def get(self):
self.response.headers['Content-Type'] = 'text/plain'
"""
for i in np.arange(250,6001,250):
list_of_entities = Forecast.query(ndb.AND(Forecast.rank >= i-250,Forecast.rank < i))#Forecast.rank < 6000)
list_of_keys = ndb.put_multi(list_of_entities)
#list_of_entities = ndb.get_multi(list_of_keys)
ndb.delete_multi(list_of_keys)
list_of_entities = StockList.query(ndb.AND(StockList.rank >= i-250,StockList.rank < i))#Forecast.rank < 6000)
list_of_keys = ndb.put_multi(list_of_entities)
#list_of_entities = ndb.get_multi(list_of_keys)
ndb.delete_multi(list_of_keys)
"""
for i in np.arange(1, 6001, 500):
list_of_keys = Forecast.query().fetch(500, keys_only=True)
#list_of_keys = ndb.put_multi(list_of_entities)
#list_of_entities = ndb.get_multi(list_of_keys)
ndb.delete_multi(list_of_keys)
list_of_keys = StockList.query().fetch(25, keys_only=True)
#list_of_keys = ndb.put_multi(list_of_entities)
#list_of_entities = ndb.get_multi(list_of_keys)
ndb.delete_multi(list_of_keys)
self.response.out.write('All Forecasts Deleted')
self.redirect('./')
def get(self):
self.response.headers['Content-Type'] = 'text/plain'
"""
for i in np.arange(250,6001,250):
list_of_entities = Forecast.query(ndb.AND(Forecast.rank >= i-250,Forecast.rank < i))#Forecast.rank < 6000)
list_of_keys = ndb.put_multi(list_of_entities)
#list_of_entities = ndb.get_multi(list_of_keys)
ndb.delete_multi(list_of_keys)
list_of_entities = StockList.query(ndb.AND(StockList.rank >= i-250,StockList.rank < i))#Forecast.rank < 6000)
list_of_keys = ndb.put_multi(list_of_entities)
#list_of_entities = ndb.get_multi(list_of_keys)
ndb.delete_multi(list_of_keys)
"""
for i in np.arange(1,6001,500):
list_of_keys = Forecast.query().fetch(500, keys_only=True)
#list_of_keys = ndb.put_multi(list_of_entities)
#list_of_entities = ndb.get_multi(list_of_keys)
ndb.delete_multi(list_of_keys)
list_of_keys = StockList.query().fetch(25, keys_only=True)
#list_of_keys = ndb.put_multi(list_of_entities)
#list_of_entities = ndb.get_multi(list_of_keys)
ndb.delete_multi(list_of_keys)
self.response.out.write('All Forecasts Deleted')
self.redirect('./')
def InjectTestData():
#init a new forecast object, fill it with anything
Forecast(
rank = 1,
symbol = "GOOG",
company = "Google, Inc.",
exchange = "NASDAQ",
industry = "Technology",
#predictions
openPredPrice = list(550.0+5.0*(np.random.ranf(11)-0.5)),
closePredPrice = list(550.0+5.0*(np.random.ranf(11)-0.5)),
highPredPrice = list(555.0+10.0*(np.random.ranf(11)-0.5)),
lowPredPrice = list(545.0+10.0*(np.random.ranf(11)-0.5)),
volumePred = list(np.random.randint(1850000,2150000, 11)),
dayOfPred = ['M','Tu','W','Th','F',
'M','Tu','W','Th', 'F',
'M','Tu','W','Th', 'F',
'M','Tu','W','Th', 'F',
'M','Tu','W','Th', 'F',
'M','Tu','W','Th', 'F',
'M'],
#History lists
openPriceHistory = list(550.0+5.0*(np.random.ranf(10)-0.5)),
closePriceHistory = list(550.0+5.0*(np.random.ranf(10)-0.5)),
highPriceHistory = list(560.0+10.0*(np.random.ranf(10)-0.5)),
lowPriceHistory = list(540.0+10.0*(np.random.ranf(10)-0.5)),
volumeHistory = list(np.random.randint(1850000,2150000, 10)),
#prediction validation value, R2
openPredR2 = 1.0 + (np.random.ranf(1)[0] - 0.5)/10.0,
closePredR2 = 1.0 + (np.random.ranf(1)[0] - 0.5)/10.0,
highPredR2 = 1.0 + (np.random.ranf(1)[0] - 0.5)/10.0,
lowPredR2 = 1.0 + (np.random.ranf(1)[0] - 0.5)/10.0,
volumePredR2 = 1.0 + (np.random.ranf(1)[0] - 0.5)/10.0,
openPredSlope = 1.0 + (np.random.ranf(1)[0] - 0.5)/10.0,
closePredSlope = 1.0 + (np.random.ranf(1)[0] - 0.5)/10.0,
highPredSlope = 1.0 + (np.random.ranf(1)[0] - 0.5)/10.0,
lowPredSlope = 1.0 + (np.random.ranf(1)[0] - 0.5)/10.0,
volumePredSlope = 1.0 + (np.random.ranf(1)[0] - 0.5)/10.0,
openModelAccuracy = 1,
closeModelAccuracy = 2,
highModelAccuracy = 2,
lowModelAccuracy = 3
).put()
StockList(
rank = 1,
symbol = "GOOG",
company = "Google, Inc.",
exchange = "NASDAQ",
currentPrice = 550.0+5.0*(np.random.ranf(1)[0]-0.5),
forecastedPrice = 550.0+5.0*(np.random.ranf(1)[0]-0.5),
modelAccuracy = np.random.randint(1,4,1)[0]
).put()
def get(self, stock_symbol):
