| Overall Statistics |
|
Total Trades 53 Average Win 0.22% Average Loss -1.24% Compounding Annual Return 58.788% Drawdown 10.500% Expectancy -0.410 Net Profit 114.024% Sharpe Ratio 3.293 Probabilistic Sharpe Ratio 98.466% Loss Rate 50% Win Rate 50% Profit-Loss Ratio 0.18 Alpha 0.48 Beta 0.402 Annual Standard Deviation 0.179 Annual Variance 0.032 Information Ratio 1.467 Tracking Error 0.215 Treynor Ratio 1.469 Total Fees $44.43 Estimated Strategy Capacity $39000000.00 Lowest Capacity Asset VIX XR3PT72RZWTQ|VIX 31 |
# Hedge using options on VIX
#
# The investment universe consists of a stock/bond portfolio with a proportion of 60-percent stocks and 40-percent bonds.
# Stocks are represented by the SPDR S&P 500 ETF Trust (SPY) and bonds by the iShares 7-10 Year Treasury Bond ETF (IEF).
# The strategy firstly invests 0-100 basis points (bsp) in the desired VIX call option, then allocates 60 percent of the
# portfolio to the SPY and the remaining 40 percent to the IEF. The option is bought at the level of 135% of the moneyness
# of the underlying VIX futures price. The strategy is systematically purchasing an equal amount in one-month, two-month,
# three-month and four-month VIX call options on VIX futures. If the VIX Index is between 15 and 30, the weight of VIX calls
# in the portfolio is 1%. If the VIX Index is between 30 and 50, the weight in the portfolio is 0,5%. If the VIX Index is over
# 50 or under 15, then the weight of options in the portfolio is 0%. Each month, on the day before expiration, the options are
# rolled to the appropriate expiry. VIX call options are purchased at the offer and sold at the bid to keep the assumptions
# conservative. The options are held to maturity and closed the Tuesday afternoon before the Wednesday morning of VIX futures
# and options expiration. If the contracts have any intrinsic value, they are sold at the bid price, and the cash is used at
# the end of the month to rebalance the stock/bond portion of the portfolio.
import numpy as np
class PortfolioHedgingUsingVIXOptions(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2020, 1, 1)
self.SetCash(1000000)
data = self.AddEquity("SPY", Resolution.Minute)
data.SetLeverage(5)
self.spy = data.Symbol
data = self.AddEquity("IEF", Resolution.Minute)
data.SetLeverage(5)
self.ief = data.Symbol
self.vix = 'VIX'
option = self.AddIndexOption('VIX', Resolution.Minute)
option.SetFilter(-20, 20, 25, 35)
def OnData(self,slice):
for i in slice.OptionChains:
chains = i.Value
# Max 2 positions - spy and ief are opened. That means option expired.
invested = [x.Key for x in self.Portfolio if x.Value.Invested]
if len(invested) <= 1:
calls = list(filter(lambda x: x.Right == OptionRight.Call, chains))
if not calls: return
underlying_price = self.Securities[self.vix].Price
expiries = [i.Expiry for i in calls]
# Determine expiration date nearly one month.
expiry = min(expiries, key=lambda x: abs((x.date() - self.Time.date()).days - 30))
strikes = [i.Strike for i in calls]
# Determine out-of-the-money strike.
otm_strike = min(strikes, key = lambda x:abs(x - (float(1.35) * underlying_price)))
otm_call = [i for i in calls if i.Expiry == expiry and i.Strike == otm_strike]
if otm_call:
# Option weighting.
weight = 0.0
if underlying_price >= 15 and underlying_price <= 30:
weight = 0.01
elif underlying_price > 30 and underlying_price <= 50:
weight = 0.005
if weight != 0:
option_price = otm_call[0].AskPrice
if np.isnan(option_price) or option_price <= 0:
for call in calls:
option_price = call.AskPrice
if not (np.isnan(option_price) or option_price <= 0):
break
options_q = int((self.Portfolio.MarginRemaining * weight) / (option_price * 100))
# Set max leverage.
self.Securities[otm_call[0].Symbol].MarginModel = BuyingPowerModel(5)
# Buy out-the-money call.
self.Buy(otm_call[0].Symbol, options_q)
self.SetHoldings(self.spy, 1.0)
self.SetHoldings(self.ief, 0.0)