---
title: "Short Strangle"
description: "A Short Strangle is an Options trading strategy that consists of simultaneously selling an OTM put and an OTM call, where both contracts have the same underlying asset and expiration date."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/short-strangle
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/short-strangle.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/22%20Trading%20and%20Orders/08%20Option%20Strategies/27%20Short%20Strangle
modified: 2026-10-30
index: https://www.quantconnect.com/llms.txt
---

# Short Strangle

[Documentation](https://www.quantconnect.com/docs/v2.md) > [Writing Algorithms](https://www.quantconnect.com/docs/v2/writing-algorithms.md) > Trading and Orders > [Option Strategies](https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies.md) > Short Strangle

> Code examples are shown in Python. Append `?lang=csharp` to this URL for the C# version of this page.

## Introduction

**Short Strangle** is an Options trading strategy that consists of simultaneously selling an OTM put and an OTM call, where both contracts have the same underlying asset and expiration date. By doing so, the trader is essentially betting that the underlying asset will remain relatively stable and not experience significant price movements before the Options' expiration.

Compared to a [short straddle](https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/short-straddle.md), the net debit of a short strangle is lower since OTM Options are cheaper. Additionally, the winning range of a short straddle is wider and the strike spread is wider.

## Implementation

Follow these steps to implement the short strangle strategy:

1. In the `initialize` method, set the start date, end date, cash, and [Option universe](https://www.quantconnect.com/docs/v2/writing-algorithms/universes/equity-options.md).
   ```python
   def initialize(self) -> None:
       self.set_start_date(2024, 9, 1)
       self.set_end_date(2024, 12, 31)
       self.set_cash(100000)

       self.universe_settings.asynchronous = True
       option = self.add_option("GOOG")
       self._symbol = option.symbol
       option.set_filter(lambda universe: universe.strangle(30, 5, -10))
   ```
   The `strangle` filter narrows the universe down to just the two contracts you need to form a short strangle.
2. In the `on_data` method, select the contracts of the strategy legs.
   ```python
   def on_data(self, slice: Slice) -> None:
       if self.portfolio.invested:
           return

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

       # Find options with the farthest expiry
       expiry = max([x.expiry for x in chain])
       contracts = [contract for contract in chain if contract.expiry == expiry]

       # Order the OTM calls by strike to find the nearest to ATM
       call_contracts = sorted([contract for contract in contracts
           if contract.right == OptionRight.CALL and
               contract.strike > chain.underlying.price],
           key=lambda x: x.strike)
       if not call_contracts:
           return

       # Order the OTM puts by strike to find the nearest to ATM
       put_contracts = sorted([contract for contract in contracts
           if contract.right == OptionRight.PUT and
              contract.strike < chain.underlying.price],
           key=lambda x: x.strike, reverse=True)
       if not put_contracts:
           return

       call = call_contracts[0]
       put = put_contracts[0]
   ```
3. In the `on_data` method, place the orders.
   **Approach A:** Call the `OptionStrategies.short_strangle` method with the details of each leg and then pass the result to the `buy` method.
   ```python
   short_strangle = OptionStrategies.short_strangle(self._symbol, call.strike, put.strike, expiry)
   self.buy(short_strangle, 1)
   ```
   **Approach B:** Create a list of `Leg` objects and then call the [combo_market_order](https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/order-types/combo-market-orders.md), [combo_limit_order](https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/order-types/combo-limit-orders.md), or [combo_leg_limit_order](https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/order-types/combo-leg-limit-orders.md) method.
   ```python
   legs = [
       Leg.create(call.symbol, -1),
       Leg.create(put.symbol, -1)
   ]
   self.combo_market_order(legs, 1)
   ```