#make symbol all caps
stock_symbol = stock_symbol.upper()
# Checks for active Google account session
user = users.get_current_user()
#Only commit to ndb if we need to
need_to_commit = False
#Do we have a loged in user
if user:
#Get the users list of favorite stocks
up = UserProfile.query(UserProfile.user_id == str(user.user_id()))
# If new user, make profile
if up.count() == 0:
UserProfile(nickname = str(user.nickname()),
email = str(user.email()),
user_id = str(user.user_id()),
federated_identity = str(user.federated_identity()),
federated_provider = str(user.federated_provider()),
favorite_list = []).put()
up = UserProfile.query(UserProfile.user_id == str(user.user_id()))
need_to_commit = True
else:
for u in up:
#Update last login date
u.last_login_date = datetime.now()
#update user email if changed
if user.email != u.email:
u.email = str(user.email())
need_to_commit = True
#optinal:Remove the stock if its in the list, otherwise add it
for u in up:
if stock_symbol != '':
need_to_commit = True
if stock_symbol in u.favorite_list:
u.favorite_list.remove(stock_symbol)
else:
u.favorite_list.append(stock_symbol)
#commit the changes
if need_to_commit:
for u in up:
u.put()
#return to where the user was
if stock_symbol != '':
try:
self.redirect(self.request.referer)
except:
self.redirect('./')
#no user
else:
#A non user tried to favorite something, lets log them in so they get the action still after login
if stock_symbol != '':
self.redirect(users.create_login_url(self.request.uri))
#Form the symbol list to query
queryList = []
if user:
for u in up:
for item in u.favorite_list:
queryList.append(item)
#Get the time, make a string of format:
#Tue, Jan 6, 2014, 12:00AM EST - US MARKETS CLOSED
now = datetime.now(tz=timezone('US/Eastern'))
#Construct the EST time for the top of page
if( (now.time() >= time(9,30) and now.time() <= time(16,30)) and (now.weekday() <= 4 ) ):
timeString = "{0:s} EST - US Markets Are Open".format(now.strftime("%a, %b %d %Y, %I:%M%p"))
else:
timeString = "{0:s} EST - US Markets Are Closed".format(now.strftime("%a, %b %d %Y, %I:%M%p"))
#
stockList = Forecast.query(Forecast.symbol.IN(queryList)).order(Forecast.rank)
#prevent empty query from causing crashes
if len(queryList)==0:
stockList = []
#Get computed values
if len(queryList)!=0:
computedCloseValues = np.zeros((stockList.count(), 2), float)
else:
computedCloseValues = np.zeros((0, 2), float)
i = 0
if len(queryList)!=0:
for stock in stockList:
#computedCloseValues[i][0] = stock.forecastedPrice-stock.currentPrice
#computedCloseValues[i][1] = (stock.forecastedPrice-stock.currentPrice)/abs(stock.currentPrice)*100.0
computedCloseValues[i][0] = stock.closePredPrice[-1]-stock.closePriceHistory[-1]
computedCloseValues[i][1] = (stock.closePredPrice[-1]-stock.closePriceHistory[-1])/abs(stock.closePriceHistory[-1])*100.0
i += 1
#Init items using info from forecast, just use the first item
dayOfForecast = now.strftime("%A, %B %d %Y")
dof = now
#if stockList.count() > 0:
for stock in stockList:
dayOfForecast = stock.date.strftime("%A, %B %d %Y")
#dof = forecast.date
break
#Form the login/logout url and a name to id the state in jinja2
if user:
url = users.create_logout_url(self.request.uri)
url_linktext = 'Logout'
else:
url = users.create_login_url(self.request.uri)
url_linktext = 'Login'
template_values = {
'stock_list':stockList,
'computed_values':computedCloseValues,
#'forecast_count':forecastCount,
'timeStr':timeString,
'dayOfForecast':dayOfForecast,
'url': url,
'url_linktext': url_linktext,
}
#Show mystock page
template = JINJA_ENVIRONMENT.get_template('mystocks.html')
self.response.write(template.render(template_values))
def get(self, stock_symbol):
#Request the market and prepare its data for plotting
#Reqest the stock and prepare its data for plotting
#Get total nubmer of predictions
#global_stat = stats.GlobalStat.query().get()
#forecastCount = global_stat.count
if stock_symbol == '':
symbol_search = self.request.get("symbol_search")
#Get the time, make a string of format:
