def collect_stock_data(symbol):
""" Collect stock data for stock with symbol symbol
:param symbol: symbol of stock to collect data for
:return: dictionary containing stock data
"""
stock_data = dict()
stock = Share(symbol)
# check if symbol was for a valid stock
name = stock.get_name()
if name == 'N/A':
return None
stock_data['symbol'] = symbol
stock_data['name'] = name
# get all data between today and start of 21st century
start_date = time.strftime("2000-01-01")
end_date = time.strftime("%Y-%m-%d")
stock_data['historical_data'] = stock.get_historical(start_date, end_date)
# get dividend information
stock_data['dividend_per_share'] = stock.get_dividend_share()
stock_data['dividend_yield'] = stock.get_dividend_yield()
# get volume information
stock_data['avg_daily_volume'] = stock.get_avg_daily_volume()
# primary key is the stock's symbol
stock_data['_id'] = symbol
return stock_data
def set_ETF_data():
etf_data = []
for index, etf_symbol in enumerate(settings.ETF_MASTER_LIST):
etf_dict = {
'model': 'portfolio.ETF',
'pk': index + 1,
'fields': {},
}
fund = Share(etf_symbol)
fields = {
'name': fund.get_name(),
'symbol': etf_symbol,
'last_trade': fund.get_price(),
'dividend_yield': fund.get_dividend_yield(),
'absolute_change': fund.get_change(),
'percentage_change': fund.get_percent_change(),
'year high': fund.get_year_high(),
'year low': fund.get_year_low(),
'50 day moving average': fund.get_50day_moving_avg(),
'200 day moving average': fund.get_200day_moving_avg(),
'average_daily_volume': fund.get_avg_daily_volume()
}
etf_dict['fields'] = fields
etf_data.append(etf_dict)
json_data = json.dumps(etf_data)
# print(json_data)
output_dict = [y for y in etf_data if y['fields']['dividend_yield'] > 1]
output_dict = [
x for x in output_dict if x['fields']['average_daily_volume'] > 100000
]
output_dict = [
z for z in output_dict
if z['fields']['200 day moving average'] < z['fields']['last_trade']
]
sorted_list = sorted(output_dict,
key=lambda k: k['fields']['dividend_yield'],
reverse=True)
for etf in sorted_list[:5]:
ETF.objects.create(
portfolio=Portfolio.objects.get(pk=1),
name=etf['fields']['name'],
symbol=etf['fields']['symbol'],
investment_style=1,
last_trade=etf['fields']['last_trade'],
dividend_yield=etf['fields']['dividend_yield'],
absolute_change=etf['fields']['absolute_change'],
percentage_change=etf['fields']['percentage_change'],
currency='USD',
last_updated=timezone.now())
def update_ETF_value(self):
fund = Share(self.symbol)
self.last_trade = fund.get_price()
self.absolute_change = fund.get_change()
self.percentage_change = fund.get_percent_change()
self.dividend_yield = fund.get_dividend_yield()
self.last_updated = timezone.now()
self.save()
def getStock(name_of_company):
global company_name,company_symbol
stock = []
k=0
stock.append([])
stock.append("NA")
stock.append("NA")
stock.append("NA")
stock.append("NA")
stock.append("NA")
stock.append("NA")
stock.append("NA")
j=0
for i in company_symbol:
if i == name_of_company:
break
j=j+1
print "j is "+str(j)+"link is "
stock[0]=company_name[j]
yahoo = Share(name_of_company)
stock[1] = yahoo.get_open()
stock[2] = yahoo.get_price()
stock[3] = yahoo.get_trade_datetime()
stock[4] = company_symbol[j]
stock[5] = yahoo.get_volume()
stock[6] = yahoo.get_dividend_yield()
stock[7] = google_links[j]
print stock
conn = mysql.connect()
cur = conn.cursor()
if 'username' in session:
username = session['username']
cur.execute("SELECT purse FROM user WHERE username = %s;", [username])
print username
for row in cur.fetchall():
for lol in row:
purse=lol
companystock = [dict(
name=stock[0],
open=stock[1],
lasttradeprice=stock[2],
lasttradetime=stock[3],
stocksymbol=stock[4],
MarketCapital=stock[5],
dividend=stock[6],
link=stock[7]
)]
cur.execute("SELECT stock FROM user WHERE username = %s;", [username])
print username
for row in cur.fetchall():
for lol in row:
newarray = lol.split(',')
currentstock = newarray[j]
print purse
return companystock,stock,purse,currentstock
def stock_summary(request, symbol=None):
if symbol == None:
symbol = request.POST['symbol']
current_stock = Stock()
stock = Share(symbol)
current_stock.symbol = symbol.upper()
current_stock.price = stock.get_price()
current_stock.change = stock.get_change()
current_stock.volume = stock.get_volume()
current_stock.prev_close = stock.get_prev_close()
current_stock.stock_open = stock.get_open()
current_stock.avg_daily_volume = stock.get_avg_daily_volume()
current_stock.stock_exchange = stock.get_stock_exchange()
current_stock.market_cap = stock.get_market_cap()
current_stock.book_value = stock.get_book_value()
current_stock.ebitda = stock.get_ebitda()
current_stock.dividend_share = stock.get_dividend_share()
current_stock.dividend_yield = stock.get_dividend_yield()
current_stock.earnings_share = stock.get_earnings_share()
current_stock.days_high = stock.get_days_high()
current_stock.days_low = stock.get_days_low()
current_stock.year_high = stock.get_year_high()
current_stock.year_low = stock.get_year_low()
current_stock.fifty_day_moving_avg = stock.get_50day_moving_avg()
current_stock.two_hundred_day_moving_avg = stock.get_200day_moving_avg()
current_stock.price_earnings_ratio = stock.get_price_earnings_ratio()
current_stock.price_earnings_growth_ratio = stock.get_price_earnings_growth_ratio()
current_stock.price_sales = stock.get_price_sales()
current_stock.price_book = stock.get_price_book()
current_stock.short_ratio = stock.get_short_ratio()
date_metrics = []
url = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+symbol+'/chartdata;type=quote;range=1y/csv'
page = urllib2.urlopen(url).read()
pagebreaks = page.split('\n')
for line in pagebreaks:
items = line.split(',')
if 'Company-Name:' in line:
current_stock.company_name = line[13:len(line)]
current_stock.save()
if 'values' not in items:
if len(items)==6:
hd = HistoricalData(
stock_id = Stock.objects.get(id=int(current_stock.id)).id,
date = items[0][4:6]+'/'+items[0][6:9]+'/'+items[0][0:4],
close = items[1][0:(len(items[1])-2)],
high = items[2][0:(len(items[2])-2)],
price_open = items[3][0:(len(items[3])-2)],
low = items[4][0:(len(items[4])-2)],
volume = items[5][0:-6]+","+items[5][-6:-3]+","+items[5][-3:len(items[5])])
hd.save()
date_metrics.append(hd)
del date_metrics[0]
return render(request, "stock_summary.html", {'current_stock': current_stock, 'date_metrics': date_metrics})
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 rec(p):
yahoo = Share(p)
a = yahoo.get_prev_close()
b = yahoo.get_year_high()
c = yahoo.get_year_low()
d = yahoo.get_open()
e = yahoo.get_ebitda()
f = yahoo.get_market_cap()
g = yahoo.get_avg_daily_volume()
