| 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