| Overall Statistics |
|
Total Trades 0 Average Win 0% Average Loss 0% Compounding Annual Return 0% Drawdown 0% Expectancy 0 Net Profit 0% Sharpe Ratio 0 Probabilistic Sharpe Ratio 0% Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0 Beta 0 Annual Standard Deviation 0 Annual Variance 0 Information Ratio -0.017 Tracking Error 0.109 Treynor Ratio 0 Total Fees $0.00 |
class CoarseFineFundamentalComboAlgorithm(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(2020,1,1) #Set Start Date
self.SetEndDate(2020,1,31) #Set End Date
self.SetCash(50000) #Set Strategy Cash
# what resolution should the data *added* to the universe be?
self.UniverseSettings.Resolution = Resolution.Minute
# 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.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
self.__numberOfSymbols = 5
self.__numberOfSymbolsFine = 2
self._changes = 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
return [ x.Symbol for x in sortedByDollarVolume[:self.__numberOfSymbols] ]
'''
def CoarseSelectionFunction(self, coarse):
coarse_WO_fundamental = [x for x in coarse if not x.HasFundamentalData]
sortedByVolume = sorted(coarse_WO_fundamental, key=lambda x: x.DollarVolume, reverse=True)
top = sortedByVolume[:20]
return [i.Symbol for i in top]
# 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
return [x.Symbol for x in sortedByPeRatio[:self.__numberOfSymbolsFine] ]
def OnData(self, data):
# if we have no changes, do nothing
if self._changes is 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
for security in self._changes.AddedSecurities:
self.SetHoldings(security.Symbol, 0.2)
self._changes = None
# this event fires whenever we have changes to our universe
def OnSecuritiesChanged(self, changes):
self._changes = changes