h = yahoo.get_dividend_yield()
i = yahoo.get_earnings_share()
j = yahoo.get_days_low()
k = yahoo.get_days_high()
l = yahoo.get_50day_moving_avg()
m = yahoo.get_200day_moving_avg()
n = yahoo.get_price_earnings_ratio()
o = yahoo.get_price_earnings_growth_ratio()
print p
print "Previous Close: ", a
print "Year High", b
print "Year Low", c
print "Open:", d
print "EBIDTA", e
print "Market Cap", f
print "Average Daily Volume", g
print "Dividend Yield", h
print "Earnings per share", i
print "Days Range:", j, "-", k
print "50 Days Moving Average", l
print "200 Days Moving Average", m
print "Price Earnings Ratio", n
print "Price Earnings Growth Ratio", o
import MySQLdb
db = MySQLdb.connect(host="127.0.0.1",
user="******",
passwd="1111",
db="stocks",
local_infile=1)
cur = db.cursor()
cur.execute(
"""
INSERT INTO stockapp_info (symbol, prev_close, year_high, year_low, open_price , ebidta, market_cap, avg_daily_vol , dividend_yield, eps , days_low ,days_high, moving_avg_50, moving_avg_200, price_earnings_ratio, price_earnings_growth_ratio)
VALUES
(%s, %s, %s, %s, %s, %s, %s,%s,%s,%s,%s,%s,%s,%s,%s,%s)
""", (p, a, b, c, d, e, f, g, h, i, j, k, l, m, n, o))
db.commit()
cur.close()
def rec(p):
yahoo = Share(p)
a=yahoo.get_prev_close()
b=yahoo.get_year_high()
c=yahoo.get_year_low()
d=yahoo.get_open()
e=yahoo.get_ebitda()
f=yahoo.get_market_cap()
g=yahoo.get_avg_daily_volume()
h=yahoo.get_dividend_yield()
i=yahoo.get_earnings_share()
j=yahoo.get_days_low()
k=yahoo.get_days_high()
l=yahoo.get_50day_moving_avg()
m=yahoo.get_200day_moving_avg()
n=yahoo.get_price_earnings_ratio()
o=yahoo.get_price_earnings_growth_ratio()
print p
print "Previous Close: ",a
print "Year High",b
print "Year Low",c
print "Open:",d
print "EBIDTA",e
print "Market Cap",f
print "Average Daily Volume",g
print "Dividend Yield",h
print "Earnings per share",i
print "Days Range:", j ,"-",k
print "50 Days Moving Average",l
print "200 Days Moving Average",m
print"Price Earnings Ratio", n
print"Price Earnings Growth Ratio",o
import MySQLdb
db = MySQLdb.connect(host="127.0.0.1", user="******",passwd="1111", db="stocks",local_infile = 1)
cur=db.cursor()
cur.execute ("""
INSERT INTO stockapp_info (symbol, prev_close, year_high, year_low, open_price , ebidta, market_cap, avg_daily_vol , dividend_yield, eps , days_low ,days_high, moving_avg_50, moving_avg_200, price_earnings_ratio, price_earnings_growth_ratio)
VALUES
(%s, %s, %s, %s, %s, %s, %s,%s,%s,%s,%s,%s,%s,%s,%s,%s)
""", (p,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o))
db.commit()
cur.close()
def selectStock(stocks):
'''
select the stock with today's trading volume at least 6 fold higher than
average historical trading volume
'''
start_time = time()
resultStock = {}
count = 0
num = 0
for symb in stocks.keys():
try:
stock = Share(symb)
vol = int(stock.get_volume())
daily_avg_vol = int(stock.get_avg_daily_volume())
price = float(stock.get_price())
prevPrice = float(stock.get_prev_close())
avg_50day = float(stock.get_50day_moving_avg())
avg_200day = float(stock.get_200day_moving_avg())
except (TypeError, AttributeError):
continue
num += 1
volRatio = vol / daily_avg_vol
print num, stocks[symb][0], volRatio
if volRatio > 6 and price > prevPrice and price > avg_50day:
count += 1
stocks[symb].extend([
vol, daily_avg_vol, volRatio, price, prevPrice, avg_50day,
avg_200day,
stock.get_price_earnings_ratio(),
stock.get_price_book(),
stock.get_short_ratio(),
stock.get_dividend_yield()
])
resultStock = {
symb: stocks[symb]
for symb in stocks.keys() if len(stocks[symb]) > 1
}
print '{} stock(s) has marvelous volume'.format(count)
print 'total time of running: {} seconds'.format(time() - start_time)
return resultStock
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 getStat(stockTicker):
StatDict = dict()
ticker = Share(stockTicker)
mktCap = ticker.get_market_cap()
if (mktCap != None and mktCap != 0):
StatDict["Market cap"] = mktCap
ebitda = ticker.get_ebitda()
if (ebitda != None and ebitda != 0):
StatDict["EBITDA"] = ebitda
peR = ticker.get_price_earnings_ratio()
if (peR != None and peR != 0):
StatDict["Price Earning Ratio"] = peR
EPS = ticker.get_earnings_share()
if (EPS != None and EPS != 0):
StatDict["EPS"] = EPS
divYield = ticker.get_dividend_yield()
if (divYield != None and divYield != 0):
StatDict["Dividend Yield"] = divYield
return StatDict
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 on_message(self, message):
print_logger.debug("Received message: %s" % (message))
self.write_message("Test Message")
if "ValidateTicker" in message:
message = message.split(":")
if len(message) != 2:
print_logger.error("Malformed ticker validation request")
self.write_message("ValidationFailed:Malformed")
return
ticker = message[1]
# The file I have stored didn't end up being a good validation
# option as it does not contain a complete list of all
# securities. I have to acquire the data from yahoo
# finance anyway, so just use that. The Share function
# call will throw a NameError exception if the ticker doesn't exist
# isValid = current_stock_list.is_valid_stock(ticker)
isValid = True
try:
test = Share(str(ticker))
if test.get_price() is None:
isValid = False
except NameError:
isValid = False
if isValid:
self.write_message("ValidationSucceeded:%s" % ticker)
print_logger.debug("Ticker was valid")
else:
self.write_message("ValidationFailed:%s" % ticker)
print_logger.debug("Ticker was bad")
return
elif "GetCompanyName" in message:
print_logger.debug("You got here")
message = message.split(":")
company_ticker = message[1]
company_name = ""
try:
company_info="../task_1/google_search_program/cleaned_data/" + company_ticker + "/company_info"
company_name = " "
f = open(company_info, "r")
line = f.readlines()
company_name = line[0].split(",")
company_name = company_name[0]
company_name = company_name.title()
if '(' not in company_name:
company_name = company_name + " (%s)" % company_ticker
except Exception:
company_name = get_company_title_proxied(company_ticker)
self.write_message("CompanyName:%s" % company_name)
elif "ExecuteQuery" in message:
message = message.split(":")
if len(message) != 2:
print_logger.error("Malformed input query")
self.write_message("QueryResult:Error")
data = current_solr_object.issue_query(str(message[1]))
data = current_solr_object.recover_links(data)
final_string = "QueryResult"
for link in data:
final_string = final_string + ":" + str(link)
self.write_message(final_string)
elif "GetStockData" in message:
message = message.split(":")
if len(message) != 2:
print_logger.error("Malformed Message from Client")
return
ticker = message[1]
# Get ticker information
share_data = Share(ticker)
