Overall Statistics |
Total Trades 113 Average Win 8.39% Average Loss -0.98% Compounding Annual Return 25.801% Drawdown 13.800% Expectancy 7.055 Net Profit 2376.430% Sharpe Ratio 1.449 Probabilistic Sharpe Ratio 93.059% Loss Rate 16% Win Rate 84% Profit-Loss Ratio 8.60 Alpha 0.156 Beta 0.283 Annual Standard Deviation 0.125 Annual Variance 0.016 Information Ratio 0.555 Tracking Error 0.167 Treynor Ratio 0.638 Total Fees $0.00 Estimated Strategy Capacity $590000.00 Lowest Capacity Asset SPDN WB6RS4QDXLK5 |
# Import packages import numpy as np import pandas as pd import scipy as sc from scipy import stats class InOut(QCAlgorithm): def Initialize(self): self.SetStartDate(2008, 1, 1) # Set Start Date self.cap = 35000 self.SetCash(self.cap) # Set Strategy Cash res = Resolution.Minute # Holdings ### 'Out' holdings and weights self.HLD_OUT = {self.AddEquity('TLT', res).Symbol: 1} #TLT; TMF for 3xlev ### 'In' holdings and weights (static stock selection strategy) self.HLD_IN = {self.AddEquity('QQQ', res).Symbol: 1} #SPY or QQQ; TQQQ for 3xlev # Market and list of signals based on ETFs self.MRKT = self.AddEquity('QQQ', res).Symbol # market; QQQ 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.SIGNALS = [self.PRDC, self.METL, self.NRES, self.USDX, self.DEBT, self.MRKT] self.FORPAIRS = [self.GOLD, self.SLVA, self.UTIL, self.INDU] self.pairlist = ['G_S', 'U_I'] self.basket_in = ['QQQ'] self.basket_out = ['TLT', 'IEF', 'SPDN'] self.position = -1 for ticker in self.basket_in: self.AddEquity(ticker, res) self.Securities[ticker].FeeModel = ConstantFeeModel(0) for ticker in self.basket_out: self.AddEquity(ticker, res) self.Securities[ticker].FeeModel = ConstantFeeModel(0) # Initialize constants and variables self.INI_WAIT_DAYS, self.lookback, self.be_in, self.dcount, self.outday, self.portf_val = [15, 252*5, [1], 0, 0, [self.cap]] # [out for 3 trading weeks, set period for returns sample, 'In'/'out' indicator, count of total days since start, dcount when self.be_in=0, portfolio value] self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('QQQ', 120), self.inout_check) # Symbols for charts self.SPY = self.AddEquity('SPY', res).Symbol self.QQQ = self.MRKT # Setup daily consolidation symbols = list(set(self.SIGNALS + [self.MRKT] + self.FORPAIRS + list(self.HLD_OUT.keys()) + list(self.HLD_IN.keys()) + [self.SPY] + [self.QQQ])) 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.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() # Benchmarks for charts self.benchmarks = [self.history[self.SPY].iloc[-2], self.history[self.QQQ].iloc[-2]] 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 inout_check(self): if self.history.empty: return # Load saved dcount and outday (for live interruptions): if (self.dcount==0) and (self.outday==0) and (self.ObjectStore.ContainsKey('OS_counts')): OS_counts = self.ObjectStore.ReadBytes('OS_counts') OS_counts = pickle.loads(bytearray(OS_counts)) self.dcount, self.outday = [OS_counts['dcount'], OS_counts['outday']] # Returns sample to detect extreme observations 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]) # Extreme observations; statistical significance = 5% extreme_b = returns_sample.iloc[-1] < np.nanpercentile(returns_sample, 5, axis=0) # Re-assess/disambiguate double-edged signals abovemedian = returns_sample.iloc[-1] > np.nanmedian(returns_sample, axis=0) ### Interest rate expectations (cost of debt) may increase because the economic outlook improves (showing in rising input prices) = actually not a negative signal extreme_b.loc[self.DEBT] = np.where((extreme_b.loc[self.DEBT].any()) & (abovemedian[[self.METL, self.NRES]].any()), False, extreme_b.loc[self.DEBT]) # Determine whether 'in' or 'out' of the market if (extreme_b[self.SIGNALS + self.pairlist]).any(): self.be_in.append(0) self.outday = self.dcount if self.dcount >= self.outday + self.INI_WAIT_DAYS: self.be_in.append(1) current_portfolio = self.Portfolio.Keys # Swap to 'out' assets if applicable if not self.be_in[-1]: if self.position == -1 or self.position == 1: self.Liquidate() dataframe = self.History(self.basket_out, 180, Resolution.Daily) df = dataframe['close'].unstack(level=0) self.adaptive_asset_allocation(df, 3, 40, 90, self.Portfolio.Cash, 1) self.position = 0 if self.be_in[-1] and self.Time.weekday()==4: if self.position == -1 