### Strategy Test ImplementationÂ¶

I am trying to:

• filter on a universe using coarse + fine (monthly or quarterly)
• create an indicator to run daily or on minute data on the resulting universe to enter trades
In [ ]:
class CoarseFineFundamentalATRComboAlgorithm(QCAlgorithm):

def Initialize(self):
'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''

self.SetStartDate(2014, 1, 1)  #Set Start Date
self.SetEndDate(2014, 6, 1)    #Set End Date
self.SetCash(50000)            #Set Strategy Cash

# what resolution should the data *added* to the universe be?
self.UniverseSettings.Resolution = Resolution.Daily

# An indicator(or any rolling window) needs data(updates) to have a value
self.atr_window = 20
self.UniverseSettings.MinimumTimeInUniverse = self.atr_window
self.SetWarmUp(self.atr_window)

# this add universe method accepts two parameters:
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)

#self.flag1 = 1  # variable to control the monthly rebalance of coarse and fine selection function

# Set dictionary of indicators
self.indicators = {}

# Set a list of the selected universe
self.universe=[]

self.__numberOfSymbols     = 200
self.__numberOfSymbolsFine = 10

self.spy = self.AddEquity("SPY", Resolution.Daily).Symbol

#self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.AfterMarketOpen("SPY", 10), Action(self.CoarseSelectionFunction))

# sort the data by daily dollar volume and take the top 'NumberOfSymbols'
def CoarseSelectionFunction(self, coarse):
# sort descending by daily dollar volume
sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)

# return the symbol objects of the top entries from our sorted collection
return [ x.Symbol for x in sortedByDollarVolume[:self.__numberOfSymbols] ]

# sort the data by P/E ratio and take the top 'NumberOfSymbolsFine'
def FineSelectionFunction(self, fine):

# sort descending by P/E ratio
sortedByPeRatio = sorted(fine, key=lambda x: x.ValuationRatios.FCFYield, reverse=True)

# resulting symbols
self.universe = [ x.Symbol for x in sortedByPeRatio[:self.__numberOfSymbolsFine] ]

# take the top entries from our sorted collection
return self.universe

def OnData(self, data):

# Return before trying to run a loop on empty list
if not self.universe:
return

for symbol in self.universe:

# is symbol iin Slice object? (do we even have data on this step for this asset)
if not data.ContainsKey(symbol):
return
self.indicators[symbol].update_value(self.Time, data[symbol].Price)

#continue

#if data.ContainsKey(symbol):
#self.indicators[symbol].update_value(self.Time, data[symbol].Price)
#else:
#    continue

# new symbol? setup indicator object. Then update
if symbol not in self.indicators:
self.indicators[symbol] = SymbolData(symbol, self, self.atr_window)

# update by bar
#self.indicators[symbol].update_bar(data[symbol])
#update by value

#self.indicators[symbol].update_value(self.Time, data[symbol].Price)

if self.IsWarmingUp: continue

# now you can use logic to trade, random example:
lowerband = self.indicators[symbol].get_atr()
upperband = self.indicators[symbol].get_atr2()

# Log the symbol, price & indicators
self.Log(str(symbol) + " : " + str(self.indicators[symbol].get_atr()))
self.Log("PRICE : {}".format(str(self.Securities[symbol].Price)))
self.Log("UPPERBAND : {}".format(str(lowerband)))
self.Log("LOWERBAND : {}".format(str(lowerband)))

# this event fires whenever we have changes to our universe
def OnSecuritiesChanged(self, changes):

# liquidate removed securities
for security in changes.RemovedSecurities:
if security.Invested:
self.Liquidate(security.Symbol)

# clean up
del self.indicators[security.Symbol]

class SymbolData(object):
def __init__(self, symbol, context, window):
self.symbol = symbol
"""
I had to pass ATR from outside object to get it to work, could pass context and use any indica
var atr = ATR(Symbol symbol, int period, MovingAverageType type = null, Resolution resolution = null, Func2[Data.IBaseData,Data.Market.IBaseDataBar] selector = null)
"""
self.window    = window
#self.indicator = context.EMA(symbol, self.window)
#self.indicator = context.BB(symbol, self.window)
self.indicator = context.BB(symbol,10,2,MovingAverageType.Simple,Resolution.Daily)
self.indicator2 = context.BB(symbol,20,1,MovingAverageType.Simple,Resolution.Daily)
self.atr       = 0.0

"""
Runtime Error: Python.Runtime.PythonException: NotSupportedException : AverageTrueRange does not support Update(DateTime, decimal) method overload. Use Update(IBaseDataBar) instead.
"""
def update_bar(self, bar):
self.indicator.Update(bar)

def update_value(self, time, value):
self.indicator.Update(time, value)

def get_atr(self):
#return self.indicator.Current.Value
return self.indicator.LowerBand.Current.Value

def get_atr2(self):
#return self.indicator.Current.Value
return self.indicator2.UpperBand.Current.Value
`