price = share_data.get_price()
percent_change = share_data.get_change()
previous_close = share_data.get_prev_close()
open_price = share_data.get_open()
volume = share_data.get_volume()
pe_ratio = share_data.get_price_earnings_ratio()
peg_ratio = share_data.get_price_earnings_growth_ratio()
market_cap = share_data.get_market_cap()
book_value = share_data.get_price_book()
average_volume = share_data.get_avg_daily_volume()
dividend_share = share_data.get_dividend_share()
dividend_yield = share_data.get_dividend_yield()
earnings_per_share = share_data.get_earnings_share()
ebitda = share_data.get_ebitda()
fifty_day_ma = share_data.get_50day_moving_avg()
days_high = share_data.get_days_high()
days_low = share_data.get_days_low()
year_high = share_data.get_year_high()
year_low = share_data.get_year_low()
two_hundred_day_ma = share_data.get_200day_moving_avg()
# Build a string to send to the server containing the stock data
share_string = "price:" + str(price) + "|"\
+ "percentChange:" + str(percent_change) + "|"\
+ "previousClose:" + str(previous_close) + "|"\
+ "openPrice:" + str(open_price) + "|"\
+ "volume:" + str(volume) + "|"\
+ "peRatio:" + str(pe_ratio) + "|"\
+ "pegRatio:" + str(peg_ratio) + "|"\
+ "marketCap:" + str(market_cap) + "|"\
+ "bookValue:" + str(book_value) + "|"\
+ "averageVolume:" + str(average_volume) + "|"\
+ "dividendShare:" + str(dividend_share) + "|"\
+ "dividendYield:" + str(dividend_yield) + "|"\
+ "earningsPerShare:" + str(earnings_per_share) + "|"\
+ "ebitda:" + str(ebitda) + "|"\
+ "50DayMa:" + str(fifty_day_ma) + "|"\
+ "daysHigh:" + str(days_high) + "|"\
+ "daysLow:" + str(days_low) + "|"\
+ "yearHigh:" + str(year_high) + "|"\
+ "yearLow:" + str(year_low) + "|"\
+ "200DayMa:" + str(two_hundred_day_ma) + "|"
self.write_message("StockData;%s" % (share_string))
print_logger.debug("Sending Message: StockData;%s" % (share_string))
elif "GetCompanyDesc" in message:
message = message.split(":")
if len(message) != 2:
print_logger.error("Malformed Message from Client")
return
ticker = message[1]
# Read in the company description
description = ""
try:
f = open("../task_1/google_search_program/cleaned_data/%s/company_description" % str(ticker), "r")
description = f.read()
except Exception:
# If the file does not exist, get the data manually
description = update_description_oneoff(ticker)
self.write_message("CompanyDescription:%s" % str(description))
elif "GetCompanyDividend" in message and "Record" not in message:
message = message.split(":")
if len(message) != 2:
print_logger.error("Malformed Message from Client")
return
ticker = message[1]
# Grab the dividend data from dividata.com
dividend_url = "https://dividata.com/stock/%s/dividend" % ticker
# This should potentially be a
dividend_data = requests.get(dividend_url)
dividend_soup = BeautifulSoup(dividend_data.text, 'html5lib')
if len(dividend_soup.find_all("table")) > 0:
dividend_soup = dividend_soup.find_all("table")[0]
else:
dividend_soup = "<h3>No dividend history found.</h3>"
# Send this div up to the server
self.write_message("DividendHistoryData:" + str(dividend_soup))
elif "GetCompanyDividendRecord" in message:
message = message.split(":")
if len(message) != 2:
print_logger.error("Malformed Message from Client")
return
ticker = message[1]
# Get the dividend record html for the table and send it up
dividend_record = strip_dividends(ticker, req_proxy)
print_logger.debug("Writing message: " + str(dividend_record))
self.write_message("DividendRecord:" + str(dividend_record))
elif "GetBollinger" in message:
message = message.split(":")
if len(message) != 2:
print_logger.error("Malformed Message from Client")
return
ticker = message[1]
# Get the bollinger band history along with the 5 day moving average
close, lower_band, five_day_ma = calculate_bands(ticker)
last_5_days_5_day_ma = []
last_5_days_bb = []
last_5_days_close = []
for i in range(0, 5):
last_5_days_5_day_ma.append(five_day_ma[i])
last_5_days_bb.append(lower_band[i])
last_5_days_close.append(close[i])
condition_1 = False
condition_2 = False
# Condition 1: Has the stock price at close been below the lower bollinger band
# at market close within the last 5 days
for i in range(0, 5):
if last_5_days_close[i] < last_5_days_bb[i]:
condition_1 = True
# Condition 2: Has the current stock price been above the 5 day moving average sometime in the last 3 days
for i in range(0, 3):
if last_5_days_close[i] > last_5_days_5_day_ma[i]:
condition_2 = True
if condition_1 is True and condition_2 is True:
self.write_message("BB:GoodCandidate")
else:
self.write_message("BB:BadCandidate")
elif "GetSentiment" in message:
message = message.split(":")
if len(message) != 2:
print_logger.error("Malformed Message from Client")
return
ticker = message[1]
# Lists of sentiment based words
good_words = ["buy", "bull", "bullish", "positive", "gain", "gains", "up"]
bad_words = ["sell", "bear", "bearish", "negative", "loss", "losses", "down"]
DATA_DIRECTORY = "../task_1/google_search_program/cleaned_data/%s" % ticker.upper()
positive_file_stats = []
negative_file_stats = []
positive_files = 0
negative_files = 0
neutral_files = 0
trump_count = 0
files_examined = 0
for root, dirs, files in os.walk(DATA_DIRECTORY):
path = root.split(os.sep)
print((len(path) - 1) * '---', os.path.basename(root))
for file in files:
if "article" in file:
f = open('/'.join(path) + '/' + file)
title = f.readline()
article_text = " ".join(f.readlines())
if article_text.count("trump") > 0:
trump_count = trump_count + 1
positive_word_count = 0
negative_word_count = 0
files_examined = files_examined + 1
for word in good_words:
if word in article_text:
positive_word_count = positive_word_count + article_text.count(word)
print "Word: %s, %s" % (word, article_text.count(word))
for word in bad_words:
if word in article_text:
negative_word_count = negative_word_count + article_text.count(word)
if positive_word_count > negative_word_count:
positive_ratio = float(positive_word_count) / float(negative_word_count + positive_word_count)
if positive_ratio > 0.7:
positive_files = positive_files + 1
positive_file_stats.append((positive_word_count, negative_word_count))
else:
neutral_files = neutral_files + 1
elif positive_word_count == negative_word_count:
neutral_files = neutral_files + 1
else:
negative_ratio = float(negative_word_count) / float(negative_word_count + positive_word_count)
if negative_ratio > 0.7:
negative_files = negative_files + 1
negative_file_stats.append((positive_word_count, negative_word_count))
else:
neutral_files = neutral_files + 1
print_logger.debug("Sentiment:" + str(positive_files) + ":" + str(negative_files) +\