#Tue, Jan 6, 2014, 12:00AM EST - US MARKETS CLOSED
now = datetime.now(tz=timezone('US/Eastern'))
#Construct the EST time for the top of page
if( (now.time() >= time(9,30) and now.time() <= time(16,30)) and (now.weekday() <= 4 ) ):
timeString = "{0:s} EST - US Markets Are Open".format(now.strftime("%a, %b %d %Y, %I:%M%p"))
else:
timeString = "{0:s} EST - US Markets Are Closed".format(now.strftime("%a, %b %d %Y, %I:%M%p"))
#
#query to get the top 10 stocks for newest forecast round
#stockList = Forecast.query(Forecast.rank.IN(list(np.arange(1,11))))
stockList = Forecast.query(Forecast.symbol == stock_symbol.upper())
#createa binary list of stock list vs user favorite list
user = users.get_current_user()
if user:
up = UserProfile.query(UserProfile.user_id == str(user.user_id()))
favBinary = []
for u in up:
for stock in stockList:
if stock.symbol in u.favorite_list:
favBinary.append(1)
else:
favBinary.append(0)
break
#case where user exists(like admin) but no profile yet
if stockList.count() != len(favBinary):
favBinary = np.zeros((stockList.count(),))
else:
favBinary = np.zeros((stockList.count(),))
#
#3d array of the candlestick plots
# stock, list of L, list of O, list of C, list of H, list of V
#stocks, history, category
forecastPlotData = np.zeros((stockList.count(), 10, 6), object)
#3d array of the validation plots
#Stocks, history, category
validationPlotData = []
#3d array of computed values nstock, 10
computedValued = np.zeros((stockList.count(), 11), float)
#Init items using info from forecast, just use the first item
dayOfForecast = now.strftime("%A, %B %d %Y")
dof = now
for forecast in stockList:
validationPlotData = np.zeros((stockList.count(), len(forecast.lowPriceHistory), 10), object)
dayOfForecast = forecast.date.strftime("%A, %B %d %Y")
dof = forecast.date
break
i = 0
for forecast in stockList:
forecastPlotData[i,:,0] = [x.encode('utf-8').replace("'","") for x in forecast.dayOfPred[-10:]]
#forecastPlotData[i,-1,0] = str(forecast.dayOfPred[-1]).replace("'","")#.encode('utf-8')#.replace(''','')
forecastPlotData[i,:-1,1] = forecast.lowPriceHistory[-9:]
forecastPlotData[i,-1,1] = forecast.lowPredPrice[-1]
forecastPlotData[i,:-1,2] = forecast.openPriceHistory[-9:]
forecastPlotData[i,-1,2] = forecast.openPredPrice[-1]
forecastPlotData[i,:-1,3] = forecast.closePriceHistory[-9:]
forecastPlotData[i,-1,3] = forecast.closePredPrice[-1]
forecastPlotData[i,:-1,4] = forecast.highPriceHistory[-9:]
forecastPlotData[i,-1,4] = forecast.highPredPrice[-1]
forecastPlotData[i,:-1,5] = forecast.volumeHistory[-9:]
forecastPlotData[i,-1,5] = forecast.volumePred[-1]
validationPlotData[i,:,0] = forecast.openPriceHistory
validationPlotData[i,:,1] = forecast.openPredPrice[:-1]
validationPlotData[i,:,2] = forecast.closePriceHistory
validationPlotData[i,:,3] = forecast.closePredPrice[:-1]
validationPlotData[i,:,4] = forecast.highPriceHistory
validationPlotData[i,:,5] = forecast.highPredPrice[:-1]
validationPlotData[i,:,6] = forecast.lowPriceHistory
validationPlotData[i,:,7] = forecast.lowPredPrice[:-1]
validationPlotData[i,:,8] = forecast.volumeHistory
validationPlotData[i,:,9] = forecast.volumePred[:-1]
computedValued[i][0] = forecast.openPredPrice[-1]-forecast.openPriceHistory[-1]
computedValued[i][1] = (forecast.openPredPrice[-1]-forecast.openPriceHistory[-1])/abs(forecast.openPriceHistory[-1])*100.0
computedValued[i][2] = forecast.closePredPrice[-1]-forecast.closePriceHistory[-1]
computedValued[i][3] =(forecast.closePredPrice[-1]-forecast.closePriceHistory[-1])/abs(forecast.closePriceHistory[-1])*100.0
computedValued[i][4] = forecast.highPredPrice[-1]-forecast.highPriceHistory[-1]
computedValued[i][5] =(forecast.highPredPrice[-1]-forecast.highPriceHistory[-1])/abs(forecast.highPriceHistory[-1])*100.0
computedValued[i][6] = forecast.lowPredPrice[-1]-forecast.lowPriceHistory[-1]
computedValued[i][7] =(forecast.lowPredPrice[-1]-forecast.lowPriceHistory[-1])/abs(forecast.lowPriceHistory[-1])*100.0
computedValued[i][8] = forecast.volumePred[-1]-forecast.volumeHistory[-1]