## Strategy Payoff

The payoff of the strategy is

$$ \begin{array}{rcll} C^{OTM}_T & = & (S_T - K^{C})^{+}\\ P^{OTM}_T & = & (K^{P} - S_T)^{+}\\ P_T & = & (-C^{OTM}_T - P^{OTM}_T + C^{OTM}_0 + P^{OTM}_0)\times m - fee \end{array} $$ $$ \begin{array}{rcll} \textrm{where} & C^{OTM}_T & = & \textrm{OTM call value at time T}\\ & P^{OTM}_T & = & \textrm{OTM put value at time T}\\ & S_T & = & \textrm{Underlying asset price at time T}\\ & K^{C} & = & \textrm{OTM call strike price}\\ & K^{P} & = & \textrm{OTM put strike price}\\ & P_T & = & \textrm{Payout total at time T}\\ & C^{OTM}_0 & = & \textrm{OTM call value at position opening (debit paid)}\\ & P^{OTM}_0 & = & \textrm{OTM put value at position opening (debit paid)}\\ & m & = & \textrm{Contract multiplier}\\ & T & = & \textrm{Time of expiration} \end{array} $$

In this example, you traded a call at a strike of $835.00 for $8.00 and a put at a strike of $832.50 for $7.40.

The following chart shows the payoff at expiration:

![Short Strangle strategy payoff at expiration](https://cdn.quantconnect.com/docs/i/writing-algorithms/trading-and-orders/option-strategies/short-strangle.png)

The maximum profit $C^{OTM}_0 + P^{OTM}_0$. It occurs when the underlying price at expiration remains within the range of the strike prices. In this case, both Options expire worthless.

The maximum loss is unlimited if the underlying price rises to infinity or substantial, $C^{OTM}_0 + P^{OTM}_0 - K^{P}$, if it drops to zero at expiration.

If the Option is American Option, there is a risk of [early assignment](https://www.quantconnect.com/docs/v2/writing-algorithms/reality-modeling/options-models/assignment.md) on the contracts you sell.

## Example

The following table shows the price details of the assets in the algorithm at Option expiration (2017-04-22):

| Asset | Price ($) | Strike ($) |
| --- | --- | --- |
| Call | 8.00 | 835.00 |
| Put | 7.40 | 832.50 |
| Underlying Equity at expiration | 843.19 | - |

Therefore, the payoff is

$$ \begin{array}{rcll} C^{OTM}_T & = & (S_T - K^{C})^{+}\\ & = & (843.19-835.00)^{+}\\ & = & 8.19\\ P^{OTM}_T & = & (K^{P} - S_T)^{+}\\ & = & (832.50-843.19)^{+}\\ & = & 0\\ P_T & = & (-C^{OTM}_T - P^{OTM}_T + C^{OTM}_0 + P^{OTM}_0)\times m - fee\\ & = & (-8.19-0+8.00+7.40)\times100-1.00\times2\\ & = & 719 \end{array} $$

So, the strategy gains $719.

The following algorithm implements a short strangle Option strategy:

```python
class ShortStrangleAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2024, 9, 1)
        self.set_end_date(2024, 12, 31)
        self.set_cash(100000)

        option = self.add_option("GOOG")
        self.symbol = option.symbol
        option.set_filter(lambda universe: universe.strangle(30, 5, -10))

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

        # Get the OptionChain
        chain = slice.option_chains.get(self.symbol)
        if not chain:
            return

        # Find options with the nearest expiry
        expiry = max([x.expiry for x in chain])
        contracts = [contract for contract in chain if contract.expiry == expiry]

        # Order the OTM calls by strike to find the nearest to ATM
        call_contracts = sorted([contract for contract in contracts
            if contract.right == OptionRight.CALL and
               contract.strike > chain.underlying.price],
            key=lambda x: x.strike)
        if not call_contracts:
            return

        # Order the OTM puts by strike to find the nearest to ATM
        put_contracts = sorted([contract for contract in contracts
            if contract.right == OptionRight.PUT and
               contract.strike < chain.underlying.price],
            key=lambda x: x.strike, reverse=True)
        if not put_contracts:
            return

        call_strike = call_contracts[0].strike
        put_strike = put_contracts[0].strike

        short_strangle = OptionStrategies.short_strangle(self.symbol, call_strike, put_strike, expiry)
        self.buy(short_strangle, 1)
```