Overall Statistics |
Total Trades 171 Average Win 0.27% Average Loss -0.22% Compounding Annual Return -3.623% Drawdown 11.100% Expectancy 0.108 Net Profit -0.454% Sharpe Ratio 0.009 Loss Rate 50% Win Rate 50% Profit-Loss Ratio 1.22 Alpha 0.037 Beta -1.137 Annual Standard Deviation 0.266 Annual Variance 0.071 Information Ratio -0.083 Tracking Error 0.335 Treynor Ratio -0.002 Total Fees $192.85 |
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, 2, 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 = 10 self.UniverseSettings.MinimumTimeInUniverse = self.atr_window self.SetWarmUp(self.atr_window) # this add universe method accepts two parameters: self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction) # Set dictionary of indicators self.indicators = {} self.__numberOfSymbols = 100 self.__numberOfSymbolsFine = 10 def OnData(self, data): 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): 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 self.Log(str(symbol) + " : " + str(self.indicators[symbol].get_atr())) # now you can use logic to trade, random example: atr = self.indicators[symbol].get_atr() if atr != 0.0: # maybe a new symbol gets added and isnt ready yet? if atr >= 3.0: self.SetHoldings(symbol, -0.1) else: self.Liquidate(symbol) # 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.OperationRatios.OperationMargin.Value, reverse=False) # resulting symbols self.universe = [ x.Symbol for x in sortedByPeRatio[:self.__numberOfSymbolsFine] ] # take the top entries from our sorted collection return self.universe # 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, Func`2[Data.IBaseData,Data.Market.IBaseDataBar] selector = null) """ self.window = window self.indicator = context.EMA(symbol, self.window) 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