computedValued[i][9] = (forecast.volumePred[-1]-forecast.volumeHistory[-1])/abs(forecast.volumeHistory[-1])*100.0
computedValued[i][10] = favBinary[i]
#Count for filling arrays
i += 1
if users.get_current_user():
url = users.create_logout_url(self.request.uri)
url_linktext = 'Logout'
else:
url = users.create_login_url(self.request.uri)
url_linktext = 'Sign Up for Updates'
template_values = {
'stock_list':stockList,
'forecast_data':forecastPlotData,
'validation_data':validationPlotData,
'computed_values':computedValued,
#'forecast_count':forecastCount,
'timeStr':timeString,
'dayOfForecast':dayOfForecast,
'url': url,
'url_linktext': url_linktext,
}
template = JINJA_ENVIRONMENT.get_template('symbol.html')
self.response.write(template.render(template_values))
def get(self):
"""list_of_entities = Forecast.query(Forecast.rank < 6000)
list_of_keys = ndb.put_multi(list_of_entities)
list_of_entities = ndb.get_multi(list_of_keys)
ndb.delete_multi(list_of_keys)"""
#Get total nubmer of predictions
global_stat = stats.GlobalStat.query().get()
forecastCount = global_stat.count
#Get the time, make a string of format:
#Tue, Jan 6, 2014, 12:00AM EST - US MARKETS CLOSED
now = datetime.now(tz=timezone('US/Eastern'))
#Construct the EST time for the top of page
if ((now.time() >= time(9, 30) and now.time() <= time(16, 30))
and (now.weekday() <= 4)):
timeString = "{0:s} EST - US Markets Are Open".format(
now.strftime("%a, %b %d %Y, %I:%M%p"))
else:
timeString = "{0:s} EST - US Markets Are Closed".format(
now.strftime("%a, %b %d %Y, %I:%M%p"))
#
#query to get the top 10 stocks for newest forecast round
stockList = Forecast.query(Forecast.rank.IN(list(np.arange(1, 11))))
#3d array of the candlestick plots
# stock, list of L, list of O, list of C, list of H, list of V
#stocks, history, category
forecastPlotData = np.zeros((stockList.count(), 10, 6), object)
#3d array of the validation plots
#Stocks, history, category
validationPlotData = []
#3d array of computed values nstock, 10
computedValued = np.zeros((stockList.count(), 10), float)
#Init items using info from forecast, just use the first item
"""dayOfForecast = now.strftime("%d/%m/%Y")
dof = now
for forecast in stockList:
validationPlotData = np.zeros((stockList.count(), len(forecast.lowPriceHistory), 10), object)
dayOfForecast = forecast.date.strftime("%m/%d/%Y")
dof = forecast.date
break
if now.date() == dof.date():
nameOfDayOfForecast = 'Today'
else:
nameOfDayOfForecast = dof.strftime("%a")"""
dayOfForecast = now.strftime("%A, %B %d %Y")
dof = now
for forecast in stockList:
validationPlotData = np.zeros(
(stockList.count(), len(forecast.lowPriceHistory), 10), object)
dayOfForecast = forecast.date.strftime("%A, %B %d %Y")
dof = forecast.date
break
i = 0
for forecast in stockList:
forecastPlotData[i, :, 0] = [
x.encode('utf-8').replace("'", "")
for x in forecast.dayOfPred[-10:]
]
#forecastPlotData[i,-1,0] = str(forecast.dayOfPred[-1]).replace("'","")#.encode('utf-8')#.replace(''','')
forecastPlotData[i, :-1, 1] = forecast.lowPriceHistory[-9:]
forecastPlotData[i, -1, 1] = forecast.lowPredPrice[-1]
forecastPlotData[i, :-1, 2] = forecast.openPriceHistory[-9:]
forecastPlotData[i, -1, 2] = forecast.openPredPrice[-1]
forecastPlotData[i, :-1, 3] = forecast.closePriceHistory[-9:]
forecastPlotData[i, -1, 3] = forecast.closePredPrice[-1]
forecastPlotData[i, :-1, 4] = forecast.highPriceHistory[-9:]
forecastPlotData[i, -1, 4] = forecast.highPredPrice[-1]
forecastPlotData[i, :-1, 5] = forecast.volumeHistory[-9:]
forecastPlotData[i, -1, 5] = forecast.volumePred[-1]
validationPlotData[i, :, 0] = forecast.openPriceHistory
validationPlotData[i, :, 1] = forecast.openPredPrice[:-1]
validationPlotData[i, :, 2] = forecast.closePriceHistory
validationPlotData[i, :, 3] = forecast.closePredPrice[:-1]
validationPlotData[i, :, 4] = forecast.highPriceHistory
validationPlotData[i, :, 5] = forecast.highPredPrice[:-1]
validationPlotData[i, :, 6] = forecast.lowPriceHistory
validationPlotData[i, :, 7] = forecast.lowPredPrice[:-1]
validationPlotData[i, :, 8] = forecast.volumeHistory
