Overall Statistics |
Total Trades 5709 Average Win 0.05% Average Loss -0.09% Compounding Annual Return 4.100% Drawdown 24.700% Expectancy 0.035 Net Profit 40.702% Sharpe Ratio 0.305 Probabilistic Sharpe Ratio 0.738% Loss Rate 34% Win Rate 66% Profit-Loss Ratio 0.57 Alpha 0.013 Beta 0.223 Annual Standard Deviation 0.114 Annual Variance 0.013 Information Ratio -0.433 Tracking Error 0.146 Treynor Ratio 0.157 Total Fees $115367.92 Estimated Strategy Capacity $11000.00 Lowest Capacity Asset LSXMB W9TJFGFY3VJ9 |
#region imports from AlgorithmImports import * #endregion # https://quantpedia.com/Screener/Details/53 from System import * from QuantConnect import * from QuantConnect.Algorithm import * from QuantConnect.Data import SubscriptionDataSource from QuantConnect.Python import PythonData from QuantConnect.Python import PythonQuandl from datetime import date, timedelta, datetime from decimal import Decimal import numpy as np from math import floor import json class SentimentAndStyleRotationAlgorithm(QCAlgorithm): def Initialize(self): self.SetStartDate(2010, 1, 1) self.SetEndDate(2018, 7, 1) self.SetCash(10000000) self.AddData(CBOE, "VIX", Resolution.Daily) self.AddData(PutCallRatio, "PutCallRatio", Resolution.Daily) self.vix_SMA_1 = SimpleMovingAverage(21) self.vix_SMA_6 = SimpleMovingAverage(21*6) self.PCRatio_SMA_1 = SimpleMovingAverage(21) self.PCRatio_SMA_6 = SimpleMovingAverage(21*6) # initialize the indicator with the history request PCRatio_history = self.History(["PutCallRatio"], 21*10, Resolution.Daily) vix_history = self.History(["VIX"], 21*10, Resolution.Daily) for tuple in vix_history.unstack(0).itertuples(): self.vix_SMA_6.Update(tuple.Index, tuple[-1]) self.vix_SMA_1.Update(tuple.Index, tuple[-1]) for tuple in PCRatio_history.unstack(0).itertuples(): self.PCRatio_SMA_1.Update(tuple.Index, tuple[-1]) self.PCRatio_SMA_6.Update(tuple.Index, tuple[-1]) self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction) self.AddEquity("SPY", Resolution.Daily) self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.At(0, 0), self.rebalance) self.month_start = False self.selection = False self.months = -1 def CoarseSelectionFunction(self, coarse): if self.month_start: # drop stocks which have no fundamental data or have low price self.filtered_coarse = [x.Symbol for x in coarse if (x.HasFundamentalData)] return self.filtered_coarse else: return Universe.Unchanged def FineSelectionFunction(self, fine): if self.month_start: self.selection = True fine = [i for i in fine if i.EarningReports.BasicAverageShares.ThreeMonths>0 and i.EarningReports.BasicEPS.TwelveMonths>0 and i.ValuationRatios.PERatio>0 and i.ValuationRatios.PBRatio>0] sotrted_market_cap = sorted(fine, key = lambda x:x.MarketCap, reverse=True) decile_top1 = sotrted_market_cap[:floor(len(sotrted_market_cap)/10)] decile_top2 = sotrted_market_cap[floor(len(sotrted_market_cap)/10):floor(len(sotrted_market_cap)*2/10)] decile_top3 = sotrted_market_cap[floor(len(sotrted_market_cap)*2/10):floor(len(sotrted_market_cap)*3/10)] sorted_PB1 = sorted(decile_top1, key = lambda x: x.ValuationRatios.PBRatio) sorted_PB2 = sorted(decile_top2, key = lambda x: x.ValuationRatios.PBRatio) sorted_PB3 = sorted(decile_top3, key = lambda x: x.ValuationRatios.PBRatio) # The value portfolio consists of all firms included in the quintile with the lowest P/B ratio PB_bottom1 = sorted_PB1[:floor(len(decile_top1)/5)] PB_bottom2 = sorted_PB2[:floor(len(decile_top2)/5)] PB_bottom3 = sorted_PB3[:floor(len(decile_top3)/5)] self.value_portfolio = [i.Symbol for i in PB_bottom1 + PB_bottom2 + PB_bottom3] # The growth portfolio consists of all firms included in the quintile with the highest P/B ratio PB_top1 = sorted_PB1[-floor(len(decile_top1)/5):] PB_top2 = sorted_PB2[-floor(len(decile_top2)/5):] PB_top3 = sorted_PB3[-floor(len(decile_top3)/5):] self.growth_portfolio = [i.Symbol for i in PB_top1 + PB_top2 + PB_top3] return self.value_portfolio + self.growth_portfolio else: return Universe.Unchanged def rebalance(self): # rebalance every three months self.months += 1 if self.months%3 == 0: self.month_start = True def OnData(self, data): if not data.ContainsKey("VIX") or not data.ContainsKey("PutCallRatio") \ or data["VIX"].Value == 0 or data["PutCallRatio"].Value == 0: return self.vix_SMA_1.Update(self.Time, data["VIX"].Value) self.vix_SMA_6.Update(self.Time, data["VIX"].Value) self.PCRatio_SMA_1.Update(self.Time, data["PutCallRatio"].Value) self.PCRatio_SMA_6.Update(self.Time, data["PutCallRatio"].Value) if self.month_start and self.selection: self.month_start = False self.selection = False stocks_invested = [x.Key for x in self.Portfolio if x.Value.Invested] for i in stocks_invested: if i not in self.value_portfolio+self.growth_portfolio: self.Liquidate(i) if self.vix_SMA_1.Current.Value > self.vix_SMA_6.Current.Value: if self.PCRatio_SMA_1.Current.Value < self.PCRatio_SMA_6.Current.Value: long_weight = 1/len(self.value_portfolio) for long in self.value_portfolio: self.SetHoldings(long, long_weight) elif self.PCRatio_SMA_1.Current.Value > self.PCRatio_SMA_6.Current.Value: short_weight = 1/len(self.value_portfolio) for short in self.value_portfolio: self.SetHoldings(short, -short_weight) else: long_weight = 1/len(self.value_portfolio+self.growth_portfolio) for long in self.value_portfolio+self.growth_portfolio: self.SetHoldings(long, long_weight) class PutCallRatio(PythonData): '''Cboe Equity Volume Put/Call Ratios (11-01-2006 to 2019) Custom Data Class''' def GetSource(self, config, date, isLiveMode): return SubscriptionDataSource("https://cdn.cboe.com/resources/options/volume_and_call_put_ratios/totalpc.csv", SubscriptionTransportMedium.RemoteFile) def Reader(self, config, line, date, isLiveMode): if not (line.strip() and line[0].isdigit()): return None index = CBOE() index.Symbol = config.Symbol try: # Example File Format: # DATE CALL PUT TOTAL P/C Ratio # 11/1/06 976510 623929 1600439 0.64 data = line.split(',') index.Time = datetime.strptime(data[0], "%m/%d/%Y").strftime("%Y-%m-%d") index.Value = Decimal(data[4]) except ValueError: return None return index