def populate_stocks():
ticker_list = []
for i in range(1, 6):
ticker_list.extend(
list(map(''.join, product(ascii_uppercase, repeat=i))))
for tick in ticker_list:
print("Checking " + tick + ".")
if Share(tick).get_name():
try:
if Stock.objects.get(ticker=tick):
print(tick + " already exists. Skipping database entry.")
continue
except:
stock_info = Share(tick)
stock = Stock(
name=stock_info.get_name(),
ticker=tick,
price=stock_info.get_price(),
price_target=stock_info.get_one_yr_target_price(),
is_bullish=None,
last_updated=timezone.now())
stock.save()
print(tick + " " + Share(tick).get_name())
def get(self, request, symbol):
try:
stock = Stock.objects.get(ticker=symbol)
self.update_stock(stock)
except ObjectDoesNotExist:
stock_info = Share(symbol)
stock = Stock(
name=stock_info.get_name(),
ticker=symbol,
price=stock_info.get_price(),
price_target=stock_info.get_one_yr_target_price(),
is_bullish=None,
last_updated=timezone.now()
)
stock.save()
try:
stock = Stock.objects.get(ticker=symbol)
except ObjectDoesNotExist:
stock = "Still can not pull stock."
return render(request, self.template_name, {
'stock': stock,
'stock_tracked': self.stock_tracked(request, symbol)
})
def update_stock(self, stock):
if stock.last_updated < datetime.date.today():
try:
stock_info = Share(stock.ticker)
stock.price = stock_info.get_price()
stock.price_target = stock_info.get_one_yr_target_price()
stock.last_updated = timezone.now()
stock.save()
except:
pass
def portfolio_stocks(stocks): tickers = [] index = 0 for stock in stocks: names = [ 'Company Name', 'Ticker', 'Price', 'Market Cap', 'P/E Ratio', 'Earnings Yield', 'Div Yield', '50 Day MA', '200 Day MA', 'Price Target' ] ticker = Share(stock) comp_name = ticker.get_name() #company name tick = stock #ticker price = ticker.get_price() #price market_cap = ticker.get_market_cap() #market_cap pe = ticker.get_price_earnings_ratio() #gets pe as a string pe_two = float(pe) if pe else 0 #returns a float of the p/e if there is a result, otherwise returns 0 final_pe = pe_two if float(pe_two) > 0 else 0 #returns pe_two if > 0 else returns 0 EPS = ticker.get_EPS_estimate_current_year() # gets eps as a string final_eps = EPS if EPS else 0 #returns eps if there is a result, else returns 0 earn_yield = float(final_eps)/float(price) #returns float of earnings yield pos_ey = earn_yield if earn_yield > 0 else 0 #turns negitive numbers to 0 print(tick, 'earn yield', pos_ey) ey = round(pos_ey*100, 2) #returns in % format div = ticker.get_dividend_yield() #returns div in string final_div = 0 if div == None else float(div) #if no result returns 0 else returns float of div fifty = ticker.get_50day_moving_avg() #returns as string short_fifty = round(float(fifty), 2) #returns div with 2 decimal places two_hundred = ticker.get_200day_moving_avg() #returns as string short_two = round(float(two_hundred), 2) #returns float with 2 decimal places target = ticker.get_one_yr_target_price() #returns as string short_target = round(float(target), 2) values = [comp_name, tick, price, market_cap, final_pe, ey, final_div, short_fifty, short_two, short_target] final_values = list(zip(names, values)) index += 1 tickers.append(final_values) return tickers
def getAllStockData(ticker):
'''Get a few random tickers.'''