validationPlotData[i, :, 9] = forecast.volumePred[:-1]
computedValued[i][
0] = forecast.openPredPrice[-1] - forecast.openPriceHistory[-1]
computedValued[i][1] = (forecast.openPredPrice[-1] -
forecast.openPriceHistory[-1]) / abs(
forecast.openPriceHistory[-1]) * 100.0
computedValued[i][2] = forecast.closePredPrice[
-1] - forecast.closePriceHistory[-1]
computedValued[i][3] = (forecast.closePredPrice[-1] -
forecast.closePriceHistory[-1]) / abs(
forecast.closePriceHistory[-1]) * 100.0
computedValued[i][
4] = forecast.highPredPrice[-1] - forecast.highPriceHistory[-1]
computedValued[i][5] = (forecast.highPredPrice[-1] -
forecast.highPriceHistory[-1]) / abs(
forecast.highPriceHistory[-1]) * 100.0
computedValued[i][
6] = forecast.lowPredPrice[-1] - forecast.lowPriceHistory[-1]
computedValued[i][7] = (forecast.lowPredPrice[-1] -
forecast.lowPriceHistory[-1]) / abs(
forecast.lowPriceHistory[-1]) * 100.0
computedValued[i][
8] = forecast.volumePred[-1] - forecast.volumeHistory[-1]
computedValued[i][9] = (forecast.volumePred[-1] -
forecast.volumeHistory[-1]) / abs(
forecast.volumeHistory[-1]) * 100.0
#Count for filling arrays
i += 1
"""
guestbook_name = self.request.get('guestbook_name',
DEFAULT_GUESTBOOK_NAME)
greetings_query = Greeting.query(
ancestor=guestbook_key(guestbook_name)).order(-Greeting.date)
greetings = greetings_query.fetch(10)
"""
if users.get_current_user():
url = users.create_logout_url(self.request.uri)
url_linktext = 'Logout'
else:
url = users.create_login_url(self.request.uri)
url_linktext = 'Sign Up for Updates'
template_values = {
'stock_list': stockList,
'forecast_data': forecastPlotData,
'validation_data': validationPlotData,
'computed_values': computedValued,
'forecast_count': forecastCount,
'timeStr': timeString,
'dayOfForecast': dayOfForecast,
#'nameOfDayOfForecast':nameOfDayOfForecast,
'url': url,
'url_linktext': url_linktext,
}
template = JINJA_ENVIRONMENT.get_template('index.html')
self.response.write(template.render(template_values))
def get(self, stock_symbol):
#make symbol all caps
stock_symbol = stock_symbol.upper()
# Checks for active Google account session
user = users.get_current_user()
#Only commit to ndb if we need to
need_to_commit = False
#Do we have a loged in user
if user:
#Get the users list of favorite stocks
up = UserProfile.query(UserProfile.user_id == str(user.user_id()))
# If new user, make profile
if up.count() == 0:
UserProfile(nickname=str(user.nickname()),
email=str(user.email()),
user_id=str(user.user_id()),
federated_identity=str(user.federated_identity()),
federated_provider=str(user.federated_provider()),
favorite_list=[]).put()
up = UserProfile.query(
UserProfile.user_id == str(user.user_id()))
need_to_commit = True
else:
for u in up:
#Update last login date
u.last_login_date = datetime.now()
#update user email if changed
if user.email != u.email:
u.email = str(user.email())
need_to_commit = True
#optinal:Remove the stock if its in the list, otherwise add it
for u in up:
if stock_symbol != '':
need_to_commit = True
if stock_symbol in u.favorite_list:
u.favorite_list.remove(stock_symbol)
else:
u.favorite_list.append(stock_symbol)
#commit the changes
if need_to_commit:
for u in up:
u.put()
#return to where the user was
if stock_symbol != '':
try:
self.redirect(self.request.referer)
except:
self.redirect('./')
#no user
else:
#A non user tried to favorite something, lets log them in so they get the action still after login
if stock_symbol != '':
self.redirect(users.create_login_url(self.request.uri))
#Form the symbol list to query
queryList = []
if user:
for u in up:
for item in u.favorite_list:
queryList.append(item)
#Get the time, make a string of format:
#Tue, Jan 6, 2014, 12:00AM EST - US MARKETS CLOSED
now = datetime.now(tz=timezone('US/Eastern'))
#Construct the EST time for the top of page
if ((now.time() >= time(9, 30) and now.time() <= time(16, 30))
and (now.weekday() <= 4)):
timeString = "{0:s} EST - US Markets Are Open".format(
now.strftime("%a, %b %d %Y, %I:%M%p"))
else:
