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 -16.605 Tracking Error 0.132 Treynor Ratio 0 Total Fees $0.00 |
class UniverseRollingAlgorithm(QCAlgorithm): def Initialize(self): #Initialize Dates, Cash, Equities, Fees, Allocation, Parameters, Indicators, Charts # Set Start Date, End Date, and Cash #------------------------------------------------------- self.SetStartDate(2020, 4, 22) # Set Start Date self.SetEndDate(2020, 4, 24) # Set End Date self.SetCash(100000) # Set Strategy Cash #------------------------------------------------------- # Set Custom Universe #------------------------------------------------------- self.AddUniverse(self.CoarseSelectionFilter, self.FineSelectionFilter) self.UniverseSettings.Resolution = Resolution.Daily #Needs to change to Resolution.Minute once code works, leaving Daily for now to minimize data self.UniverseSettings.SetDataNormalizationMode = DataNormalizationMode.SplitAdjusted self.UniverseSettings.FeeModel = ConstantFeeModel(0.0) self.UniverseSettings.Leverage = 1 #------------------------------------------------------- self.EMA_Period_Fast = 50 self.EMA_Period_Slow = 200 self.UniverseSettings.MinimumTimeInUniverse = self.EMA_Period_Slow self.SetWarmUp(self.EMA_Period_Slow) self.__numberOfSymbols = 100 self.__numberOfSymbolsFine = 10 self.indicators = {} self.fast_ema_window = RollingWindow[IndicatorDataPoint](20) self.slow_ema_window = RollingWindow[IndicatorDataPoint](20) def CoarseSelectionFilter(self, coarse): sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True) # sort descending by daily dollar volume return [ x.Symbol for x in sortedByDollarVolume[:self.__numberOfSymbols] ] # return the symbol objects of the top entries from our sorted collection def FineSelectionFilter(self, fine): # sort the data by P/E ratio and take the top 'NumberOfSymbolsFine' sortedByPeRatio = sorted(fine, key=lambda x: x.OperationRatios.OperationMargin.Value, reverse=False) # sort descending by P/E ratio self.universe = [ x.Symbol for x in sortedByPeRatio[:self.__numberOfSymbolsFine] ] # take the top entries from our sorted collection return self.universe def OnSecuritiesChanged(self, changes): #self.Log(f"OnSecuritiesChanged({self.Time}):: {changes}" for security in changes.RemovedSecurities: if security.Invested: self.Liquidate(security.Symbol) del self.indicators[security.Symbol] # clean up def OnData(self, data): #Entry Point for Data and algorithm - Check Data, Define Buy Quantity, Process Volume, Check Portfolio, Check RSI, Execute Buy/Sell orders, Chart Plots for symbol in self.universe: if not data.ContainsKey(symbol): # is symbol in Slice object? (do we even have data on this step for this asset) continue if data[symbol] is None: # Runtime Error: Python.Runtime.PythonException: AttributeError : 'NoneType' object has no attribute 'Price' continue if data[symbol].Price is None: # Does this slice have the price data we need at this moment? continue if symbol not in self.indicators: # new symbol? setup indicator object. Then update self.indicators[symbol] = SymbolData(symbol, self, self.EMA_Period_Fast, self.EMA_Period_Slow, self.fast_ema_window, self.slow_ema_window) for symbol in self.universe: # Check for Indicator Readiness within Rolling Window #------------------------------------------------------- if not (self.fast_ema_window.IsReady and self.slow_ema_window.IsReady): return #if self.IsWarmingUp: continue self.indicators[symbol].update_value(self.Time, data[symbol].Price) #update by value