Overall Statistics Total Trades 74 Average Win 0.57% Average Loss -0.37% Compounding Annual Return 4.511% Drawdown 5.700% Expectancy 0.129 Net Profit 4.511% Sharpe Ratio 0.657 Loss Rate 56% Win Rate 44% Profit-Loss Ratio 1.54 Alpha 0.008 Beta 0.291 Annual Standard Deviation 0.057 Annual Variance 0.003 Information Ratio -0.763 Tracking Error 0.086 Treynor Ratio 0.129 Total Fees \$116.16
```from System.Collections.Generic import List
from QuantConnect.Data.UniverseSelection import *

class CoarseFineFundamentalComboAlgorithm(QCAlgorithm):
'''In this algorithm we demonstrate how to define a universe as a combination of use the coarse fundamental data and fine fundamental data'''
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(2014,01,01)  #Set Start Date
self.SetEndDate(2015,01,01)    #Set End Date
self.SetCash(50000)            #Set Strategy Cash

self.UniverseSettings.Resolution = Resolution.Daily

# this add universe method accepts two parameters:
# - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol>
# - fine selection function: accepts an IEnumerable<FineFundamental> and returns an IEnumerable<Symbol>

self.__numberOfSymbols = 5
self.__numberOfSymbolsFine = 2
self._changes = SecurityChanges.None

# 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
top5 = sortedByDollarVolume[:self.__numberOfSymbols]

# we need to return only the symbol objects
list = List[Symbol]()
for x in top5:

return list

# 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.PERatio, reverse=True)

# take the top entries from our sorted collection
topFine = sortedByPeRatio[:self.__numberOfSymbolsFine]

list = List[Symbol]()
for x in topFine:

return list

def OnData(self, data):
# if we have no changes, do nothing
if self._changes == SecurityChanges.None: return

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

# we want 20% allocation in each security in our universe