":" + str(neutral_files) + ":" + str(trump_count) + ":" + str(files_examined))
self.write_message("Sentiment:" + str(positive_files) + ":" + str(negative_files) +\
":" + str(neutral_files) + ":" + str(trump_count) + ":" + str(files_examined))
for index, ETF in enumerate(ETF_master_list):
etf_dict = {
'model': 'portfolio.ETF',
'pk': index + 1,
'fields': {},
}
fund = Share(ETF)
fields = {
'name': fund.get_name(),
'symbol': ETF,
'last_trade': fund.get_price(),
'dividend_yield': fund.get_dividend_yield(),
'absolute_change': fund.get_change(),
'percentage_change': fund.get_percent_change(),
'year high': fund.get_year_high(),
'year low': fund.get_year_low(),
'50 day moving average': fund.get_50day_moving_avg(),
'200 day moving average': fund.get_200day_moving_avg(),
'average_daily_volume': fund.get_avg_daily_volume()
}
etf_dict['fields'] = fields
etf_data.append(etf_dict)
json_data = json.dumps(etf_data)
# print(json_data)
def on_message(self, message):
print_logger.debug("Received message: %s" % (message))
if "ValidateTicker" in message:
message = message.split(":")
if len(message) != 2:
print_logger.error("Malformed ticker validation request")
self.write_message("ValidationFailed:Malformed")
return
ticker = message[1]
if validate_ticker(ticker):
self.write_message("ValidationSucceeded:%s" % ticker)
print_logger.debug("Ticker was valid")
else:
self.write_message("ValidationFailed:%s" % ticker)
print_logger.debug("Ticker was bad")
return
elif "GetCompanyName" in message:
print_logger.debug("You got here")
message = message.split(":")
company_ticker = message[1]
company_name = get_company_title(company_ticker)
self.write_message("CompanyName:%s" % company_name)
elif "GetStockData" in message:
message = message.split(":")
if len(message) != 2:
print_logger.error("Malformed Message from Client")
return
ticker = message[1]
# Get ticker information
share_data = Share(ticker)
price = share_data.get_price()
percent_change = share_data.get_change()
previous_close = share_data.get_prev_close()
open_price = share_data.get_open()
volume = share_data.get_volume()
pe_ratio = share_data.get_price_earnings_ratio()
peg_ratio = share_data.get_price_earnings_growth_ratio()
market_cap = share_data.get_market_cap()
book_value = share_data.get_price_book()
average_volume = share_data.get_avg_daily_volume()
dividend_share = share_data.get_dividend_share()
dividend_yield = share_data.get_dividend_yield()
earnings_per_share = share_data.get_earnings_share()
ebitda = share_data.get_ebitda()
fifty_day_ma = share_data.get_50day_moving_avg()
days_high = share_data.get_days_high()
days_low = share_data.get_days_low()
year_high = share_data.get_year_high()
year_low = share_data.get_year_low()
two_hundred_day_ma = share_data.get_200day_moving_avg()
# Build a string to send to the server containing the stock data
share_string = "price:" + str(price) + "|"\
+ "percentChange:" + str(percent_change) + "|"\
+ "previousClose:" + str(previous_close) + "|"\
+ "openPrice:" + str(open_price) + "|"\
+ "volume:" + str(volume) + "|"\
+ "peRatio:" + str(pe_ratio) + "|"\
+ "pegRatio:" + str(peg_ratio) + "|"\
+ "marketCap:" + str(market_cap) + "|"\
+ "bookValue:" + str(book_value) + "|"\
+ "averageVolume:" + str(average_volume) + "|"\
+ "dividendShare:" + str(dividend_share) + "|"\
+ "dividendYield:" + str(dividend_yield) + "|"\
+ "earningsPerShare:" + str(earnings_per_share) + "|"\
+ "ebitda:" + str(ebitda) + "|"\
+ "50DayMa:" + str(fifty_day_ma) + "|"\
+ "daysHigh:" + str(days_high) + "|"\
+ "daysLow:" + str(days_low) + "|"\
+ "yearHigh:" + str(year_high) + "|"\
+ "yearLow:" + str(year_low) + "|"\
+ "200DayMa:" + str(two_hundred_day_ma) + "|"
self.write_message("StockData;%s" % (share_string))
print_logger.debug("Sending Message: StockData;%s" % (share_string))
elif "GetCompanyDesc" in message:
message = message.split(":")
if len(message) != 2:
print_logger.error("Malformed Message from Client")
return
ticker = message[1]
description = update_description_oneoff(ticker)
self.write_message("CompanyDescription:%s" % str(description))
elif "GetCompanyDividend" in message and "Record" not in message:
message = message.split(":")
if len(message) != 2:
print_logger.error("Malformed Message from Client")
return
ticker = message[1]
# Grab the dividend data from dividata.com
dividend_url = "https://dividata.com/stock/%s/dividend" % ticker
# This should potentially be a
dividend_data = requests.get(dividend_url)
dividend_soup = BeautifulSoup(dividend_data.text, 'html5lib')
if len(dividend_soup.find_all("table")) > 0:
dividend_soup = dividend_soup.find_all("table")[0]
else:
dividend_soup = "<h3>No dividend history found.</h3>"
# Send this div up to the server
self.write_message("DividendHistoryData:" + str(dividend_soup))
elif "GetCompanyDividendRecord" in message:
message = message.split(":")
if len(message) != 2:
print_logger.error("Malformed Message from Client")
return
ticker = message[1]
# Get the dividend record html for the table and send it up
#dividend_record = strip_dividends(ticker, req_proxy)
#print_logger.debug("Writing message: " + str(dividend_record))
#self.write_message("DividendRecord:" + str(dividend_record))
elif "GetBollinger" in message:
message = message.split(":")
if len(message) != 2:
print_logger.error("Malformed Message from Client")
return
ticker = message[1]
# Switch into the tmp directory
old_dir = os.getcwd()
os.chdir(TEMP_DIR)
# Update the historical data for the ticker symbol
YAHOO_FINANCE_HISTORICAL_OBJECT.read_ticker_historical(ticker)
bands = BollingerBandStrategy(data_storage_dir="%s/historical_stock_data" % TEMP_DIR\
, ticker_file="%s/stock_list.txt" % TEMP_DIR, filtered_ticker_file=\
"%s/filtered_stock_list.txt" % TEMP_DIR)
# Save the graph so that we can show it on the website
bands.save_stock_chart(ticker, "%s" % TEMP_DIR)
# Also let the server know that we found an answer
result = bands.test_ticker(ticker)
if result is not None:
print_logger.debug("BB:GoodCandidate")
self.write_message("BB:GoodCandidate")
else:
print_logger.debug("BB:BadCandidate")
self.write_message("BB:BadCandidate")
elif "CheckRobinhoodLogin" in message:
print "HELLO WORLD!!! HELLO WORLD!!! HELLO WORLD!!!%s" % ROBINHOOD_INSTANCE
if ROBINHOOD_INSTANCE.is_logged_in() is True:
self.write_message("RobinhoodLoggedIn:%s" % ROBINHOOD_INSTANCE.username)
else:
self.write_message("RobinhoodNotLoggedIn")
elif "GetPosition" in message:
ticker = message.replace("GetPosition:", "")
account_positions = ROBINHOOD_INSTANCE.get_position_history(active=True)
user_owns_stock = False
position_string = ""
for position in account_positions:
