Overall Statistics |
Total Trades 723 Average Win 0.41% Average Loss -0.35% Compounding Annual Return 7.157% Drawdown 9.000% Expectancy 0.520 Net Profit 99.884% Sharpe Ratio 0.856 Loss Rate 30% Win Rate 70% Profit-Loss Ratio 1.18 Alpha -0.037 Beta 5.499 Annual Standard Deviation 0.084 Annual Variance 0.007 Information Ratio 0.621 Tracking Error 0.084 Treynor Ratio 0.013 Total Fees $734.31 |
# from clr import AddReference # AddReference("System.Core") # AddReference("QuantConnect.Common") # AddReference("QuantConnect.Algorithm") # from System import * # from QuantConnect import * # from QuantConnect.Algorithm import QCAlgorithm # from QuantConnect.Data.UniverseSelection import * import decimal as d from datetime import datetime, timedelta from decimal import Decimal import numpy as np class ProtectiveAssetAllocationAlgo(QCAlgorithm): def Initialize(self): self.SetCash(25000) self.SetStartDate(2008,1,1) ##Parameters for algorithm self.lookback = 4 ##Lookback in months self.protection = 2 ##Protection factor = 0(low), 1, 2 (high) self.topM = 6 ##topM is the max number of equities self.n_levels = 2 ##number of discrete levels for bond_fraction (>=2) self.SafetySymbols = ["IEF"] ##risk free asset to move into for protection self.N_safe = int(len(self.SafetySymbols)) # these are the growth symbols we"ll rotate through self.GrowthSymbols =["SPY", "QQQ", "IWM", "VGK", "EWJ", "EEM", "VNQ", "DBC", "GLD", "HYG", "LQD", "TLT"] self.N_eq = len(self.GrowthSymbols) # these are the safety symbols we go to when things are looking bad for growth self.AddSecurity(SecurityType.Equity, "IEF", Resolution.Minute) self.syl_objs = [] # we'll hold some computed data in these guys for symbol in list(self.GrowthSymbols): self.syl_objs.append(self.AddSecurity(SecurityType.Equity, symbol, Resolution.Minute).Symbol) for syl_obj in self.syl_objs: syl_obj.lookbackMovingAverage = self.SMA(syl_obj, 21*self.lookback, Resolution.Daily) self.SetWarmup(21*self.lookback+1) self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.At(9,45), Action(self.Rebalance)) def OnData(self, data): pass def Rebalance(self): # poll the Growth Symbols set to determine the number of assets with positive momentum n = 0 for syl_obj in self.syl_objs: price = self.Securities[syl_obj].Price sma = syl_obj.lookbackMovingAverage.Current.Value if price > sma: n += 1 # Calculate the bond fraction based on N_eq, prot, and n # This is the portion to be invested in safe harbor # Calculate equity fraction and weight per equity (frac_eq, w_eq) # Limit bond_fraction to a discrete number of levels (n_levels >=2) n1 = int(int(self.protection) * int(self.N_eq) / 4.0) bond_fraction = float(min(1.0, float(float(self.N_eq) - float(n)) / float(float(self.N_eq) - float(n1)))) #n_steps = float(self.n_levels) - 1.0 #bond_fraction = float(bond_fraction*n_steps)/n_steps w_safe = float(bond_fraction) self.Log("Safe Weight "+str(w_safe)) # # calculate the MOM for each equity # determine the number of equities to be purchases # N = 0 for syl_obj in self.syl_objs: price = self.Securities[syl_obj].Price sma = syl_obj.lookbackMovingAverage.Current.Value syl_obj.MOM = (price / sma) - 1 if syl_obj.MOM > 0.0: N+=1 frac_eq = float(1.0 - w_safe) n_eq = min(N, self.topM) w_eq = 0.0 if N > 0: w_eq = float(float(frac_eq) / float(n_eq)) mom_threshold = sorted([i.MOM for i in self.syl_objs], reverse=True)[n_eq - 1] if frac_eq > 0.0: for syl_obj in self.syl_objs: if syl_obj.MOM >= float(mom_threshold): self.SetHoldings(syl_obj, w_eq) else: if self.Portfolio[syl_obj].HoldStock: self.Liquidate(syl_obj) self.SetHoldings(self.SafetySymbols[0], w_safe) else: for syl_obj in self.syl_objs: if self.Portfolio[syl_obj].HoldStock: self.Liquidate(syl_obj) self.SetHoldings(self.SafetySymbols[0], 1.0)