def portfolio(request, portfolio_pk):
portfolio = Portfolio.objects.get(pk=portfolio_pk)
check_ownership(request, portfolio)
stock_not_found = False
quote_name = request.POST.get('quote', None)
if quote_name:
quote_name = quote_name.upper()
answer = ystockquote.get(quote_name)
if answer["stock_exchange"] != '"N/A"':
try:
stock = Stock.objects.get(name=quote_name)
except Stock.DoesNotExist:
stock = Stock.objects.create(
name=quote_name,
stock_exchange=answer['stock_exchange'],
market_cap=convert_number(answer['market_cap']),
last_price=convert_number(answer['price']))
PortfolioStock.objects.get_or_create(stock=stock,
portfolio=portfolio)
else:
stock_not_found = True
stocks = PortfolioStock.objects.filter(
portfolio=portfolio).order_by("-stock__value_score")
operations = Operation.objects.filter(
portfolio_stock__portfolio=portfolio)[0:10]
total_value, total_spent, profit = portfolio.balance_sheet()
if total_spent != 0:
profit_purcent = round(profit / total_spent * 100, 2)
else:
profit_purcent = 0
total_cash_value = total_value + portfolio.cash
c = {
'quote_name': quote_name,
'stock_not_found': stock_not_found,
'portfolio': portfolio,
'stocks': stocks,
'operations': operations,
'total_value': total_value,
'total_spent': total_spent,
'profit': profit,
'profit_purcent': profit_purcent,
'total_cash_value': total_cash_value,
}
c.update(csrf(request))
return render_to_response('portfolio.html', c)
def portfolio(request, portfolio_pk):
portfolio = Portfolio.objects.get(pk=portfolio_pk)
check_ownership(request, portfolio)
stock_not_found = False
quote_name = request.POST.get('quote', None)
if quote_name:
quote_name = quote_name.upper()
answer = ystockquote.get(quote_name)
if answer["stock_exchange"] != '"N/A"':
try:
stock = Stock.objects.get(name=quote_name)
except Stock.DoesNotExist:
stock = Stock.objects.create(
name=quote_name,
stock_exchange=answer['stock_exchange'],
market_cap=convert_number(answer['market_cap']),
last_price=convert_number(answer['price'])
)
PortfolioStock.objects.get_or_create(stock=stock,
portfolio=portfolio)
else:
stock_not_found = True
stocks = PortfolioStock.objects.filter(portfolio=portfolio).order_by("-stock__value_score")
operations = Operation.objects.filter(portfolio_stock__portfolio=portfolio)[0:10]
total_value, total_spent, profit = portfolio.balance_sheet()
if total_spent != 0:
profit_purcent = round(profit / total_spent * 100, 2)
else:
profit_purcent = 0
total_cash_value = total_value + portfolio.cash
c = {
'quote_name': quote_name,
'stock_not_found': stock_not_found,
'portfolio':portfolio,
'stocks': stocks,
'operations':operations,
'total_value':total_value,
'total_spent':total_spent,
'profit':profit,
'profit_purcent':profit_purcent,
'total_cash_value':total_cash_value,
}
c.update(csrf(request))
return render_to_response('portfolio.html', c)
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
answer = ystockquote.get(stock.name)
stock.last_price = convert_number(answer['price'])
stock.price_sales_ratio = convert_number(answer['price_sales_ratio'])
stock.dividend_yield = convert_number(answer['dividend_yield'])
stock.save()
return http.HttpResponseRedirect('..')
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 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
answer = ystockquote.get(stock.name)
stock.last_price = convert_number(answer['price'])
stock.price_sales_ratio = convert_number(answer['price_sales_ratio'])
stock.dividend_yield = convert_number(answer['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
infos = ystockquote.get(stock.name)
stock_analysis = StockAnalysis(infos)
# update important fields
stock.last_price = convert_number(infos['price'])
stock.price_sales_ratio = convert_number(infos['price_sales_ratio'])
stock.dividend_yield = convert_number(infos['dividend_yield'])
historical_prices = ystockquote.legacy.get_historical_prices(stock.name,
year_before.strftime('%Y%m%d'), now.strftime('%Y%m%d'))
#[['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,
'infos':infos,
'volatilities':volatilities,
'stock_analysis':stock_analysis,
'days':days,
'history': history
}
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
}
def analyze_stock(pstock):
days = 1000
now = datetime.datetime.now()
year_before = now - datetime.timedelta(days=days)
stock = pstock.stock
infos = ystockquote.get(stock.name)
stock_analysis = StockAnalysis(infos)
# update important fields
stock.last_price = convert_number(infos['price'])
stock.price_sales_ratio = convert_number(infos['price_sales_ratio'])
stock.dividend_yield = convert_number(infos['dividend_yield'])
historical_prices = ystockquote.legacy.get_historical_prices(
stock.name, year_before.strftime('%Y%m%d'), now.strftime('%Y%m%d'))
#[['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,
'infos': infos,
'volatilities': volatilities,
'stock_analysis': stock_analysis,
'days': days,
'history': history
}
