So I'm undertaking my first project that uses universes, rather than simply just feeding the algorithm a fixed set of symbols to work with, and so far it's been a disaster.

The whole day, no matter what I do I get the Error: "Specified Cast is not valid" with no line given, and I'm at my wit's end at this point. All I'm trying to do is take the universe and load it into an array, so I can select ETF's to trade buy on some indicator.

If anybody can help me with this, I will be eternally grateful. Thank you in advance.

Here is my code:

import numpy as np


class RegionalETFrotationAlgorithm(QCAlgorithm):


def Initialize(self):

self.UniverseSettings.Resolution = Resolution.Daily

self.AddUniverse(self.CoarseSelectionFunction)

for universe in self.UniverseManager.Values:
if universe is UserDefinedUniverse:
continue
self.tickers = universe.Members.Keys
for symbol in self.tickers:
self.Debug(symbol.Symbol.Value)


self.changes = None
self.SetStartDate(2008, 1, 1) #Set Start Date
self.SetEndDate(2018, 6 ,1) #Set End Date
self.SetCash(100000) #Set Strategy Cash
# Find more symbols here: http://quantconnect.com/data
#self.tickers = ["MDY", "IEV", "EEM", "ILF", "EPP", "DIA"]
self.AddEquity("EDV", Resolution.Daily)
self.spy = self.AddEquity("SPY", Resolution.Daily).Symbol
self.growth = []

for ticker in self.tickers:
self.AddSecurity(SecurityType.Equity, ticker.Symbol.Value, Resolution.Daily)
dictionary = [ticker.Symbol, self.MACD(ticker.Symbol.Value,12, 26, 9, MovingAverageType.Exponential, Resolution.Daily) ]
self.growth.append(dictionary)

#self.etf = self.growth[0]
self.current = []
self.top = []
self.Schedule.On(self.DateRules.MonthStart(self.spy), self.TimeRules.AfterMarketOpen(self.spy,5), Action(self.rebalance))
'''
def OnSecuritiesChanged(self, changes):
self.growth = []
self.changes = changes
for ticker in self._changes:
self.AddSecurity(SecurityType.Equity, ticker.Symbol.Value, Resolution.Daily)
dictionary = [ticker.Symbol, self.MACD(ticker.Symbol.Value,12, 26, 9, MovingAverageType.Exponential, Resolution.Daily) ]
self.growth.append(dictionary)
'''


def OnData(self, data):
if self.changes is None:
return


# if self.etf[1].Current.Value < 0:
# self.rebalance()
if not self.Portfolio.Invested:
self.rebalance()
for etf in self.current:
if etf[1].Current.Value < 0:
self.replace(etf)
pass


def replace(etf):
self.SetHoldings(etf[0], 0)
self.current.remove(etf)
for security in self.top:
if security in self.current:
continue
else:
if security[1].Current.Value > 0:
self.current.append(security)
self.SetHoldings(security, 0.2)


def CoarseSelectionFunction(self, coarse):
list = []
for security in coarse:
if security.HasFundamentalData==False:
list.append(security)
return list

def rebalance(self):
#self.current = self.growth
#self.current.sort(key=self.getMomentum, reverse = True)
#self.etf = self.current[0]

# if self.etf[1].Current.Value > 0:
# if self.Portfolio.Invested and not self.Portfolio[self.etf[0]].Invested:
# self.Liquidate()
# self.SetHoldings(self.etf[0], 1)

# else:
# if self.Portfolio.Invested:
# self.Liquidate()
# self.SetHoldings("EDV", 1)
self.Liquidate()
self.top = []
for etf in self.growth:
if etf[1].Current.Value > 0:
self.top.append(etf)
self.current.sort(key=self.getMomentum, reverse = True)
if len(self.top) ==0:
self.SetHoldings("EDV", 1)
else:
self.SetHoldings(self.top[0], 0.2)
self.SetHoldings(self.top[1], 0.2)
self.SetHoldings(self.top[2], 0.2)
self.SetHoldings(self.top[3], 0.2)
self.SetHoldings(self.top[4], 0.2)



def getMomentum(self,elem):
return elem[1]