# Get data about the position, including current price.
position_data = requests.get(position["instrument"])
position_data = json.loads(position_data._content)
position.update(position_data)
if position["symbol"] != ticker:
continue
quote_data = requests.get(position["quote"]);
quote_data = json.loads(quote_data._content)
position.update(quote_data)
position_string = json.dumps(position)
user_owns_stock = True
if user_owns_stock is True:
self.write_message("Position:%s" % position_string)
else:
self.write_message("Position:None")
print marketcap if myargs.getopen is True: getopen = stock.get_open() print getopen if myargs.getbook is True: getbook = stock.get_book_value() print getbook if myargs.dividendshare is True: getdiv = stock.get_dividend_share() print getdiv if myargs.dividendyield is True: dividendyield = stock.get_dividend_yield() print dividendyield if myargs.eps is True: eps = stock.get_earnings_share() print eps if myargs.dayh is True: dayhigh = stock.get_days_high() print dayhigh if myargs.dayl is True: daylow = stock.get_days_low() print daylow if myargs.yearhigh is True:
def main():
# 1. get the time
day = Time.get_utc_day()
hours_mins = Time.get_utc_hours_minutes()
# 1. Get all the list of stocks
stocks = base.managers.stock_manager.get_many()
# 2. go through stock and update the desired values
for stock in stocks:
ticker = stock.get('ticker')
try:
# 2.1 Get the info from the yahoo API
updated_stock = Share(ticker)
except:
print "-->Failed to update: %s with Yahoo API" % ticker
continue
price = updated_stock.get_price()
open = updated_stock.get_open()
days_high = updated_stock.get_days_high()
days_low = updated_stock.get_days_low()
year_high = updated_stock.get_year_high()
year_low = updated_stock.get_year_low()
volume = updated_stock.get_volume()
market_cap = updated_stock.get_market_cap()
pe_ratio = updated_stock.get_price_earnings_ratio()
div_yield = updated_stock.get_dividend_yield()
change = updated_stock.get_change()
change_percent = updated_stock.data_set.get('ChangeinPercent')
# 2.2 Get the stock body
stock_body = stock.get('body')
stock_price = {hours_mins: price}
if stock_body:
# 1. Get the stock info for the day:
stock_info = stock_body.get(day)
if stock_info:
stock_price = stock_info.get('price')
stock_price.update({hours_mins: price})
else:
stock_body = {}
# 2.2.4 update the stock info dict
stock_info = {'price': stock_price}
stock_info.update({'open': open})
stock_info.update({'days_high': days_high})
stock_info.update({'days_low': days_low})
stock_info.update({'year_high': year_high})
stock_info.update({'year_low': year_low})
stock_info.update({'volume': volume})
stock_info.update({'market_cap': market_cap})
stock_info.update({'pe_ratio': pe_ratio})
stock_info.update({'div_yield': div_yield})
stock_info.update({'change': change})
stock_info.update({'change_percent': change_percent})
# update the stock body
stock_body.update({day: stock_info})
stock.body = stock_body
# 3. update the stock in the DB
try:
base.managers.stock_manager.update_one(stock)
except:
print "-->Failed to update: %s in DB" % ticker
continue
# Determine functionality
from yahoo_finance import Share
tesla = Share('TSLA')
print tesla.get_price()
print tesla.get_market_cap()
print "get_book_value:", tesla.get_book_value()
print "get_ebitda:", tesla.get_ebitda()
print "get_dividend_share:", tesla.get_dividend_share()
print "get_dividend_yield:", tesla.get_dividend_yield()
print "get_earnings_share:", tesla.get_earnings_share()
print "get_days_high:", tesla.get_days_high()
print "get_days_low:", tesla.get_days_low()
print "get_year_high:", tesla.get_year_high()
print "get_year_low:", tesla.get_year_low()
print "get_50day_moving_avg:", tesla.get_50day_moving_avg()
print "get_200day_moving_avg:", tesla.get_200day_moving_avg()
print "get_price_earnings_ratio:", tesla.get_price_earnings_ratio()
print "get_price_earnings_growth_ratio:", tesla.get_price_earnings_growth_ratio(
)
print "get_price_sales:", tesla.get_price_sales()
print "get_price_book:", tesla.get_price_book()
print "get_short_ratio:", tesla.get_short_ratio()
print "get_trade_datetime:", tesla.get_trade_datetime()
# "a:", print tesla.get_historical(start_date, end_date)
# "a:", print tesla.get_info()
print "get_name:", tesla.get_name()
print "refresh:", tesla.refresh()
print "get_percent_change_from_year_high:", tesla.get_percent_change_from_year_high(
)
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)
etfResult = StockInfo.objects.values('ticker', 'name')
#for etfIdx in range(0, len(etfResult)) :
tickerStr = etfResult[0]['ticker']
share = Share(tickerStr)
dateStr = share.get_trade_datetime()[0:11].replace('-','')
ma_200Str = convert(share.get_200day_moving_avg())
ma_50Str = convert(share.get_50day_moving_avg())
book_valueStr = convert(share.get_book_value())
volume_avgStr = convert(share.get_avg_daily_volume())
ebitdaStr = convert(share.get_ebitda())
dividend_yieldStr = convert(share.get_dividend_yield())
market_capStr = convert(share.get_market_cap())
year_highStr = convert(share.get_year_high())
year_lowStr = convert(share.get_year_low())
print tickerStr, dateStr, ma_200Str, ma_50Str, book_valueStr, volume_avgStr, ebitdaStr, dividend_yieldStr, market_capStr, year_highStr, year_lowStr
# print share.get_change()
# print share.get_days_high()
# print share.get_days_low()
# print share.get_dividend_share()
# print share.get_info()
# print share.get_open()
# print share.get_prev_close()
# print share.get_price()
from yahoo_finance import Share
#yahoo = Share('YHOO')
#yahoo = Share('SPXC')
yahoo = Share('TFM')
#yahoo = Share('INDU')
#INDEXSP
#yahoo = Share('NDX')
print yahoo
print yahoo.get_open()
#'36.60'
print yahoo.get_price()
print yahoo.get_price_earnings_ratio()
print 'get_dividend_share: ',yahoo.get_dividend_share()
print 'get_dividend_yield: ',yahoo.get_dividend_yield()
print 'get_earnings_share: ',yahoo.get_earnings_share()
print 'get_price_earnings_ratio: ',yahoo.get_price_earnings_ratio()