self.Debug("Rolling Fast Index[0] = " + str(symbol) + "-EMA : " + str(self.indicators[symbol].fast_ema_window[0])) self.Debug("Rolling Fast Index[1] = " + str(symbol) + "-EMA : " + str(self.indicators[symbol].fast_ema_window[1])) self.Debug("Rolling Slow Index[0] = " + str(symbol) + "-EMA : " + str(self.indicators[symbol].slow_ema_window[0])) self.Debug("Rolling Slow Index[1] = " + str(symbol) + "-EMA : " + str(self.indicators[symbol].slow_ema_window[1])) ''' self.Debug("RW4F = " + str(symbol) + "-EMA : " + str(self.indicators[symbol].slow_ema_window[4])) self.Debug("RW5F = " + str(symbol) + "-EMA : " + str(self.indicators[symbol].slow_ema_window[5])) self.Debug("RW6F = " + str(symbol) + "-EMA : " + str(self.indicators[symbol].slow_ema_window[6])) self.Debug("RW7F = " + str(symbol) + "-EMA : " + str(self.indicators[symbol].slow_ema_window[7])) self.Debug("RW8F = " + str(symbol) + "-EMA : " + str(self.indicators[symbol].slow_ema_window[8])) self.Debug("RW9F = " + str(symbol) + "-EMA : " + str(self.indicators[symbol].slow_ema_window[9])) self.Debug("RW10F = " + str(symbol) + "-EMA : " + str(self.indicators[symbol].slow_ema_window[10])) self.Debug("RW11F = " + str(symbol) + "-EMA : " + str(self.indicators[symbol].slow_ema_window[11])) self.Debug("RW12F = " + str(symbol) + "-EMA : " + str(self.indicators[symbol].slow_ema_window[12])) self.Debug("RW13F = " + str(symbol) + "-EMA : " + str(self.indicators[symbol].slow_ema_window[13])) self.Debug("RW14F = " + str(symbol) + "-EMA : " + str(self.indicators[symbol].slow_ema_window[14])) self.Debug("RW15F = " + str(symbol) + "-EMA : " + str(self.indicators[symbol].slow_ema_window[15])) self.Debug("RW16F = " + str(symbol) + "-EMA : " + str(self.indicators[symbol].slow_ema_window[16])) self.Debug("RW17F = " + str(symbol) + "-EMA : " + str(self.indicators[symbol].slow_ema_window[17])) self.Debug("RW18F = " + str(symbol) + "-EMA : " + str(self.indicators[symbol].slow_ema_window[18])) self.Debug("RW19F = " + str(symbol) + "-EMA : " + str(self.indicators[symbol].slow_ema_window[19])) ''' #EXECUTE TRADING LOGIC HERE - PSEUDOCODE FOR NOW #if self.EMA_FastWin[0] > self.EMA_SlowWin[0] and self.EMA_FastWin[1] < self.EMA_SlowWin[1]: # buy_order = self.SetHoldings(security.Key, 0.10) #elif self.EMA_FastWin[0] < self.EMA_SlowWin[0] and self.EMA_FastWin[1] > self.EMA_SlowWin[1]: # self.Liquidate(security.Key) class SymbolData(object): def __init__(self, symbol, context, fast_ema_period, slow_ema_period, fast_ema_window, slow_ema_window): self.symbol = symbol self.fast_ema_period = fast_ema_period self.slow_ema_period = slow_ema_period self.fast_ema = context.EMA(symbol, self.fast_ema_period) self.slow_ema = context.EMA(symbol, self.slow_ema_period) self.fast_ema.Updated += self.EMAUpdated_F self.slow_ema.Updated += self.EMAUpdated_S self.fast_ema_window = fast_ema_window #RollingWindow[IndicatorDataPoint](5) self.slow_ema_window = slow_ema_window #RollingWindow[IndicatorDataPoint](5) #def update_bar(self, bar): #self.indicator.Update(bar) def update_value(self, time, value): self.fast_ema.Update(time, value) self.slow_ema.Update(time, value) def get_fast_EMA(self, context, symbol, period): return self.fast_ema.Current.Value def get_slow_EMA(self): return self.slow_ema.Current.Value def EMAUpdated_F(self, sender, updated): self.fast_ema_window.Add(updated) # Adds updated values to rolling window def EMAUpdated_S(self, sender, updated): self.slow_ema_window.Add(updated) # Adds updated values to rolling window #END CODE