timeString = "{0:s} EST - US Markets Are Closed".format(
now.strftime("%a, %b %d %Y, %I:%M%p"))
#
stockList = Forecast.query(Forecast.symbol.IN(queryList)).order(
Forecast.rank)
#prevent empty query from causing crashes
if len(queryList) == 0:
stockList = []
#Get computed values
if len(queryList) != 0:
computedCloseValues = np.zeros((stockList.count(), 2), float)
else:
computedCloseValues = np.zeros((0, 2), float)
i = 0
if len(queryList) != 0:
for stock in stockList:
#computedCloseValues[i][0] = stock.forecastedPrice-stock.currentPrice
#computedCloseValues[i][1] = (stock.forecastedPrice-stock.currentPrice)/abs(stock.currentPrice)*100.0
computedCloseValues[i][
0] = stock.closePredPrice[-1] - stock.closePriceHistory[-1]
computedCloseValues[i][1] = (
stock.closePredPrice[-1] - stock.closePriceHistory[-1]
) / abs(stock.closePriceHistory[-1]) * 100.0
i += 1
#Init items using info from forecast, just use the first item
dayOfForecast = now.strftime("%A, %B %d %Y")
dof = now
#if stockList.count() > 0:
for stock in stockList:
dayOfForecast = stock.date.strftime("%A, %B %d %Y")
#dof = forecast.date
break
#Form the login/logout url and a name to id the state in jinja2
if user:
url = users.create_logout_url(self.request.uri)
url_linktext = 'Logout'
else:
url = users.create_login_url(self.request.uri)
url_linktext = 'Login'
template_values = {
'stock_list': stockList,
'computed_values': computedCloseValues,
#'forecast_count':forecastCount,
'timeStr': timeString,
'dayOfForecast': dayOfForecast,
'url': url,
'url_linktext': url_linktext,
}
#Show mystock page
template = JINJA_ENVIRONMENT.get_template('mystocks.html')
self.response.write(template.render(template_values))
def get(self, stock_symbol):
#Request the market and prepare its data for plotting
#Reqest the stock and prepare its data for plotting
#Get total nubmer of predictions
#global_stat = stats.GlobalStat.query().get()
#forecastCount = global_stat.count
if stock_symbol == '':
symbol_search = self.request.get("symbol_search")
#Get the time, make a string of format:
#Tue, Jan 6, 2014, 12:00AM EST - US MARKETS CLOSED
now = datetime.now(tz=timezone('US/Eastern'))
#Construct the EST time for the top of page
if ((now.time() >= time(9, 30) and now.time() <= time(16, 30))
and (now.weekday() <= 4)):
timeString = "{0:s} EST - US Markets Are Open".format(
now.strftime("%a, %b %d %Y, %I:%M%p"))
else:
timeString = "{0:s} EST - US Markets Are Closed".format(
now.strftime("%a, %b %d %Y, %I:%M%p"))
#
#query to get the top 10 stocks for newest forecast round
#stockList = Forecast.query(Forecast.rank.IN(list(np.arange(1,11))))
stockList = Forecast.query(Forecast.symbol == stock_symbol.upper())
#createa binary list of stock list vs user favorite list
user = users.get_current_user()
if user:
up = UserProfile.query(UserProfile.user_id == str(user.user_id()))
favBinary = []
for u in up:
for stock in stockList:
if stock.symbol in u.favorite_list:
favBinary.append(1)
else:
favBinary.append(0)
break
#case where user exists(like admin) but no profile yet
if stockList.count() != len(favBinary):
favBinary = np.zeros((stockList.count(), ))
else:
favBinary = np.zeros((stockList.count(), ))
#
#3d array of the candlestick plots
# stock, list of L, list of O, list of C, list of H, list of V
#stocks, history, category
forecastPlotData = np.zeros((stockList.count(), 10, 6), object)
#3d array of the validation plots
#Stocks, history, category
validationPlotData = []
#3d array of computed values nstock, 10
computedValued = np.zeros((stockList.count(), 11), float)
#Init items using info from forecast, just use the first item
dayOfForecast = now.strftime("%A, %B %d %Y")
dof = now
for forecast in stockList:
validationPlotData = np.zeros(
(stockList.count(), len(forecast.lowPriceHistory), 10), object)
dayOfForecast = forecast.date.strftime("%A, %B %d %Y")
dof = forecast.date
break
i = 0
for forecast in stockList:
forecastPlotData[i, :, 0] = [
x.encode('utf-8').replace("'", "")
for x in forecast.dayOfPred[-10:]
]
#forecastPlotData[i,-1,0] = str(forecast.dayOfPred[-1]).replace("'","")#.encode('utf-8')#.replace(''','')
