Overall Statistics |
Total Trades 816 Average Win 1.66% Average Loss -0.74% Compounding Annual Return 22.452% Drawdown 24.900% Expectancy 0.779 Net Profit 811.462% Sharpe Ratio 1.206 Probabilistic Sharpe Ratio 64.274% Loss Rate 45% Win Rate 55% Profit-Loss Ratio 2.25 Alpha 0.191 Beta 0.047 Annual Standard Deviation 0.164 Annual Variance 0.027 Information Ratio 0.327 Tracking Error 0.222 Treynor Ratio 4.212 Total Fees $3220.69 |
from QuantConnect.Data.UniverseSelection import * import math import numpy as np import pandas as pd import scipy as sp # import statsmodels.api as sm class InOutWithFundamentalFactorAlgorithm(QCAlgorithm): def Initialize(self): self.SetStartDate(2010, 1, 1) #Set Start Date self.SetEndDate(2020, 11, 27) #Set Start Date self.SetCash(100000) #Set Strategy Cash self.UniverseSettings.Resolution = Resolution.Daily self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction) self.spy = self.AddEquity("SPY", Resolution.Minute).Symbol self.holding_months = 1 self.num_screener = 100 self.num_stocks = 10 self.formation_days = 200 self.lowmom = False self.month_count = self.holding_months #self.Schedule.On(self.DateRules.MonthEnd("SPY"), self.TimeRules.AfterMarketOpen('SPY', 120), Action(self.big_rebalance)) #self.Schedule.On(self.DateRules.MonthEnd("SPY"), self.TimeRules.AfterMarketOpen('SPY', 120), Action(self.rebalance)) # rebalance the universe selection once a month self.rebalence_flag = 0 # make sure to run the universe selection at the start of the algorithm even it's not the manth start self.flip_flag = 0 self.first_month_trade_flag = 1 self.trade_flag = 0 self.symbols = None res = Resolution.Minute # Feed-in constants self.INI_WAIT_DAYS = 15 # out for 3 trading weeks # Market and list of signals based on ETFs self.MRKT = self.AddEquity('SPY', res).Symbol # market self.PRDC = self.AddEquity('XLI', res).Symbol # production (industrials) self.METL = self.AddEquity('DBB', res).Symbol # input prices (metals) self.NRES = self.AddEquity('IGE', res).Symbol # input prices (natural res) self.DEBT = self.AddEquity('SHY', res).Symbol # cost of debt (bond yield) self.USDX = self.AddEquity('UUP', res).Symbol # safe haven (USD) self.GOLD = self.AddEquity('GLD', res).Symbol # gold self.SLVA = self.AddEquity('SLV', res).Symbol # vs silver self.UTIL = self.AddEquity('XLU', res).Symbol # utilities self.INDU = self.PRDC # vs industrials self.SHCU = self.AddEquity('FXF', res).Symbol # safe haven currency (CHF) self.RICU = self.AddEquity('FXA', res).Symbol # vs risk currency (AUD) self.FORPAIRS = [self.GOLD, self.SLVA, self.UTIL, self.SHCU, self.RICU] self.SIGNALS = [self.PRDC, self.METL, self.NRES, self.DEBT, self.USDX] # Initialize variables ## 'In'/'out' indicator self.be_in = 999 #initially, set to an arbitrary value different from 1 (in) and 0 (out) ## Day count variables self.dcount = 0 # count of total days since start self.outday = 0 # dcount when self.be_in=0 ## Flexi wait days self.WDadjvar = self.INI_WAIT_DAYS # set a warm-up period to initialize the indicator self.SetWarmUp(timedelta(350)) self.Schedule.On( self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('SPY', 120), self.rebalance_when_out_of_the_market ) def rebalance_when_out_of_the_market(self): # Returns sample to detect extreme observations hist = self.History( self.SIGNALS + [self.MRKT] + self.FORPAIRS, 252, Resolution.Daily)['close'].unstack(level=0).dropna() hist_shift = hist.apply(lambda x: (x.shift(65) + x.shift(64) + x.shift(63) + x.shift(62) + x.shift( 61) + x.shift(60) + x.shift(59) + x.shift(58) + x.shift(57) + x.shift(56) + x.shift(55)) / 11) returns_sample = (hist / hist_shift - 1) # Reverse code USDX: sort largest changes to bottom returns_sample[self.USDX] = returns_sample[self.USDX] * (-1) # For pairs, take returns differential, reverse coded returns_sample['G_S'] = -(returns_sample[self.GOLD] - returns_sample[self.SLVA]) returns_sample['U_I'] = -(returns_sample[self.UTIL] - returns_sample[self.INDU]) returns_sample['C_A'] = -(returns_sample[self.SHCU] - returns_sample[self.RICU]) self.pairlist = ['G_S', 'U_I', 'C_A'] # Extreme observations; statist. significance = 1% pctl_b = np.nanpercentile(returns_sample, 1, axis=0) extreme_b = returns_sample.iloc[-1] < pctl_b # Determine waitdays empirically via safe haven excess returns, 50% decay self.WDadjvar = int( max(0.50 * self.WDadjvar, self.INI_WAIT_DAYS * max(1, np.where((returns_sample[self.GOLD].iloc[-1]>0) & (returns_sample[self.SLVA].iloc[-1]<0) & (returns_sample[self.SLVA].iloc[-2]>0), self.INI_WAIT_DAYS, 