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
def analyze_stock(pstock):
days = 1000
now = datetime.datetime.now()
year_before = now - datetime.timedelta(days=days)
stock = pstock.stock
yf = YahooFinancials(stock.name)
stock_analysis = StockAnalysis(yf)
# update important fields
stock.last_price = convert_number(yf.get_current_price())
stock.price_sales_ratio = convert_number(yf.get_price_to_sales())
stock.dividend_yield = convert_number(yf.get_annual_avg_div_yield())
# historical_prices = yf.get_historical_price_data(stock.name,
# year_before.strftime('%Y%m%d'), now.strftime('%Y%m%d'), 'daily')
#[['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Adj Clos']
# history = []
# historical_prices = historical_prices[1:]
# has_history = True
# for p in historical_prices:
# # error index out of range here
# if(len(p) == 1):
# has_history = False
# break
# close_v = float(p[4])
# date = datetime.datetime.strptime(p[0],'%Y-%m-%d')
# history.append({"date":date, "close_value":close_v})
# price_trends = []
# volatilities = [
# {'days':100},
# {'days':300},
# {'days':600},
# ]
# if has_history:
# # today first
# history = sorted(history, key=lambda p: p['date'])
# assert history[1]['date'] > history[0]['date']
# for v in volatilities:
# if len(history) < v['days']:
# v['days'] = len(history)
# v['volatility'] = round(calculate_historical_volatility(history[-v['days']:]), 2)
# v['start_date'] = history[-v['days']:][0]['date']
# stock_analysis.volatility = volatilities[1]['volatility']
# stock.volatility = stock_analysis.volatility
# start = 0
# interval = int(len(history) / 5.0)
# while len(history) > (start + interval) and interval > 0:
# trend = calculate_historical_price_trend(history[start:start+interval])
# price_trends.append(trend)
# start = start + interval
# #if len(history) < (start + interval):
# # interval = len(history) - start - 1
# trend = calculate_historical_price_trend(history)
# price_trends.append(trend)
# stock_analysis.trend = trend
stock.value_score = stock_analysis.value_score_analysis()
stock.save()
return {
#'price_trends':price_trends,
'stock':stock,
#'volatilities':volatilities,
'stock_analysis':stock_analysis,
'days':days,
#'history': history
}