Overall Statistics |
Total Trades 733 Average Win 2.20% Average Loss -0.75% Compounding Annual Return 25.782% Drawdown 21.900% Expectancy 0.995 Net Profit 1844.376% Sharpe Ratio 1.285 Probabilistic Sharpe Ratio 73.458% Loss Rate 49% Win Rate 51% Profit-Loss Ratio 2.92 Alpha 0.219 Beta 0.08 Annual Standard Deviation 0.176 Annual Variance 0.031 Information Ratio 0.525 Tracking Error 0.246 Treynor Ratio 2.819 Total Fees $923.02 |
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(2008, 1, 1) #Set Start Date #self.SetEndDate(2020, 12, 1) self.cap = 12000 self.SetCash(self.cap) 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.AddEquity("TLT", Resolution.Minute) self.MKT = self.AddEquity('SPY', Resolution.Minute).Symbol self.spy = [] self.FORPAIRS = [self.GOLD, self.SLVA, self.UTIL, self.SHCU, self.RICU] #self.SIGNALS = [self.PRDC, self.METL, self.NRES, self.DEBT, self.USDX] self.SIGNALS = [self.PRDC, self.METL, self.NRES, 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 # Setup daily consolidation symbols = self.SIGNALS + [self.MRKT] + self.FORPAIRS for symbol in symbols: self.consolidator = TradeBarConsolidator(timedelta(days=1)) self.consolidator.DataConsolidated += self.consolidation_handler self.SubscriptionManager.AddConsolidator(symbol, self.consolidator) # Warm up history self.lookback = 252 self.history = self.History(symbols, self.lookback, Resolution.Daily) if self.history.empty or 'close' not in self.history.columns: return self.history = self.history['close'].unstack(level=0).dropna() self.update_history_shift() self.UniverseSettings.Resolution = Resolution.Minute self.AddUniverse(self.MomentumSelectionFunction, self.FundamentalSelectionFunction) self.num_screener = 100 self.num_stocks = 10 self.formation_days = 70 self.lowmom = False # 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 self.Schedule.On( self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('SPY', 120), self.rebalance_when_out_of_the_market ) self.Schedule.On( self.DateRules.EveryDay(), self.TimeRules.BeforeMarketClose('SPY', 0), self.record_vars ) def consolidation_handler(self, sender, consolidated): self.history.loc[consolidated.EndTime, consolidated.Symbol] = consolidated.Close self.history = self.history.iloc[-self.lookback:] self.update_history_shift() def update_history_shift(self): self.history_shift = self.history.rolling(11, center=True).mean().shift(60) def rebalance_when_out_of_the_market(self): returns_sample = (self.history / self.history_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 self.SetHoldings("TLT", 1, True) self.Debug(str(extreme_b[self.SIGNALS + self.pairlist])) self.Log(str(extreme_b[self.SIGNALS + self.pairlist])) #self.Log("BE OUT to TLT " + str(self.Time)) #self.Log("BE OUT dcount:" + str(self.dcount)) #self.Log("BE OUT outday:" + str(self.outday)) #self.Log("BE OUT adjwaitdays:" + str(adjwaitdays)) #self.Log("BE OUT outday + adjwaitdays:" + str(self.outday + adjwaitdays)) #self.Debug("Be OUT TRIGGERED: " + str(self.Time)) #self.Plot("Be Out", "dcount", self.dcount) #self.Plot("Be Out", "outday", self.outday) #self.Plot("Be Out", "adjwaitdays", adjwaitdays) #self.Plot("Be Out", "outday + adjwaitdays", self.outday + adjwaitdays) 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.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 MomentumSelectionFunction(self, momentum): #self.Debug(str(self.Time) + "MomentumSelectionFunction: 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 momentum 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 FundamentalSelectionFunction(self, fundamental): #self.Debug(str(self.Time) + "FundamentalSelectionFunction: 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 fundamental], 1, Resolution.Daily) try: filtered_fundamental = [x for x in fundamental 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_fundamental = [x for x in fundamental if (x.ValuationRatios.EVToEBITDA > 0) and (x.EarningReports.BasicAverageShares.ThreeMonths > 0)] top = sorted(filtered_fundamental, 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 rebalance(self): self.rebalence_flag = 1 #self.Debug(str(self.Time) + "rebalance: be_in:" + str(self.be_in) + " flip_flag:" + str(self.flip_flag)) spy_hist = self.History([self.MRKT], 120, Resolution.Daily).loc[str(self.MRKT)]['close'] if self.Securities[self.MRKT].Price < spy_hist.mean(): self.SetHoldings("TLT", 1, True) self.Log("Rebalance to TLT") 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.Create(symbol, SecurityType.Equity, Market.USA), 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 def record_vars(self): hist = self.History(self.MKT, 2, Resolution.Daily)['close'].unstack(level= 0).dropna() self.spy.append(hist[self.MKT].iloc[-1]) spy_perf = self.spy[-1] / self.spy[0] * self.cap self.Plot('Strategy Equity', 'SPY', spy_perf) account_leverage = self.Portfolio.TotalHoldingsValue / self.Portfolio.TotalPortfolioValue