Overall Statistics |
Total Trades 189 Average Win 0.52% Average Loss -0.38% Compounding Annual Return -1.802% Drawdown 9.700% Expectancy -0.011 Net Profit -1.802% Sharpe Ratio -0.198 Loss Rate 59% Win Rate 41% Profit-Loss Ratio 1.38 Alpha -0.026 Beta 0.176 Annual Standard Deviation 0.064 Annual Variance 0.004 Information Ratio -0.989 Tracking Error 0.086 Treynor Ratio -0.072 Total Fees $752.83 |
from clr import AddReference AddReference("System.Collections") 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.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction) 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: list.Add(x.Symbol) 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: list.Add(x.Symbol) 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 for security in self._changes.AddedSecurities: self.SetHoldings(security.Symbol, 0.2) self._changes = SecurityChanges.None; # this event fires whenever we have changes to our universe def OnSecuritiesChanged(self, changes): self._changes = changes