or self.position == 0: self.Liquidate() self.SetHoldings('QQQ', 1) self.position = 1 self.charts(extreme_b) self.dcount += 1 # Save data: day counts from live trading for interruptions (note: backtest saves data at the end so that it's available for live trading). if self.LiveMode: self.SaveData_Counts() def charts(self, extreme_b): # Market comparisons spy_perf = self.history[self.SPY].iloc[-1] / self.benchmarks[0] * self.cap qqq_perf = self.history[self.QQQ].iloc[-1] / self.benchmarks[1] * self.cap self.Plot('Strategy Equity', 'SPY', spy_perf) self.Plot('Strategy Equity', 'QQQ', qqq_perf) # Signals self.Plot("In Out", "in_market", self.be_in[-1]) self.Plot("Signals", "PRDC", int(extreme_b[self.SIGNALS + self.pairlist][0])) self.Plot("Signals", "METL", int(extreme_b[self.SIGNALS + self.pairlist][1])) self.Plot("Signals", "NRES", int(extreme_b[self.SIGNALS + self.pairlist][2])) self.Plot("Signals", "USDX", int(extreme_b[self.SIGNALS + self.pairlist][3])) self.Plot("Signals", "DEBT", int(extreme_b[self.SIGNALS + self.pairlist][4])) self.Plot("Signals", "G_S", int(extreme_b[self.SIGNALS + self.pairlist][5])) self.Plot("Signals", "U_I", int(extreme_b[self.SIGNALS + self.pairlist][6])) # Comparison of out returns self.portf_val.append(self.Portfolio.TotalPortfolioValue) if not self.be_in[-1] and len(self.be_in)>=2: period = np.where(np.array(self.be_in)[:-1] != np.array(self.be_in)[1:])[0][-1] - len(self.be_in) mrkt_ret = self.history[self.MRKT].iloc[-1] / self.history[self.MRKT].iloc[period] - 1 strat_ret = self.portf_val[-1] / self.portf_val[period] - 1 strat_vs_mrkt = round(float(strat_ret - mrkt_ret), 4) else: strat_vs_mrkt = 0 self.Plot('Out return', 'PF vs MRKT', strat_vs_mrkt) def trade(self, weight_by_sec): # sort: execute largest sells first, largest buys last hold_wt = {k: (self.Portfolio[k].Quantity*self.Portfolio[k].Price)/self.Portfolio.TotalPortfolioValue for k in self.Portfolio.Keys} order_wt = {k: weight_by_sec[k] - hold_wt.get(k, 0) for k in weight_by_sec} weight_by_sec = {k: weight_by_sec[k] for k in dict(sorted(order_wt.items(), key=lambda item: item[1]))} for sec, weight in weight_by_sec.items(): # Check that we have data in the algorithm to process a trade if not self.CurrentSlice.ContainsKey(sec) or self.CurrentSlice[sec] is None: continue # Only trade if holdings fundamentally change cond1 = (weight==0) and self.Portfolio[sec].IsLong cond2 = (weight>0) and not self.Portfolio[sec].Invested if cond1 or cond2: self.SetHoldings(sec, weight) def SaveData_Counts(self): counts = {"dcount": self.dcount, "outday": self.outday} self.ObjectStore.SaveBytes('OS_counts', pickle.dumps(counts)) def adaptive_asset_allocation(self, df, nlargest, volatility_window, return_window, portfolio_value, leverage): window_returns = np.log(df.iloc[-1]) - np.log(df.iloc[0]) nlargest = list(window_returns.nlargest(nlargest).index) returns = df[nlargest].pct_change() returns_cov_normalized = returns[-volatility_window:].apply(lambda x: np.log(1+x)).cov() returns_corr_normalized = returns[-volatility_window:].apply(lambda x: np.log(1+x)).corr() returns_std = returns.apply(lambda x: np.log(1+x)).std() port_returns = [] port_volatility = [] port_weights = [] num_assets = len(returns.columns) num_portfolios = 100 individual_rets = window_returns[nlargest] for port in range(num_portfolios): weights = np.random.random(num_assets) weights = weights/np.sum(weights) port_weights.append(weights) rets = np.dot(weights, individual_rets) port_returns.append(rets) var = returns_cov_normalized.mul(weights, axis=0).mul(weights, axis=1).sum().sum() sd = np.sqrt(var) ann_sd = sd * np.sqrt(256) port_volatility.append(ann_sd) data = {'Returns': port_returns, 'Volatility': port_volatility} hover_data = [] for counter, symbol in enumerate(nlargest): data[symbol] = [w[counter] for w in port_weights] hover_data.append(symbol) portfolios_V1 = pd.DataFrame(data) min_var_portfolio = portfolios_V1.iloc[portfolios_V1['Volatility'].idxmin()] max_sharpe_portfolio = portfolios_V1.iloc[(portfolios_V1['Returns'] / portfolios_V1['Volatility']).idxmax()] proportions = min_var_portfolio[nlargest] index = 0 for proportion in proportions: self.SetHoldings(nlargest[index], proportion * leverage) self.Debug('{}% of portfolio in {}'.format(proportion * leverage, nlargest[index])) index += 1