forecastPlotData[i, :-1, 1] = forecast.lowPriceHistory[-9:]
forecastPlotData[i, -1, 1] = forecast.lowPredPrice[-1]
forecastPlotData[i, :-1, 2] = forecast.openPriceHistory[-9:]
forecastPlotData[i, -1, 2] = forecast.openPredPrice[-1]
forecastPlotData[i, :-1, 3] = forecast.closePriceHistory[-9:]
forecastPlotData[i, -1, 3] = forecast.closePredPrice[-1]
forecastPlotData[i, :-1, 4] = forecast.highPriceHistory[-9:]
forecastPlotData[i, -1, 4] = forecast.highPredPrice[-1]
forecastPlotData[i, :-1, 5] = forecast.volumeHistory[-9:]
forecastPlotData[i, -1, 5] = forecast.volumePred[-1]
validationPlotData[i, :, 0] = forecast.openPriceHistory
validationPlotData[i, :, 1] = forecast.openPredPrice[:-1]
validationPlotData[i, :, 2] = forecast.closePriceHistory
validationPlotData[i, :, 3] = forecast.closePredPrice[:-1]
validationPlotData[i, :, 4] = forecast.highPriceHistory
validationPlotData[i, :, 5] = forecast.highPredPrice[:-1]
validationPlotData[i, :, 6] = forecast.lowPriceHistory
validationPlotData[i, :, 7] = forecast.lowPredPrice[:-1]
validationPlotData[i, :, 8] = forecast.volumeHistory
validationPlotData[i, :, 9] = forecast.volumePred[:-1]
computedValued[i][
0] = forecast.openPredPrice[-1] - forecast.openPriceHistory[-1]
computedValued[i][1] = (forecast.openPredPrice[-1] -
forecast.openPriceHistory[-1]) / abs(
forecast.openPriceHistory[-1]) * 100.0
computedValued[i][2] = forecast.closePredPrice[
-1] - forecast.closePriceHistory[-1]
computedValued[i][3] = (forecast.closePredPrice[-1] -
forecast.closePriceHistory[-1]) / abs(
forecast.closePriceHistory[-1]) * 100.0
computedValued[i][
4] = forecast.highPredPrice[-1] - forecast.highPriceHistory[-1]
computedValued[i][5] = (forecast.highPredPrice[-1] -
forecast.highPriceHistory[-1]) / abs(
forecast.highPriceHistory[-1]) * 100.0
computedValued[i][
6] = forecast.lowPredPrice[-1] - forecast.lowPriceHistory[-1]
computedValued[i][7] = (forecast.lowPredPrice[-1] -
forecast.lowPriceHistory[-1]) / abs(
forecast.lowPriceHistory[-1]) * 100.0
computedValued[i][
8] = forecast.volumePred[-1] - forecast.volumeHistory[-1]
computedValued[i][9] = (forecast.volumePred[-1] -
forecast.volumeHistory[-1]) / abs(
forecast.volumeHistory[-1]) * 100.0
computedValued[i][10] = favBinary[i]
#Count for filling arrays
i += 1
if users.get_current_user():
url = users.create_logout_url(self.request.uri)
url_linktext = 'Logout'
else:
url = users.create_login_url(self.request.uri)
url_linktext = 'Sign Up for Updates'
template_values = {
'stock_list': stockList,
'forecast_data': forecastPlotData,
'validation_data': validationPlotData,
'computed_values': computedValued,
#'forecast_count':forecastCount,
'timeStr': timeString,
'dayOfForecast': dayOfForecast,
'url': url,
'url_linktext': url_linktext,
}
template = JINJA_ENVIRONMENT.get_template('symbol.html')
self.response.write(template.render(template_values))
def get(self):
"""list_of_entities = Forecast.query(Forecast.rank < 6000)
list_of_keys = ndb.put_multi(list_of_entities)
list_of_entities = ndb.get_multi(list_of_keys)
ndb.delete_multi(list_of_keys)"""
#Get total nubmer of predictions
global_stat = stats.GlobalStat.query().get()
forecastCount = global_stat.count
#Get the time, make a string of format:
#Tue, Jan 6, 2014, 12:00AM EST - US MARKETS CLOSED
now = datetime.now(tz=timezone('US/Eastern'))
#Construct the EST time for the top of page
if( (now.time() >= time(9,30) and now.time() <= time(16,30)) and (now.weekday() <= 4 ) ):
timeString = "{0:s} EST - US Markets Are Open".format(now.strftime("%a, %b %d %Y, %I:%M%p"))
else:
timeString = "{0:s} EST - US Markets Are Closed".format(now.strftime("%a, %b %d %Y, %I:%M%p"))
#
#query to get the top 10 stocks for newest forecast round
stockList = Forecast.query(Forecast.rank.IN(list(np.arange(1,11))))
#3d array of the candlestick plots
# stock, list of L, list of O, list of C, list of H, list of V
#stocks, history, category
forecastPlotData = np.zeros((stockList.count(), 10, 6), object)
#3d array of the validation plots
