-11-21',
'daily') #gets historical daily stock price of company
# get_Div_data(historical_stock_prices_data[tickers])
get_stock_price_data(historical_stock_prices_data[tickers])
# company_balance_sheet_data_qt = financials.get_financial_stmts('quarterly', 'balance') #get balance sheet
# company_income_statement_data_qt = financials.get_financial_stmts('quarterly', 'income') #get income statement
company_key_statistics_data = financials.get_key_statistics_data(
) #includes profit margins, forward eps, yearly change etc.
# get_forward_pe(company_key_statistics_data[tickers])
# get_trailing_eps(company_key_statistics_data[tickers])
# get_foward_eps(company_key_statistics_data[tickers])
# get_ytdReturn(company_key_statistics_data[tickers])
company_earnings_data = financials.get_stock_earnings_data(
) #historical eps only for 1 year span
# get_earnings_data(company_earnings_data[tickers])
company_dividend_yield = financials.get_dividend_yield(
) #current dividends yield
company_dividend = financials.get_dividend_rate() #current dividends rate
company_avg_div_yield_1year = financials.get_annual_avg_div_yield(
) #average 1 year div yield
company_avg_div_yield_5year = financials.get_five_yr_avg_div_yield(
) #average 5 year div yield
company_eps = financials.get_earnings_per_share() #current eps
company_pe = financials.get_pe_ratio() #current pe ratio
company_beta = financials.get_beta() #current beta
company_current_stock_price = financials.get_current_price(
) #current stock price
company_revenue = financials.get_total_revenue() #current company revenue
company_operating_income = financials.get_operating_income(
) #current company operating income
company_net_income = financials.get_net_income() #current net income
company_yearly_high = financials.get_yearly_high() #get yearly high
company_yearly_low = financials.get_yearly_low() #get yearly low
company_moving_50 = financials.get_50day_moving_avg(
) #50 day moving average of stock
company_moving_200 = financials.get_200day_moving_avg(
) #200 day moving average of stock
async def stock(ctx, ticker, info):
yahoo_financials = YahooFinancials(ticker)
if (info == "current"):
await ctx.send(ticker.upper() + " current share price: $" +
str(yahoo_financials.get_current_price()))
if (info == "open"):
await ctx.send(ticker.upper() + " share price at opening: $" +
str(yahoo_financials.get_open_price()))
if (info == "prevclose"):
await ctx.send(ticker.upper() + " share priced at previous close: $" +
str(yahoo_financials.get_prev_close_price()))
if (info == "cap"):
await ctx.send(ticker.upper() + " market cap: $" +
str("{:,}".format(yahoo_financials.get_market_cap())))
if (info == "dailylow"):
await ctx.send(ticker.upper() + " daily low: $" +
str(yahoo_financials.get_daily_low()))
if (info == "dailyhigh"):
await ctx.send(ticker.upper() + " daily high: $" +
str(yahoo_financials.get_daily_high()))
if (info == "yearlow"):
await ctx.send(ticker.upper() + " yearly low: $" +
str(yahoo_financials.get_yearly_low()))
if (info == "yearhigh"):
await ctx.send(ticker.upper() + " yearly high: $" +
str(yahoo_financials.get_yearly_high()))
if (info == "rev"):
await ctx.send(ticker.upper() + " total revenue: $" +
str("{:,}".format(yahoo_financials.get_total_revenue()))
)
if (info == "net"):
await ctx.send(ticker.upper() + " net income: $" +
str("{:,}".format(yahoo_financials.get_net_income())))
if (info == "op"):
await ctx.send(
ticker.upper() + " operating income: $" +
str("{:,}".format(yahoo_financials.get_operating_income())))
if (info == "profit"):
await ctx.send(ticker.upper() + " gross profit: $" +
str("{:,}".format(yahoo_financials.get_gross_profit())))
from yahoofinancials import YahooFinancials
yahoo_financials = YahooFinancials(list_names)
# pullling operating income data for most recent year reported
minilist_opinc = yahoo_financials.get_operating_income()
minilist_opinc_df = pd.Series(minilist_opinc).to_frame('op_inc')
print(minilist_opinc_df)
# pulling operating expense data for most recent year reported
minilist_opex = yahoo_financials.get_total_operating_expense()
minilist_opex_df = pd.Series(minilist_opex).to_frame('op_ex')
print(minilist_opex_df)
# pulling total revenue data for most recent year reported
minilist_rev = yahoo_financials.get_total_revenue()
minilist_rev_df = pd.Series(minilist_rev).to_frame('rev')
print(minilist_rev_df)
mergeddf = minilist_opinc_df.join(
minilist_opex_df
) # merging operating income dataframe and operating expense dataframe
mergeddf = mergeddf.join(minilist_rev_df) # joining revenue dataframe
mergeddf['MTC'] = mergeddf['op_inc'] / mergeddf[
'op_ex'] # generating col for markup on total cost
mergeddf['OM'] = mergeddf['op_inc'] / mergeddf[
'rev'] # generating col for operating margin
mergeddf.drop(
['op_inc', 'op_ex'], axis=1,