print 'get_price_earnings_growth_ratio: ',yahoo.get_price_earnings_growth_ratio()
print 'get_year_high: ',yahoo.get_year_high()
print 'get_year_low: ',yahoo.get_year_low()
print 'get_days_high: ',yahoo.get_days_high()
print 'get_days_low: ',yahoo.get_days_low()
print 'get_ebitda: ',yahoo.get_ebitda()
print 'get_book_value: ',yahoo.get_book_value()
#'36.84'
#print yahoo.get_trade_datetime()
#'2014-02-05 20:50:00 UTC+0000'
#get_avg_daily_volume()
def loadKeyStatistics (cls, companyID = 'A'):
'''
dataset
df= pd.DataFrame(columns=['marketCapital','bookValue','ebitda','dividentShare','DividentYield','earningShare',
'BookPrice','SalesPrice','earningsGrowth','earningsRatio', 'symbol', 'date'])
'''
yahoo = Share(companyID)
yahoo.refresh()
try:
a = re.search('[a-zA-Z]+', yahoo.get_market_cap())
b = re.search('[a-zA-Z]+', yahoo.get_ebitda())
if a.group(0) is not None:
p = re.split('[a-zA-Z]+', yahoo.get_market_cap())
if a.group(0) in 'B':
marketCap = float(p[0]) * 10 ** 9
elif a.group(0) in 'M':
marketCap = float(p[0]) * 10 ** 6
else:
marketCap = -1
print ('Market cap: ' + yahoo.get_market_cap())
else:
marketCap = yahoo.get_market_cap()
if b.group(0) is not None:
p = re.split('[a-zA-Z]+', yahoo.get_ebitda())
if b.group(0) in 'B':
ebitda = float(p[0]) * 10 ** 9
elif b.group(0) in 'M':
ebitda = float(p[0]) * 10 ** 6
else:
ebitda = -1
print ('Ebitda: ' +yahoo.get_ebitda())
else:
ebitda = yahoo.get_ebitda()
except (TypeError, AttributeError):
print ('Missing :' + companyID)
e = sys.exc_info()[0]
print( "<p>Error: %s</p>" % e )
ebitda = -1.0
marketCap = -1.0
try:
company = LoadYahooFinance(symbol = companyID, marketCap = marketCap, bookValue = float(yahoo.get_book_value()), ebitda = ebitda,
dividentShare = float(yahoo.get_dividend_share()), dividentYield = float(yahoo.get_dividend_yield()), earningShare = float(yahoo.get_earnings_share()),
bookPrice = float(yahoo.get_price_book()), salesPrice = float(yahoo.get_price_sales()), earningsGrowth = float(yahoo.get_price_earnings_growth_ratio()),
earningRatio = float(yahoo.get_price_earnings_ratio()))
return company
except TypeError:
print ('Missing :' + companyID)
e = sys.exc_info()[0]
print( "<p>Error: %s</p>" % e )
def write_technical_files(stock_code, start_time, end_time):
# """ Experiment on quandl """
# print('quandl data')
# mydata = quandl.get("FRED/GDP")
# print(mydata)
# print('hello')
# data = quandl.get("WIKI/FB.11", start_date="2014-01-01", end_date="2014-12-31", collapse="monthly", transform="diff")
# print(data)
stock = Share(stock_code)
print('stock.get_info()')
print(stock.get_info())
print('get_price()')
print(stock.get_price())
print('get_change()')
print(stock.get_change())
print('get_stock_exchange()')
print(stock.get_stock_exchange())
print('get_market_cap()')
print(stock.get_market_cap())
print('get_book_value()')
print(stock.get_book_value())
print('get_ebitda()')
print(stock.get_ebitda())
print('get_dividend_share()')
print(stock.get_dividend_share())
print('get_dividend_yield()')
print(stock.get_dividend_yield())
print('get_earnings_share()')
print(stock.get_earnings_share())
print('get_50day_moving_avg()')
print(stock.get_50day_moving_avg())
print('get_200day_moving_avg()')
print(stock.get_200day_moving_avg())
print('get_price_earnings_ratio()')
print(stock.get_price_earnings_ratio())
print('get_price_earnings_growth_ratio()')
print(stock.get_price_earnings_growth_ratio())
print('get_price_sales()')
print(stock.get_price_sales())
print('get_price_book()')
print(stock.get_price_book())
print('get_short_ratio()')
print(stock.get_short_ratio())
print('historical_data')
print(stock.get_historical(start_time, end_time))
historical_data = stock.get_historical(start_time, end_time)
info_text = "Symbol\t" + "Stock Exchange\t" + "Price\t" + "Market Cap\t" + "Book Value\t" + "EBITDA\t" + "50d Moving Avg\t" + "100d Moving Avg\n"
info_text += str(stock.get_info()['symbol']) + "\t" + str(stock.get_stock_exchange()) + "\t" + str(stock.get_price()) + "\t" + str(stock.get_market_cap()) + "\t" + str(stock.get_book_value()) + "\t";
info_text += str(stock.get_ebitda()) + "\t" + str(stock.get_50day_moving_avg()) + "\t" + str(stock.get_200day_moving_avg()) + "\n";
info_directory = '/data/info.tsv'
write_to_file(info_directory, info_text)
high_low_text = "date\t" + "High\t" + "Low\n"
open_close_text = "date\t" + "Open\t" + "Close\n"
volume_text = "date\t" + "Volume\n"
for index, value in enumerate(historical_data):
date = str(historical_data[len(historical_data) - 1 - index]['Date'])
date = date.replace('-','')
stock_high = str(historical_data[len(historical_data) - 1 - index]['High'])
stock_low = str(historical_data[len(historical_data) - 1 - index]['Low'])
stock_open = str(historical_data[len(historical_data) - 1 - index]['Open'])
stock_close = str(historical_data[len(historical_data) - 1 - index]['Close'])
stock_volume = str(int(historical_data[len(historical_data) - 1 - index]['Volume']) / 1000)
high_low_text += date + "\t" + stock_high + "\t" + stock_low + "\n"
open_close_text += date + "\t" + stock_open + "\t" + stock_close + "\n"
volume_text += date + "\t" + stock_volume + "\n"
high_low_directory = '/data/highlow.tsv'
open_close_directory = '/data/openclose.tsv'
volume_directory = '/data/volume.tsv'
write_to_file(high_low_directory, high_low_text)
write_to_file(open_close_directory, open_close_text)
write_to_file(volume_directory, volume_text)
ratio_text = "name\t" + "value\n"
if stock.get_change() != None:
name = "Change"
value = str(stock.get_change())
ratio_text += name + "\t" + value + "\n"
if stock.get_dividend_share() != None:
name = "Dividend Share"
value = str(stock.get_dividend_share())
ratio_text += name + "\t" + value + "\n"
if stock.get_dividend_yield() != None:
name = "Divident Yield"
value = str(stock.get_dividend_yield())
ratio_text += name + "\t" + value + "\n"
if stock.get_earnings_share() != None:
name = "Earning Share"
value = str(stock.get_earnings_share())
