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Option Strategies

Short Straddle

Introduction

A Short Straddle consists of selling a call and a put, where both contracts have the same underlying asset, strike price (normally at-the-money), and expiration date. If you enter a short straddle, you bet that the underlying asset will remain relatively stable and not experience significant price movements before the Option expires.

Implementation

Follow these steps to implement the short straddle strategy:

  1. In the initialize method, set the start date, end date, cash, and Option universe.
  2. Select Language:
    def initialize(self) -> None:
        self.set_start_date(2017, 4, 1)
        self.set_end_date(2017, 6, 30)
        self.set_cash(100000)
    
        self.universe_settings.asynchronous = True   
        option = self.add_option("GOOG")
        self._symbol = option.symbol
        option.set_filter(lambda universe: universe.include_weeklys().straddle(30))

    The straddle filter narrows the universe down to just the two contracts you need to form a short straddle.

  3. In the on_data method, select the expiration date and strike price of the contracts in the strategy legs.
  4. Select Language:
    def on_data(self, slice: Slice) -> None:
        if self.portfolio.invested:
            return
    
        chain = slice.option_chains.get(self._symbol, None)
        if not chain:
            return
    
        # Find ATM options with the nearest expiry
        expiry = min([x.expiry for x in chain])
        strike = sorted(chain, key=lambda x: abs(x.strike - chain.underlying.price))[0].strike
  5. In the on_data method, select the contracts and place the orders.
  6. Approach A: Call the OptionStrategies.short_straddle method with the details of each leg and then pass the result to the buy method.

    Select Language:
    short_straddle = OptionStrategies.short_straddle(self._symbol, contracts[0].strike, expiry)
    self.buy(short_straddle, 1)

    Approach B: Create a list of Leg objects and then call the combo_market_order, combo_limit_order, or combo_leg_limit_order method.

    Select Language:
    contracts = [x for x in chain if x.expiry == expiry and x.strike == strike]
    if len(contracts) < 2:
        return
    
    atm_call = [x for x in contracts if x.right == OptionRight.CALL][0]
    atm_put = [x for x in contracts if x.right == OptionRight.PUT][0]
    
    legs = [
        Leg.create(atm_call.symbol, -1),
        Leg.create(atm_put.symbol, -1)
    ]
    self.combo_market_order(legs, 1)

Strategy Payoff

The payoff of the strategy is

CATMT=(STKC)+PATMT=(KPST)+PT=(CATMTPATMT+CATM0+PATM0)×mfee
whereCATMT=ATM call value at time TPATMT=ATM put value at time TST=Underlying asset price at time TKC=ATM call strike priceKP=ATM put strike pricePT=Payout total at time TCATM0=ATM call value at position opening (debit paid)PATM0=ATM put value at position opening (debit paid)m=Contract multiplierT=Time of expiration

The following chart shows the payoff at expiration:

Strategy payoff decomposition and analysis of short straddle

The maximum profit is CATM0+PATM0. It occurs when the price of the underlying asset remains exactly at the strike price upon expiration.

The maximum loss is unlimited if the underlying price rises to infinity or drops to zero at expiration.

If the Option is American Option, there is a risk of early assignment on the contracts you sell.

Example

The following table shows the price details of the assets in the algorithm at Option expiration (2017-05-20):

AssetPrice ($)Strike ($)
Call19.6835.00
Put21.4835.00
Underlying Equity at early (2017-05-15) call assignment932.22-
Underlying Equity at expiration934.01-

Therefore, the payoff is

CATMT=(STKC)+=(934.01835.00)+=99.01PATMT=(KPST)+=(835.00934.01)+=0PT=(CATMTPATMT+CATM0+PATM0)×mfee=(99.010+19.6+21.4)×1002.00=5803

So, the strategy loses $5,277. The early assigment doesn't influence the payoff.

The following algorithm implements a short straddle Option strategy:

Select Language:
class LongStraddleAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2017, 4, 1)
        self.set_end_date(2017, 6, 30)
        self.set_cash(100000)
        
        option = self.add_option("GOOG")
        self.symbol = option.symbol
        option.set_filter(lambda universe: universe.include_weeklys().straddle(30))

    def on_data(self, slice: Slice) -> None:
        if self.portfolio.invested:
            return

        chain = slice.option_chains.get(self.symbol, None)
        if not chain:
            return

        # Find ATM options with the nearest expiry
        expiry = min([x.expiry for x in chain])
        contracts = sorted([x for x in chain if x.expiry == expiry],
            key=lambda x: abs(chain.underlying.price - x.strike))
        
        if len(contracts) < 2:
            return

        # The first two contracts are the ATM Call and the ATM Put
        contracts = contracts[0:2]

        short_straddle = OptionStrategies.short_straddle(self.symbol, contracts[0].strike, expiry)
        self.buy(short_straddle, 1)

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