stock = Share(ticker)
stock.refresh()
data = {
'name': stock.get_name(),
'price': stock.get_price(),
'change': stock.get_change(),
'volume': stock.get_volume(),
'prev_close': stock.get_prev_close(),
'open': stock.get_open(),
'avg_daily_volume': stock.get_avg_daily_volume(),
'stock_exchange': stock.get_stock_exchange,
'market_cap': stock.get_market_cap(),
'book_value': stock.get_book_value(),
'ebitda': stock.get_ebitda(),
'dividend_share': stock.get_dividend_share(),
'dividend_yield': stock.get_dividend_yield(),
'earnings_share': stock.get_earnings_share(),
'days_high': stock.get_days_high(),
'days_low': stock.get_days_low(),
'year_high': stock.get_year_high(),
'year_low': stock.get_year_low(),
'50day_moving_avg': stock.get_50day_moving_avg(),
'200day_moving_avg': stock.get_200day_moving_avg(),
'price_earnings_ratio': stock.get_price_earnings_ratio(),
'price_earnings_growth_ratio': stock.get_price_earnings_growth_ratio(),
'get_price_sales': stock.get_price_sales(),
'get_price_book': stock.get_price_book(),
'get_short_ratio': stock.get_short_ratio(),
'trade_datetime': stock.get_trade_datetime(),
'percent_change_from_year_high': stock.get_percent_change_from_year_high(),
'percent_change_from_year_low': stock.get_percent_change_from_year_low(),
'change_from_year_low': stock.get_change_from_year_low(),
'change_from_year_high': stock.get_change_from_year_high(),
'percent_change_from_200_day_moving_average': stock.get_percent_change_from_200_day_moving_average(),
'change_from_200_day_moving_average': stock.get_change_from_200_day_moving_average(),
'percent_change_from_50_day_moving_average': stock.get_percent_change_from_50_day_moving_average(),
'change_from_50_day_moving_average': stock.get_change_from_50_day_moving_average(),
'EPS_estimate_next_quarter': stock.get_EPS_estimate_next_quarter(),
'EPS_estimate_next_year': stock.get_EPS_estimate_next_year(),
'ex_dividend_date': stock.get_ex_dividend_date(),
'EPS_estimate_current_year': stock.get_EPS_estimate_current_year(),
'price_EPS_estimate_next_year': stock.get_price_EPS_estimate_next_year(),
'price_EPS_estimate_current_year': stock.get_price_EPS_estimate_current_year(),
'one_yr_target_price': stock.get_one_yr_target_price(),
'change_percent_change': stock.get_change_percent_change(),
'divended_pay_date': stock.get_dividend_pay_date(),
'currency': stock.get_currency(),
'last_trade_with_time': stock.get_last_trade_with_time(),
'days_range': stock.get_days_range(),
'years_range': stock.get_year_range()
}
return data
def update_stocks(self, request):
user = User.objects.get(username=request.user)
for stock in list(user.stocks.order_by('name')):
if stock.last_updated < datetime.date.today():
try:
stock_info = Share(stock.ticker)
stock.price = stock_info.get_price()
stock.price_target = stock_info.get_one_yr_target_price()
stock.last_updated = timezone.now()
stock.save()
except:
pass
self.user_stock_list.append(stock)
def get_symbol_yahoo_stats_yql(symbols, exclude_name=False):
"""
Get the symbols' basic statistics from Yahoo Finance.
Input:
symbols - a list of symbol strings, e.g. ['AAPL']
Output: stats in Pandas DataFrame.
This function is ported from pandas_datareader/yahoo/components.py
"""
sym_list = str2list(symbols)
if sym_list == None:
return DataFrame()
# Yahoo Finance tags, refer to http://www.financialwisdomforum.org/gummy-stuff/Yahoo-data.htm
tags = ['Symbol']
if not exclude_name:
tags += ['Name']
tags += ['Exchange', 'MarketCap', 'Volume', 'AverageDailyVolume', 'BookValue', 'P/E', 'PEG', 'Price/Sales',
'Price/Book', 'EBITDA', 'EPS', 'EPSEstimateNextQuarter', 'EPSEstimateCurrentYear', 'EPSEstimateNextYear',
'OneyrTargetPrice', 'PriceEPSEstimateCurrentYear', 'PriceEPSEstimateNextYear', 'ShortRatio',
'Dividend/Share', 'DividendYield', 'DividendPayDate', 'ExDividendDate']
lines = []
for sym in sym_list:
stock = Share(sym)
line = [sym]
if not exclude_name:
line += [stock.get_name()]
line += [stock.get_stock_exchange(), str2num(stock.get_market_cap(), m2b=True),
str2num(stock.get_volume()), str2num(stock.get_avg_daily_volume()), str2num(stock.get_book_value()),
str2num(stock.get_price_earnings_ratio()), str2num(stock.get_price_earnings_growth_ratio()),
str2num(stock.get_price_sales()), str2num(stock.get_price_book()), str2num(stock.get_ebitda()),
str2num(stock.get_earnings_share()), str2num(stock.get_EPS_estimate_next_quarter()),
str2num(stock.get_EPS_estimate_current_year()), str2num(stock.get_EPS_estimate_next_year()),
str2num(stock.get_one_yr_target_price()), str2num(stock.get_price_EPS_estimate_current_year()),
str2num(stock.get_price_EPS_estimate_next_year()), str2num(stock.get_short_ratio()),
str2num(stock.get_dividend_share()), str2num(stock.get_dividend_yield()), stock.get_dividend_pay_date(),
stock.get_ex_dividend_date()]
lines.append(line)
stats = DataFrame(lines, columns=tags)
stats = stats.drop_duplicates()
stats = stats.set_index('Symbol')
return stats
