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