def update_shares(request, portfolio_pk):
portfolio = Portfolio.objects.get(pk=portfolio_pk)
check_ownership(request, portfolio)
pstocks = PortfolioStock.objects.filter(portfolio=portfolio)
for pstock in pstocks:
stock = pstock.stock
yf = YahooFinancials(quote_name)
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_dividend_yield())
stock.save()
return http.HttpResponseRedirect('..')
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
}