Overall Statistics |
Total Orders 511 Average Win 8.80% Average Loss -5.84% Compounding Annual Return 17.260% Drawdown 65.300% Expectancy 0.340 Start Equity 100000 End Equity 1056775.13 Net Profit 956.775% Sharpe Ratio 0.489 Sortino Ratio 0.364 Probabilistic Sharpe Ratio 0.850% Loss Rate 47% Win Rate 53% Profit-Loss Ratio 1.51 Alpha 0.184 Beta -0.28 Annual Standard Deviation 0.325 Annual Variance 0.106 Information Ratio 0.19 Tracking Error 0.37 Treynor Ratio -0.568 Total Fees $33625.44 Estimated Strategy Capacity $510000.00 Lowest Capacity Asset VIXY UT076X30D0MD Portfolio Turnover 9.39% |
# https://quantpedia.com/strategies/trading-vix-etfs-v2/ # # Investment universe consists of SPDR S&P500 Trust ETF (SPY) and ProShares Short S&P500 ETF (SH) for long and short exposure to the # S&P500 and iPath S&P500 VIX ST Futures ETN (VXX) and VelocityShares Daily Inverse VIX ST ETN (XIV) for long and short exposure to # short-term VIX futures. First, the relative difference between the front-month VIX futures and spot VIX is calculated # (contango/backwardation check). If the relative basis is above (below) an upper (lower) buy threshold, BU (BL) determined by the trader, # it indicates that the market is in contango (backwardation) and that one should hold XIV (VXX) and hedge with SH (SPY). The position is # closed when the relative basis falls below an upper (lower) sell-threshold, SU (SL), which may be set equal to, or lower (higher) than # the buy-threshold. A reason why one might want the upper (lower) sell-threshold lower (higher) than the upper (lower) buy-threshold is # to avoid too-frequent trading. The best results are with a 0% hedge ratio (trader doesn’t use SPY/SH hedging). However, it is possible # to use multiple different hedging levels with different results (see table 10 in a source academic paper for more options). from QuantConnect.Python import PythonQuandl from AlgorithmImports import * class TradingVIXETFsv2(QCAlgorithm): def Initialize(self): self.SetStartDate(2010, 1, 1) self.SetCash(100000) self.vixy = self.AddEquity('VIXY', Resolution.Minute).Symbol # Vix futures data. self.vix_future = self.AddFuture(Futures.Indices.VIX, Resolution.Minute) # Vix spot. self.vix_spot = self.AddData(CBOE, 'VIX', Resolution.Daily).Symbol self.vix_future.SetFilter(timedelta(0), timedelta(30)) # Vix futures active contract updated on expiration. self.active_contract = None self.Schedule.On(self.DateRules.EveryDay(self.vixy), self.TimeRules.AfterMarketOpen(self.vixy, 1), self.Rebalance) def Rebalance(self): # split data error prevention if self.Time.year == 2021 and self.Time.month == 5: self.Liquidate() return if self.active_contract: if self.Securities.ContainsKey(self.vix_spot): spot_price = self.Securities[self.vix_spot].Price vix_future_price = self.active_contract.LastPrice if spot_price == 0 or vix_future_price == 0: return relative_basis = vix_future_price / spot_price # BU 8%, SU 6%, BL -8%, SL -6% thresholds. # Short volatility. if relative_basis > 1.08: if not self.Portfolio[self.vixy].IsShort and self.Securities[self.vixy].Price != 0: self.SetHoldings(self.vixy, -1) if relative_basis >= 1.06 and relative_basis <= 1.08 and self.Portfolio[self.vixy].IsLong: self.Liquidate(self.vixy) if relative_basis < 1.06 and relative_basis > 0.94: if self.Portfolio[self.vixy].Invested: self.Liquidate(self.vixy) if relative_basis <= 0.94 and relative_basis >= 0.92 and self.Portfolio[self.vixy].IsShort: self.Liquidate(self.vixy) # Long volatility. if not self.Portfolio[self.vixy].IsLong and relative_basis < 0.92: if self.Securities[self.vixy].Price != 0: self.SetHoldings(self.vixy, 1) def OnData(self, slice): chains = [x for x in slice.FutureChains] cl_chain = None if len(chains) > 0: cl_chain = chains[0] else: return if cl_chain.Value.Contracts.Count >= 1: contracts = [i for i in cl_chain.Value] contracts = sorted(contracts, key = lambda x: x.Expiry) near_contract = contracts[0] self.active_contract = near_contract