#Stocks, history, category
validationPlotData = []
#3d array of computed values nstock, 10
computedValued = np.zeros((stockList.count(), 10), float)
#Init items using info from forecast, just use the first item
"""dayOfForecast = now.strftime("%d/%m/%Y")
dof = now
for forecast in stockList:
validationPlotData = np.zeros((stockList.count(), len(forecast.lowPriceHistory), 10), object)
dayOfForecast = forecast.date.strftime("%m/%d/%Y")
dof = forecast.date
break
if now.date() == dof.date():
nameOfDayOfForecast = 'Today'
else:
nameOfDayOfForecast = dof.strftime("%a")"""
dayOfForecast = now.strftime("%A, %B %d %Y")
dof = now
for forecast in stockList:
validationPlotData = np.zeros((stockList.count(), len(forecast.lowPriceHistory), 10), object)
dayOfForecast = forecast.date.strftime("%A, %B %d %Y")
dof = forecast.date
break
i = 0
for forecast in stockList:
forecastPlotData[i,:,0] = [x.encode('utf-8').replace("'","") for x in forecast.dayOfPred[-10:]]
#forecastPlotData[i,-1,0] = str(forecast.dayOfPred[-1]).replace("'","")#.encode('utf-8')#.replace(''','')
forecastPlotData[i,:-1,1] = forecast.lowPriceHistory[-9:]
forecastPlotData[i,-1,1] = forecast.lowPredPrice[-1]
forecastPlotData[i,:-1,2] = forecast.openPriceHistory[-9:]
forecastPlotData[i,-1,2] = forecast.openPredPrice[-1]
forecastPlotData[i,:-1,3] = forecast.closePriceHistory[-9:]
forecastPlotData[i,-1,3] = forecast.closePredPrice[-1]
forecastPlotData[i,:-1,4] = forecast.highPriceHistory[-9:]
forecastPlotData[i,-1,4] = forecast.highPredPrice[-1]
forecastPlotData[i,:-1,5] = forecast.volumeHistory[-9:]
forecastPlotData[i,-1,5] = forecast.volumePred[-1]
validationPlotData[i,:,0] = forecast.openPriceHistory
validationPlotData[i,:,1] = forecast.openPredPrice[:-1]
validationPlotData[i,:,2] = forecast.closePriceHistory
validationPlotData[i,:,3] = forecast.closePredPrice[:-1]
validationPlotData[i,:,4] = forecast.highPriceHistory
validationPlotData[i,:,5] = forecast.highPredPrice[:-1]
validationPlotData[i,:,6] = forecast.lowPriceHistory
validationPlotData[i,:,7] = forecast.lowPredPrice[:-1]
validationPlotData[i,:,8] = forecast.volumeHistory
validationPlotData[i,:,9] = forecast.volumePred[:-1]
computedValued[i][0] = forecast.openPredPrice[-1]-forecast.openPriceHistory[-1]
computedValued[i][1] = (forecast.openPredPrice[-1]-forecast.openPriceHistory[-1])/abs(forecast.openPriceHistory[-1])*100.0
computedValued[i][2] = forecast.closePredPrice[-1]-forecast.closePriceHistory[-1]
computedValued[i][3] =(forecast.closePredPrice[-1]-forecast.closePriceHistory[-1])/abs(forecast.closePriceHistory[-1])*100.0
computedValued[i][4] = forecast.highPredPrice[-1]-forecast.highPriceHistory[-1]
computedValued[i][5] =(forecast.highPredPrice[-1]-forecast.highPriceHistory[-1])/abs(forecast.highPriceHistory[-1])*100.0
computedValued[i][6] = forecast.lowPredPrice[-1]-forecast.lowPriceHistory[-1]
computedValued[i][7] =(forecast.lowPredPrice[-1]-forecast.lowPriceHistory[-1])/abs(forecast.lowPriceHistory[-1])*100.0
computedValued[i][8] = forecast.volumePred[-1]-forecast.volumeHistory[-1]
computedValued[i][9] = (forecast.volumePred[-1]-forecast.volumeHistory[-1])/abs(forecast.volumeHistory[-1])*100.0
#Count for filling arrays
i += 1
"""
guestbook_name = self.request.get('guestbook_name',
DEFAULT_GUESTBOOK_NAME)
greetings_query = Greeting.query(
ancestor=guestbook_key(guestbook_name)).order(-Greeting.date)
greetings = greetings_query.fetch(10)
"""
if users.get_current_user():
url = users.create_logout_url(self.request.uri)
url_linktext = 'Logout'
else:
url = users.create_login_url(self.request.uri)
url_linktext = 'Sign Up for Updates'
template_values = {
'stock_list':stockList,
'forecast_data':forecastPlotData,
'validation_data':validationPlotData,
'computed_values':computedValued,
'forecast_count':forecastCount,
'timeStr':timeString,
'dayOfForecast':dayOfForecast,
#'nameOfDayOfForecast':nameOfDayOfForecast,
'url': url,
'url_linktext': url_linktext,
}
template = JINJA_ENVIRONMENT.get_template('index.html')
self.response.write(template.render(template_values))