ratio_text += name + "\t" + value + "\n"
if stock.get_price_earnings_ratio() != None:
name = "Price Earning"
value = str(stock.get_price_earnings_ratio())
ratio_text += name + "\t" + value + "\n"
if stock.get_price_earnings_growth_ratio() != None:
name = "Price Earning Growth"
value = str(stock.get_price_earnings_growth_ratio())
ratio_text += name + "\t" + value + "\n"
if stock.get_price_sales() != None:
name = "Price Sales"
value = str(stock.get_price_sales())
ratio_text += name + "\t" + value + "\n"
if stock.get_price_book() != None:
name = "Price Book"
value = str(stock.get_price_book())
ratio_text += name + "\t" + value + "\n"
if stock.get_short_ratio() != None:
name = "Short"
value = str(stock.get_short_ratio())
ratio_text += name + "\t" + value + "\n"
ratio_directory = '/data/ratio.tsv'
write_to_file(ratio_directory, ratio_text)
class StockAnalysis:
stock_symbol = 'YHOO'
stock_company_name = "Yahoo Inc,"
quote = Share(stock_symbol)
name = quote.get_name()
stock_company_name = name.split(". ")
def __init__(self,symbol):
self.stock_symbol = symbol
self.quote = Share(self.stock_symbol)
self.name = self.quote.get_name()
self.stock_company_name = self.name.split(". ")
def getStockSymbol(self):
return self.stock_symbol
def getStockPriceInfo(self,symbol):
stock_price = self.quote.get_price()
currency = self.quote.get_currency()
#name = self.quote.get_name()
#stock_company_name = name.split(". ")
price_info = {'Symbol':symbol,'Price':stock_price,'Currency':currency,'Name':self.stock_company_name[0]}
return price_info
def getDividendYieldInfo(self,symbol):
dividend_yield = self.quote.get_dividend_yield()
dividend_info = {'Symbol':symbol,'DividendYield':dividend_yield,'Unit':'%'}
return dividend_info
def getPayOurRatio(self,symbol):
dividend_per_share = self.quote.get_dividend_share()
EPS_current_year = self.quote.get_EPS_estimate_current_year()
pay_out_ratio = (float(dividend_per_share) / float(EPS_current_year))*100
pay_out_ratio = float("{0:.2f}".format(pay_out_ratio))
pay_out_info = {'Symbol':symbol,'PayOut Ratio':pay_out_ratio, "Unit":"%",'Name':self.stock_company_name[0]}
return pay_out_info
def getDeividendDetails(self,symbol):
dividend_per_share = self.quote.get_dividend_share()
EPS_current_year = self.quote.get_EPS_estimate_current_year()
dividend_yield = self.quote.get_dividend_yield()
hold_date = self.quote.get_ex_dividend_date()
payout_date = self.quote.get_dividend_pay_date()
if dividend_per_share is None:
dividend_per_share = "0"
dividend_yield = "0%"
payout_date = "None"
hold_date = "None"
pay_out_ratio = (float(dividend_per_share) / float(EPS_current_year))*100
pay_out_ratio = str(float("{0:.2f}".format(pay_out_ratio)))+"%"
dividend_info = {'Symbol':symbol, 'DividedPerShare':dividend_per_share, 'EPSCurrentYearEstimation': EPS_current_year, 'PayOutRatio':pay_out_ratio,'ExDividedDate':hold_date,'LastPayoutDate':payout_date,'DividendYield':dividend_yield,'Name':self.stock_company_name[0]}
return dividend_info
def getExDividedDate(self,symbol):
hold_date = self.quote.get_ex_dividend_date()
payout_date = self.quote.get_dividend_pay_date()
data = {'Symbol':symbol,'ExDividedDate':hold_date,'LastPayoutDate':payout_date,'Name':self.stock_company_name[0]}
return data
def isDividedRateHealthy(self):
pass
def getDividendDateDetails(self):
pass
def getNumberOfOutStandingShares(self,symbol):
total_market_cap = self.quote.get_market_cap()
current_price = self.quote.get_prev_close()
if "B" in total_market_cap:
total_market_cap = total_market_cap.rstrip("B")
total_market_cap = float(total_market_cap)*1000000000
elif "M" in total_market_cap:
total_market_cap = total_market_cap.rstrip("M")
total_market_cap = float(total_market_cap)*100000000
out_standing_shares = (total_market_cap/float(current_price))/1000000
out_standing_shares = round(out_standing_shares,2)
data = {'Symbol':symbol,'OutStandingShares':out_standing_shares,'Name':self.stock_company_name[0]}
return data
def get200DaysLowPrice(self,symbol):
pass
def get200DaysHighPrice(self,symbol):
pass
def get50DaysLowPrice(self,symbol):
pass
def get50DaysHighPrice(self,symbol):
pass
def getPERation(self):
pass
def result_page(request):
rstlist = list()
grow_sortlst = list()
value_sortlst = list()
quality_sortlst = list()
grow_stcklist = [ 'PNW','RL','PPG','ACN', 'ATVI', 'AYI', 'AMD', 'LNT', 'ALXN', 'GOOGL']
value_stcklist = ['AMG', 'AFL', 'APD', 'AKAM','PCG','PM','PSX', 'AAP', 'AES', 'AET']
quality_stcklist = ['MMM', 'ABT', 'ABBV','PEP','PKI','PRGO','PFE', 'ALB', 'ECL', 'AGN']
stckdict = dict()
if request.method == 'POST':
allotment = int(str(request.POST['allotment']))
ori_allotment = allotment
iv_type = 0
for key in request.POST.keys():
print "iv_type="+str(iv_type)
print str(key)
if key in ['ethical','growth','index','quality','value']:
iv_type = iv_type + 1
print("current selected types count = " + str(iv_type))
if iv_type == 2:
allotment = allotment/float(2)
if 'ethical' in request.POST.keys():
AAPL = Share('AAPL')
aapl_f = float(str(AAPL.get_percent_change_from_year_low())[0:-1]) + \
float(str(AAPL.get_percent_change_from_50_day_moving_average())[0:-1]) - \
float(str(AAPL.get_percent_change_from_year_high())[0:-1])
ADBE = Share('ADBE')
adbe_f = float(str(ADBE.get_percent_change_from_year_low())[0:-1]) + \
float(str(ADBE.get_percent_change_from_50_day_moving_average())[0:-1]) - \
float(str(ADBE.get_percent_change_from_year_high())[0:-1])
NSRGY = Share('NSRGY')
nsrgy_f = float(str(NSRGY.get_percent_change_from_year_low())[0:-1]) + \
float(str(NSRGY.get_percent_change_from_50_day_moving_average())[0:-1]) - \
float(str(NSRGY.get_percent_change_from_year_high())[0:-1])
stckdict['AAPL'] = AAPL
stckdict['ADBE'] = ADBE
stckdict['NSRGY'] = NSRGY
total_f = (aapl_f + adbe_f + nsrgy_f)*iv_type
aapl_pct = aapl_f/float(total_f)
adbe_pct = adbe_f/float(total_f)
nsrgy_pct = nsrgy_f/float(total_f)
aaplD = {"sticker":"AAPL","name":AAPL.get_name(),"aloc_pct":str(aapl_pct),"aloc_amt":str(allotment*aapl_pct),"price":str(AAPL.get_price()),"exchange":str(AAPL.get_stock_exchange())}
