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 Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0 Beta 0 Annual Standard Deviation 0 Annual Variance 0 Information Ratio 0 Tracking Error 0 Treynor Ratio 0 Total Fees $0.00 |
from clr import AddReference AddReference("System") AddReference("QuantConnect.Algorithm") AddReference("QuantConnect.Indicators") AddReference("QuantConnect.Common") from System import * from QuantConnect import * from QuantConnect.Algorithm import * from QuantConnect.Indicators import * from datetime import datetime from Alphas.MacdAlphaModel import MacdAlphaModel from Execution.ImmediateExecutionModel import ImmediateExecutionModel from Portfolio.EqualWeightingPortfolioConstructionModel import EqualWeightingPortfolioConstructionModel class SymbolData: def __init__(self, symbol, algorithm): self.Symbol = symbol self.buypoint, self.sellpoint= None, None self.longLiqPoint, self.shortLiqPoint, self.yesterdayclose= None, None, None self.numdays = 30 self.Bolband = algorithm.BB(symbol, self.numdays, 2, MovingAverageType.Simple, Resolution.Daily) self.__previous = datetime.min class OptimizedDynamicRegulators(QCAlgorithm): def Initialize(self): self.SetStartDate(2018, 10, 25) # Set Start Date self.SetCash(100000) # Set Strategy Cash # self.AddEquity("SPY", Resolution.Minute) self.AddAlpha(MacdAlphaModel(12, 26, 9, MovingAverageType.Exponential, Resolution.Daily)) self.numdays = 30 self.ceiling,self.floor = 60,20 self.SetBenchmark("SPY") self.SetExecution(ImmediateExecutionModel()) self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel()) self.SetRiskManagement(MaximumSectorExposureRiskManagementModel()) self.__numberOfSymbols = 100 self.__numberOfSymbolsFine = 5 self.SetUniverseSelection(FineFundamentalUniverseSelectionModel(self.CoarseSelectionFunction, self.FineSelectionFunction, None, None)) def OnData(self,data): '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.''' # wait for our macd to fully initialize if not self.MACD.IsReady: return # only once per day if self.__previous.date() == self.Time.date(): return # define a small tolerance on our checks to avoid bouncing tolerance = 0.0025 holdings = self.Portfolio[symbol].Quantity signalDeltaPercent = (self.MACD.Current.Value - self.MACD.Signal.Current.Value)/self.MACD.Fast.Current.Value # if our macd is greater than our signal, then let's go long if holdings <= 0 and signalDeltaPercent > tolerance: # 0.01% # longterm says buy as well self.SetHoldings(symbol, 1.0) # of our macd is less than our signal, then let's go short elif holdings >= 0 and signalDeltaPercent < -tolerance: self.Liquidate(symbol) self.__previous = self.Time def SetSignal(self): close = self.History(symbol, 31, Resolution.Daily)['close'] todayvol = np.std(close[1:31]) yesterdayvol = np.std(close[0:30]) deltavol = (todayvol - yesterdayvol) / todayvol self.numdays = int(round(self.numdays * (1 + deltavol))) if self.numdays > self.ceiling: self.numdays = self.ceiling elif self.numdays < self.floor: self.numdays = self.floor self.high = self.History(symbol, self.numdays, Resolution.Daily)['high'] self.low = self.History(symbol, self.numdays, Resolution.Daily)['low'] self.buypoint = max(self.high) self.sellpoint = min(self.low) historyclose = self.History(symbol, self.numdays, Resolution.Daily)['close'] self.longLiqPoint = np.mean(historyclose) self.shortLiqPoint = np.mean(historyclose) self.yesterdayclose = historyclose.iloc[-1] # wait for our BollingerBand to fully initialize if not self.Bolband.IsReady: return holdings = self.Portfolio[symbol].Quantity if self.yesterdayclose > self.Bolband.UpperBand.Current.Value and self.Portfolio[symbol].Price >= self.buypoint: self.SetHoldings(symbol, 1) elif self.yesterdayclose < self.Bolband.LowerBand.Current.Value and self.Portfolio[symbol].Price <= self.sellpoint: self.SetHoldings(symbol, -1) if holdings > 0 and self.Portfolio[symbol].Price <= self.shortLiqPoint: self.Liquidate(symbol) elif holdings < 0 and self.Portfolio[symbol].Price >= self.shortLiqPoint: self.Liquidate(symbol) self.Log(str(self.yesterdayclose)+(" # of days ")+(str(self.numdays))) # 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.ValuationRatios.PERatio, reverse=True) # take the top entries from our sorted collection return [ x.Symbol for x in sortedByPeRatio[:self.__numberOfSymbolsFine] ]