---
title: "Short Straddle"
description: "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."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/short-straddle
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/short-straddle.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/22%20Trading%20and%20Orders/08%20Option%20Strategies/25%20Short%20Straddle
modified: 2026-10-31
index: https://www.quantconnect.com/llms.txt
---

# Short Straddle

[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 Straddle

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

## 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](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.straddle(30))
   ```
   The `straddle` filter narrows the universe down to just the two contracts you need to form a short straddle.
2. In the `on_data` method, select the expiration date and strike price of the contracts in the strategy legs.
   ```python
   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
   ```
3. In the `on_data` method, select the contracts and place the orders.
   **Approach A:** Call the `OptionStrategies.short_straddle` method with the details of each leg and then pass the result to the `buy` method.
   ```python
   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](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
   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

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

In this example, you traded a call and a put, both at a strike of $835.00, for $19.60 and $21.40 respectively.

The following chart shows the payoff at expiration:

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

The maximum profit is $C^{ATM}_0 + P^{ATM}_0$. 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](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 (2017-05-20):

| Asset | Price ($) | Strike ($) |
| --- | --- | --- |
| Call | 19.6 | 835.00 |
| Put | 21.4 | 835.00 |
| Underlying Equity at early (2017-05-15) call assignment | 932.22 | - |
| Underlying Equity at expiration | 934.01 | - |

Therefore, the payoff is

$$ \begin{array}{rcll} C^{ATM}_T & = & (S_T - K^{C})^{+}\\ & = & (934.01-835.00)^{+}\\ & = & 99.01\\ P^{ATM}_T & = & (K^{P} - S_T)^{+}\\ & = & (835.00-934.01)^{+}\\ & = & 0\\ P_T & = & (-C^{ATM}_T - P^{ATM}_T + C^{ATM}_0 + P^{ATM}_0)\times m - fee\\ & = & (-99.01-0+19.6+21.4)\times 100 - 2.00\\ & = & -5803 \end{array} $$

So, the strategy loses $5,803. The early assigment doesn't influence the payoff.

The following algorithm implements a short straddle Option strategy:

```python
class LongStraddleAlgorithm(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.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)
```