---
title: "Bear Put Spread"
description: "Bear put spread, also known as short put spread, consists of buying an ITM put and selling an OTM put."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/bear-put-spread
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/bear-put-spread.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/22%20Trading%20and%20Orders/08%20Option%20Strategies/02%20Bear%20Put%20Spread
modified: 2026-10-31
index: https://www.quantconnect.com/llms.txt
---

# Bear Put Spread

[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) > Bear Put Spread

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

## Introduction

**Bear put spread**, also known as **short put spread**, consists of buying an ITM put and selling an OTM put. Both puts have the same underlying Equity and the same expiration date. The OTM put serves as a hedge for the ITM put. The bear put spread profits from a decline in underlying asset price.

## Implementation

Follow these steps to implement the bear put spread 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(500000)

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

       # Get the OptionChain
       chain = slice.option_chains.get(self._symbol, None)
       if not chain:
           return

       # Select the put Option contracts with the furthest expiry
       expiry = max([x.expiry for x in chain])
       puts = [i for i in chain if i.expiry == expiry and i.right == OptionRight.PUT]
       if not puts == 0:
           return

       # Select the ITM and OTM contract strike prices from the remaining contracts
       strikes = [x.strike for x in puts]
       otm_strike = min(strikes)
       itm_strike = max(strikes)
   ```
3. In the `on_data` method, select the contracts and place the orders.
   **Approach A:** Call the `OptionStrategies.bear_put_spread` method with the details of each leg and then pass the result to the `buy` method.
   ```python
   option_strategy = OptionStrategies.bear_put_spread(self._symbol, itm_strike, otm_strike, expiry)
   self.buy(option_strategy, 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
   itm_put = [x for x in puts if x.strike == itm_strike][0]
   otm_put = [x for x in puts if x.strike == otm_strike][0]

   legs = [
       Leg.create(itm_put.symbol, 1),
       Leg.create(otm_put.symbol, -1)
   ]
   self.combo_market_order(legs, 1)
   ```

## Strategy Payoff

The bear put spread is a limited-reward-limited-risk strategy. The payoff is

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

In this example, the underlying stock price is $829.08 at expiration. You sold an OTM put at a strike of $767.50 for $4.60 and bought an ITM put at a strike of $835.00 for $40.00.

The following chart shows the payoff at expiration:

![Bear Put Spread strategy payoff at expiration](https://cdn.quantconnect.com/docs/i/writing-algorithms/trading-and-orders/option-strategies/bear-put-spread.png)

The maximum profit is $K^{ITM} - K^{OTM} + P^{OTM}_0 - P^{ITM}_0$. If the underlying price is below than the strike prices of both put Option contracts, they are worth $(K - S_T)$ at expiration.

The maximum loss is the net debit you paid to open the position, $P^{OTM}_0 - P^{ITM}_0$. 

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 contract you sell.

## Example

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

| Asset | Price ($) | Strike ($) |
| --- | --- | --- |
| OTM put | 4.60 | 767.50 |
| ITM put | 40.00 | 835.00 |
| Underlying Equity at expiration | 829.08 | - |

Therefore, the payoff is

$$ \begin{array}{rcll} P^{OTM}_T & = & (K^{OTM} - S_T)^{+}\\ & = & (767.50-829.08)^{+}\\ & = & 0\\ P^{ITM}_T & = & (K^{ITM} - S_T)^{+}\\ & = & (835.00-829.08)^{+}\\ & = & 5.92\\ P_T & = & (P^{ITM}_T - P^{OTM}_T + P^{OTM}_0 - P^{ITM}_0)\times m - fee\\ & = & (5.92-0+4.60-40.00)\times100-1.00\times2\\ & = & -2950\\ \end{array} $$

So, the strategy loses $2,950.

The following algorithm implements a bear put spread strategy:

```python
class BearPutSpreadStrategy(QCAlgorithm):
    def initialize(self) -> None:
        self.set_start_date(2024, 9, 1)
        self.set_end_date(2024, 12, 31)
        self.set_cash(500000)

        option = self.add_option("GOOG", Resolution.MINUTE)
        self.symbol = option.symbol
        option.set_filter(self.universe_func)

    def universe_func(self, universe: OptionFilterUniverse) -> OptionFilterUniverse:
        return universe.put_spread(30, 5)

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

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

        # Get the furthest expiry date of the contracts
        expiry = sorted(chain, key = lambda x: x.expiry, reverse=True)[0].expiry

        # Select the put Option contracts with the furthest expiry
        puts = [i for i in chain if i.expiry == expiry and i.right == OptionRight.PUT]
        if len(puts) == 0: return

        # Select the ITM and OTM contract strike prices from the remaining contracts
        put_strikes = sorted([x.strike for x in puts])
        otm_strike = put_strikes[0]
        itm_strike = put_strikes[-1]

        option_strategy = OptionStrategies.bear_put_spread(self.symbol, itm_strike, otm_strike, expiry)
        self.buy(option_strategy, 1)
```

### Other Examples

For more examples, see the following algorithms:

**Demonstration Algorithm**

- [IndexOptionBearPutSpreadAlgorithm.py](https://github.com/QuantConnect/Lean/blob/master/Algorithm.Python/IndexOptionBearPutSpreadAlgorithm.py) (Python)