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Option Strategies

Covered Put

Introduction

A Covered Put consists of a short position in a stock and a short position in put Options for the same amount of stock. Covered puts aim to profit from the Option premium by selling puts written on the stock you already shorted. At any time for American Options or at expiration for European Options, if the stock moves below the strike price, you keep the premium and still maintain the underlying Equity position. If the underlying price moves above the strike, the Option buyer can exercise the Options contract, which mean you buy the stock at the strike price but you will still keep the premium. Another risk of a covered put comes from the short stock position, which can drop in value.

Implementation

Follow these steps to implement the covered put strategy:

  1. In the initialize method, set the start date, end date, starting cash, and Options universe.
    2. Select Language: 
    def initialize(self) -> None:
    self.set_start_date(2014, 1, 1)
    self.set_end_date(2014, 3, 1)
    self.set_cash(100000)

    self.universe_settings.asynchronous = True
    option = self.add_option("IBM")
    self._symbol = option.symbol
    option.set_filter(lambda universe: universe.include_weeklys().naked_put(30, 0))

    The naked_put filter narrows the universe down to just the one contract you need to form a covered put.

  2. In the on_data method, select the Option contract.
    3. Select Language: 
    def on_data(self, slice: Slice) -> None:
    if self.portfolio.invested:
        return

    chain = slice.option_chains.get(self._symbol)
    if not chain:
        return

    # Find ATM put with the farthest expiry
    expiry = max([x.expiry for x in chain])
    put_contracts = sorted([x for x in chain
        if x.right == OptionRight.PUT and x.expiry == expiry],
        key=lambda x: abs(chain.underlying.price - x.strike))

    if not put_contracts:
        return

    atm_put = put_contracts[0]
  3. In the on_data method, place the orders.

    4. Approach A: Call the OptionStrategies.covered_put method with the details of each leg and then pass the result to the buy method.

    Select Language: 
    covered_put = OptionStrategies.covered_put(self._symbol, atm_put.strike, expiry)
self.buy(covered_put, 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: 
    legs = [
    Leg.create(atm_put.symbol, -1),
    Leg.create(chain.underlying.symbol, -chain.underlying.symbol_properties.contract_multiplier)
]
self.combo_market_order(legs, 1)

Strategy Payoff

The payoff of the strategy is

PKT=(K−ST)+PT=(S0−ST+PK0−PKT)×m−fee wherePKT=Put value at time TST=Underlying asset price at time TK=Put strike pricePT=Payout total at time TS0=Underlying asset price when the trade openedPK0=Put price when the trade opened (credit received)m=Contract multiplierT=Time of expiration

The following chart shows the payoff at expiration:

Strategy payoff decomposition and analysis of covered put

The maximum profit is ST−K+PK0. It occurs when the underlying price is at or below the strike price of the put at expiration.

If the underlying price increase, the maximum loss is unlimited.

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

Example

The following table shows the price details of the assets in the algorithm:

AssetPrice ($)Strike ($)
Put1.37185.00
Underlying Equity at start of the trade186.94-
Underlying Equity at expiration190.01-

Therefore, the payoff is

PKT=(K−ST)+=(185−190.01)+=0PT=(S0−ST+PK0−PKT)×m−fee=(186.94−190.01+1.37−0)×m−fee=−1.70×100−2=−172

So, the strategy loses $172.

The following algorithm implements a covered put strategy:

Select Language: 
class CoveredputAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self.set_start_date(2014, 1, 1)
        self.set_end_date(2014, 3, 1)
        self.set_cash(100000)

        option = self.add_option("IBM")
        self.symbol = option.symbol
        option.set_filter(lambda universe: universe.include_weeklys().naked_put(30, 0))

        self.put = None

        # use the underlying equity as the benchmark
        self.set_benchmark(self.symbol.underlying)

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

        chain = slice.option_chains.get(self.symbol)
        if not chain:
            return

        # Find ATM put with the farthest expiry
        expiry = max([x.expiry for x in chain])
        put_contracts = sorted([x for x in chain
            if x.right == OptionRight.PUT and x.expiry == expiry],
            key=lambda x: abs(chain.underlying.price - x.strike))

        if not put_contracts:
            return

        atm_put = put_contracts[0]

        covered_put = OptionStrategies.covered_put(self.symbol, atm_put.strike, expiry)
        self.buy(covered_put, 1)

        self.put = atm_put.symbol

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