1), np.where((returns_sample[self.UTIL].iloc[-1]>0) & (returns_sample[self.INDU].iloc[-1]<0) & (returns_sample[self.INDU].iloc[-2]>0), self.INI_WAIT_DAYS, 1), np.where((returns_sample[self.SHCU].iloc[-1]>0) & (returns_sample[self.RICU].iloc[-1]<0) & (returns_sample[self.RICU].iloc[-2]>0), self.INI_WAIT_DAYS, 1) )) ) adjwaitdays = min(60, self.WDadjvar) # self.Debug('{}'.format(self.WDadjvar)) # Determine whether 'in' or 'out' of the market if (extreme_b[self.SIGNALS + self.pairlist]).any(): self.be_in = False self.outday = self.dcount for symbol in self.Portfolio.Keys: if symbol.Value != "TLT": self.Liquidate(symbol.Value) self.AddEquity("TLT") self.SetHoldings("TLT", 1) #self.Debug("Be OUT TRIGGERED: " + str(self.Time)) if self.dcount >= self.outday + adjwaitdays: #So this logic is best put as: we are about to flip the be_in to TRUE #so right before that do the rebalance otherwise everyday it be_in is true it will try to rebalance if not self.be_in: self.flip_flag = 1 self.big_rebalance() self.rebalance() self.flip_flag = 0 self.be_in = True #self.Debug("Be IN TRIGGERED: " + str(self.Time)) self.dcount += 1 self.Plot("In Out", "in_market", int(self.be_in)) self.Plot("In Out", "num_out_signals", extreme_b[self.SIGNALS + self.pairlist].sum()) self.Plot("Wait Days", "waitdays", adjwaitdays) def CoarseSelectionFunction(self, coarse): #self.Debug(str(self.Time) + "CoarseSelectionFunction: be_in:" + str(self.be_in) + " flip_flag:" + str(self.flip_flag)) if (self.rebalence_flag or self.first_month_trade_flag) and (self.be_in or self.flip_flag): # drop stocks which have no fundamental data or have too low prices selected = [x for x in coarse if (x.HasFundamentalData) and (float(x.Price) > 5)] # rank the stocks by dollar volume filtered = sorted(selected, key=lambda x: x.DollarVolume, reverse=True) return [ x.Symbol for x in filtered[:200]] else: return self.symbols def FineSelectionFunction(self, fine): #self.Debug(str(self.Time) + "FineSelectionFunction: be_in:" + str(self.be_in) + " flip_flag:" + str(self.flip_flag)) if (self.rebalence_flag or self.first_month_trade_flag) and (self.be_in or self.flip_flag): hist = self.History([i.Symbol for i in fine], 1, Resolution.Daily) try: filtered_fine = [x for x in fine if (x.ValuationRatios.EVToEBITDA > 0) and (x.EarningReports.BasicAverageShares.ThreeMonths > 0) and float(x.EarningReports.BasicAverageShares.ThreeMonths) * hist.loc[str(x.Symbol)]['close'][0] > 2e9] except: filtered_fine = [x for x in fine if (x.ValuationRatios.EVToEBITDA > 0) and (x.EarningReports.BasicAverageShares.ThreeMonths > 0)] top = sorted(filtered_fine, key = lambda x: x.ValuationRatios.EVToEBITDA, reverse=True)[:self.num_screener] self.symbols = [x.Symbol for x in top] self.rebalence_flag = 0 self.first_month_trade_flag = 0 self.trade_flag = 1 return self.symbols else: return self.symbols def OnData(self, data): pass def big_rebalance(self): self.rebalence_flag = 1 def rebalance(self): #self.Debug(str(self.Time) + "rebalance: be_in:" + str(self.be_in) + " flip_flag:" + str(self.flip_flag)) spy_hist = self.History([self.spy], 120, Resolution.Daily).loc[str(self.spy)]['close'] if self.Securities[self.spy].Price < spy_hist.mean(): for symbol in self.Portfolio.Keys: if symbol.Value != "TLT": self.Liquidate(symbol.Value) self.AddEquity("TLT") self.SetHoldings("TLT", 1) if self.symbols is None: return chosen_df = self.calc_return(self.symbols) chosen_df = chosen_df.iloc[:self.num_stocks] self.existing_pos = 0 add_symbols = [] for symbol in self.Portfolio.Keys: if symbol.Value == 'SPY': continue if (symbol.Value not in chosen_df.index): self.SetHoldings(symbol, 0) elif (symbol.Value in chosen_df.index): self.existing_pos += 1 weight = 0.99/len(chosen_df) for symbol in chosen_df.index: self.AddEquity(symbol) self.SetHoldings(symbol, weight) def calc_return(self, stocks): hist = self.History(stocks, self.formation_days, Resolution.Daily) current = self.History(stocks, 1, Resolution.Minute) self.price = {} ret = {} for symbol in stocks: if str(symbol) in hist.index.levels[0] and str(symbol) in current.index.levels[0]: self.price[symbol.Value] = list(hist.loc[str(symbol)]['close']) self.price[symbol.Value].append(current.loc[str(symbol)]['close'][0]) for symbol in self.price.keys(): ret[symbol] = (self.price[symbol][-1] - self.price[symbol][0]) / self.price[symbol][0] df_ret = pd.DataFrame.from_dict(ret, orient='index') df_ret.columns = ['return'] sort_return = df_ret.sort_values(by = ['return'], ascending = self.lowmom) return sort_return