adbeD = {"sticker":"ADBE","name":ADBE.get_name(),"aloc_pct":str(adbe_pct),"aloc_amt":str(allotment*adbe_pct),"price":str(ADBE.get_price()),"exchange":str(ADBE.get_stock_exchange())}
nsrgyD = {"sticker":"NSRGY","name":NSRGY.get_name(),"aloc_pct":str(nsrgy_pct),"aloc_amt":str(allotment*nsrgy_pct),"price":str(NSRGY.get_price()),"exchange":str(NSRGY.get_stock_exchange())}
rstlist.append(aaplD)
rstlist.append(adbeD)
rstlist.append(nsrgyD)
if 'growth' in request.POST.keys():
for stock in grow_stcklist:
while True:
try:
detail = Share(stock)
except:
time.sleep(0.1)
continue
break
stckdict[stock]=detail
heapq.heappush(grow_sortlst, (float(str(detail.get_percent_change_from_year_low())[0:-1]) +
float(
str(detail.get_percent_change_from_200_day_moving_average())[0:-1]) +
float(str(detail.get_percent_change_from_50_day_moving_average())[0:-1]) +
float(str(detail.get_percent_change_from_year_high())[0:-1]), stock,
detail))
if len(grow_sortlst) > 3:
heapq.heappop(grow_sortlst)
total_f = 0
for item in grow_sortlst:
total_f = total_f + item[0]
for item in grow_sortlst:
item_pct = item[0]/float(total_f*iv_type)
item_amt = allotment*item_pct
rstlist.append({"sticker":item[1],"name":item[2].get_name(),"aloc_pct":str(item_pct),"aloc_amt":str(item_amt),"price":str(item[2].get_price()),"exchange":str(item[2].get_stock_exchange())})
if 'index' in request.POST.keys():
VTI = Share('VTI')
vti_f = float(str(VTI.get_percent_change_from_year_low())[0:-1]) + \
float(str(VTI.get_percent_change_from_200_day_moving_average())[0:-1]) - \
float(str(VTI.get_percent_change_from_year_high())[0:-1])
IXUS = Share('IXUS')
ixus_f = float(str(IXUS.get_percent_change_from_year_low())[0:-1]) + \
float(str(IXUS.get_percent_change_from_200_day_moving_average())[0:-1]) - \
float(str(IXUS.get_percent_change_from_year_high())[0:-1])
ILTB = Share('ILTB')
iltb_f = float(str(ILTB.get_percent_change_from_year_low())[0:-1]) + \
float(str(ILTB.get_percent_change_from_200_day_moving_average())[0:-1]) - \
float(str(ILTB.get_percent_change_from_year_high())[0:-1])
stckdict['VTI'] = VTI
stckdict['IXUS'] = IXUS
stckdict['ILTB'] = ILTB
total_f = (vti_f + ixus_f + iltb_f)*iv_type
vti_pct = vti_f / float(total_f)
ixus_pct = ixus_f / float(total_f)
iltb_pct = iltb_f / float(total_f)
vtiD = {"sticker": "VTI", "name": VTI.get_name(), "aloc_pct": str(vti_pct),
"aloc_amt": str(allotment * vti_pct), "price": str(VTI.get_price()),
"exchange": str(VTI.get_stock_exchange())}
ixusD = {"sticker": "IXUS", "name": IXUS.get_name(), "aloc_pct": str(ixus_pct),
"aloc_amt": str(allotment * ixus_pct), "price": str(IXUS.get_price()),
"exchange": str(IXUS.get_stock_exchange())}
iltbD = {"sticker": "ILTB", "name": ILTB.get_name(), "aloc_pct": str(iltb_pct),
"aloc_amt": str(allotment * iltb_pct), "price": str(ILTB.get_price()),
"exchange": str(ILTB.get_stock_exchange())}
rstlist.append(vtiD)
rstlist.append(ixusD)
rstlist.append(iltbD)
if 'quality' in request.POST.keys():
for stock in quality_stcklist:
while True:
try:
detail = Share(stock)
except:
time.sleep(0.1)
continue
break
stckdict[stock] = detail
heapq.heappush(quality_sortlst, (
float(detail.get_price_earnings_ratio() if detail.get_price_earnings_ratio() != None else 0) +
float(
detail.get_price_earnings_growth_ratio() if detail.get_price_earnings_growth_ratio() != None else 0) +
float(
detail.get_change_from_200_day_moving_average() if detail.get_change_from_200_day_moving_average() != None else 0) +
float(
detail.get_price_earnings_growth_ratio() if detail.get_price_earnings_growth_ratio() != None else 0),
stock, detail))
if len(quality_sortlst) > 3:
heapq.heappop(quality_sortlst)
total_f = 0
for item in quality_sortlst:
total_f = total_f + item[0]
for item in quality_sortlst:
item_pct = item[0] / float(total_f*iv_type)
item_amt = allotment * item_pct
rstlist.append({"sticker": item[1], "name": item[2].get_name(), "aloc_pct": str(item_pct),
"aloc_amt": str(item_amt), "price": str(item[2].get_price()),
"exchange": str(item[2].get_stock_exchange())})
if 'value' in request.POST.keys():
for stock in value_stcklist:
while True:
try:
detail = Share(stock)
except:
time.sleep(0.1)
continue
break
stckdict[stock] = detail
heapq.heappush(value_sortlst,
(float(detail.get_dividend_yield() if detail.get_dividend_yield() != None else 0) +
float(
detail.get_price_earnings_growth_ratio() if detail.get_price_earnings_growth_ratio() != None else 0) -
float(detail.get_price_book() if detail.get_price_book() != None else 0), stock,
detail))
if len(value_sortlst) > 3:
heapq.heappop(value_sortlst)
total_f = 0
for item in value_sortlst:
total_f = total_f + item[0]
print "total_f=" + str(total_f)
for item in value_sortlst:
item_pct = item[0] / float(total_f*iv_type)
item_amt = allotment * item_pct
rstlist.append({"sticker": item[1], "name": item[2].get_name(), "aloc_pct": str(item_pct),
"aloc_amt": str(item_amt), "price": str(item[2].get_price()),
"exchange": str(item[2].get_stock_exchange())})
days_cnt = 15
str_date = str((datetime.now() - timedelta(days=days_cnt)).date().isoformat())
end_date = str(datetime.today().date().isoformat())
day_pro = list()
remaining = 0
for item in rstlist:
while True:
try:
hist_info = stckdict[item["sticker"]].get_historical(str_date,end_date)
except:
continue
break
for i in range(0,6):
if len(day_pro)<=i:
day_pro.append({hist_info[i]["Date"]:float(hist_info[i]["Close"])*int(float(item["aloc_amt"])/float(hist_info[i]["Open"]))+float(item["aloc_amt"])%float(hist_info[i]["Open"])})
else:
day_pro[i][hist_info[i]["Date"]] = float(day_pro[i][hist_info[i]["Date"]]) + float(hist_info[i]["Close"])*int(float(item["aloc_amt"])/float(hist_info[i]["Open"])) + float(item["aloc_amt"])%float(hist_info[i]["Open"])
rstlist.append({"sticker":"portfolio","history":day_pro})
print(json.dumps(rstlist))
return render(request, 'portfolio/result.html', {'result': json.dumps(rstlist)})
return render(request, 'portfolio/home.html')