def stock_info(ticker):
obj = session['objective']
stock_list = session['stock_list']
time = session['time']
stock = Share(ticker)
name = stock.get_name()
price = stock.get_price()
pe = stock.get_price_earnings_ratio()
final_pe = 0 if not pe else pe
EPS = float(stock.get_EPS_estimate_current_year())
earn_yield = 0 if float(EPS) <= 0 else float(price) / EPS
final_yield = '%.2f' % earn_yield
div = stock.get_dividend_yield()
final_div = 0 if div == None else div
target = stock.get_one_yr_target_price()
fifty = stock.get_50day_moving_avg()
two_hundred = stock.get_200day_moving_avg()
info = Stock(name, price, pe, final_yield, final_div, target, fifty,
two_hundred)
beta = Beta(ticker)
return render_template("stock-info.html",
name=info.name,
num_beta=beta.calculate_beta(),
beta=beta.compare_beta(),
pe_num=final_pe,
pe=info.compare_pe(),
ey_num=final_yield,
ey=info.compare_earn_yield(),
div_num=final_div,
div=info.compare_div(),
fifty=fifty,
two=two_hundred,
ma_compare=info.compare_ma(),
target_num=target,
target=info.compare_target(),
obj=obj,
time=time,
stock=stock_list)
def stock_quote_get():
print(request.args.get('symbol'))
symbol = str(request.args.get('symbol'))
# get all the relevant data from the Yahoo Finance API
stock = Share(symbol)
stock_name = stock.get_name()
stock_symbol = stock.symbol
stock_price = stock.get_price()
stock_change = stock.get_change()
stock_change_pct = stock.get_percent_change()
prev_close = stock.get_prev_close()
open = stock.get_open()
day_range = stock.get_days_range()
year_range = stock.get_year_range()
volume = stock.get_volume()
avg_volume = stock.get_avg_daily_volume()
market_cap = stock.get_market_cap()
pe_ratio = stock.get_price_earnings_ratio()
eps = stock.get_earnings_share()
dividend = stock.get_dividend_share()
dividend_yld = stock.get_dividend_yield()
dividend_ex_date = stock.get_ex_dividend_date()
yr_target = stock.get_one_yr_target_price()
historical = stock.get_historical('2017-01-01',
date.isoformat(date.today()))
# put the data into the DynamoDB database
table = dynamodb.Table('Stocks')
response = table.put_item(
Item={
'symbol': symbol,
'date': date.isoformat(date.today()),
'prev_close': prev_close,
'open': open,
'day_range': day_range,
'year_range': year_range,
'volume': volume,
'avg_volume': avg_volume,
'market_cap': market_cap,
'pe_ratio': pe_ratio,
'eps': eps,
'dividend': dividend,
'dividend_yld': dividend_yld,
'dividend_ex_date': dividend_ex_date,
'yr_target': yr_target,
})
close_history = []
for point in historical:
close_date = point['Date']
close_date = int(
time.mktime(datetime.strptime(close_date, "%Y-%m-%d").timetuple()))
close_price = point['Adj_Close']
close_price = float(close_price)
close_history.append([close_date, close_price])
return render_template("stock/stock_detail.html",
stock_name=stock_name,
stock_symbol=stock_symbol,
stock_price=stock_price,
stock_change=stock_change,
stock_change_pct=stock_change_pct,
prev_close=prev_close,
open=open,
day_range=day_range,
year_range=year_range,
volume=volume,
avg_volume=avg_volume,
market_cap=market_cap,
pe_ratio=pe_ratio,
eps=eps,
dividend=dividend,
dividend_yld=dividend_yld,
dividend_ex_date=dividend_ex_date,
yr_target=yr_target,
close_history=close_history)
print "get_percent_change_from_year_low:", tesla.get_percent_change_from_year_low( ) print "get_change_from_year_low:", tesla.get_change_from_year_low() print "get_change_from_year_high:", tesla.get_change_from_year_high() print "get_percent_change_from_200_day_moving_average:", tesla.get_percent_change_from_200_day_moving_average( ) print "get_change_from_200_day_moving_average:", tesla.get_change_from_200_day_moving_average( ) print "get_percent_change_from_50_day_moving_average:", tesla.get_percent_change_from_50_day_moving_average( ) print "get_change_from_50_day_moving_average:", tesla.get_change_from_50_day_moving_average( ) print "get_EPS_estimate_next_quarter:", tesla.get_EPS_estimate_next_quarter() print "get_EPS_estimate_next_year:", tesla.get_EPS_estimate_next_year() print "get_ex_dividend_date:", tesla.get_ex_dividend_date() print "get_EPS_estimate_current_year:", tesla.get_EPS_estimate_current_year() print "get_price_EPS_estimate_next_year:", tesla.get_price_EPS_estimate_next_year( ) print "get_price_EPS_estimate_current_year:", tesla.get_price_EPS_estimate_current_year( ) print "get_one_yr_target_price:", tesla.get_one_yr_target_price() print "get_change_percent_change:", tesla.get_change_percent_change() print "get_dividend_pay_date:", tesla.get_dividend_pay_date() print "get_currency:", tesla.get_currency() print "get_last_trade_with_time:", tesla.get_last_trade_with_time() print "get_days_range:", tesla.get_days_range() print "get_year_range:", tesla.get_year_range() # http://stackoverflow.com/questions/24233385/pulling-yahoo-finance-data-using-python # http://chartapi.finance.yahoo.com/instrument/1.0/TSLA/chartdata;type=quote;range=1y/csv
