---
title: "Long Put Backspread"
description: "Long put backspread, a combination of bull put spread and long put, consists of selling a higher-strike put and buying two lower-srtike puts with the same expiry."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/long-put-backspread
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/long-put-backspread.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/22%20Trading%20and%20Orders/08%20Option%20Strategies/40%20Long%20Put%20Backspread
modified: 2026-10-31
index: https://www.quantconnect.com/llms.txt
---

# Long Put Backspread

[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) > Long Put Backspread

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

## Introduction

**Long Put Backspread**, consists of short 1 higher-strike put and long 2 lower-strike puts. It is a combination of a [bull put spread](https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/bull-put-spread.md) and a long put with the same strike price as the lower-strike leg the put spread. All puts have the same underlying Equity and expiration date. This strategy profits from increasing volatility of the underlying asset. For instance, the underlying price moves away from its current price.

## Implementation

Follow these steps to implement the long put backspread 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). You can use the `put_spread` helper method in option universe filtering, since a put backspread consists of the same contracts as a put spread.
   ```python
   def initialize(self) -> None:
       self.set_start_date(2024, 9, 1)
       self.set_end_date(2024, 12, 31)
       self.set_cash(1000000)

       self.universe_settings.asynchronous = True
       option = self.add_option("GOOG", Resolution.MINUTE)
       self._symbol = option.symbol
       option.set_filter(lambda universe: universe.put_spread(20, 5))
   ```
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]
       if not puts:
           return

       # Select the strike prices from the remaining contracts
       strikes = sorted(set(x.strike for x in puts))
       if len(strikes) < 2:
           return

       low_strike = strikes[0]
       high_strike = strikes[1]
   ```
3. In the `on_data` method, select the contracts and place the orders.
   **Approach A:** Call the `OptionStrategies.put_backspread` method with the details of each leg and then pass the result to the `buy` method.
   ```python
   option_strategy = OptionStrategies.put_backspread(self._symbol, high_strike, low_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
   low_strike_put = next(filter(lambda x: x.strike == low_strike, puts))
   high_strike_put = next(filter(lambda x: x.strike == high_strike, puts))

   legs = [
       Leg.create(low_strike_put.symbol, 2),
       Leg.create(high_strike_put.symbol, -1)
   ]
   self.combo_market_order(legs, 1)
   ```

## Strategy Payoff

The long put backspread is an unlimited-profit-limited-risk strategy. The payoff is

$$ \begin{array}{rcll} P^{low}_T & = & (K^{low} - S_T)^{+}\\ P^{high}_T & = & (K^{high} - S_T)^{+}\\ Payoff_T & = & (P^{high}_0 - P^{high}_T + P^{low}_T \times 2 - P^{low}_0 \times 2)\times m - fee\\ \end{array} $$ $$ \begin{array}{rcll} \textrm{where} & P^{low}_T & = & \textrm{Lower-strike put value at time T}\\ & P^{high}_T & = & \textrm{Higher-strike put value at time T}\\ & S_T & = & \textrm{Underlying asset price at time T}\\ & K^{low} & = & \textrm{Lower-strike put strike price}\\ & K^{high} & = & \textrm{Higher-strike put strike price}\\ & P^{low}_0 & = & \textrm{Lower-strike put value at position opening (credit received)}\\ & P^{high}_0 & = & \textrm{Higher-strike put value at position opening (debit paid)}\\ & m & = & \textrm{Contract multiplier}\\ & T & = & \textrm{Time of expiration} \end{array} $$

In this example, the underlying stock price at expiration is $843.19. You traded two lower-strike puts at $825.00 for $6.90 each and a higher-strike put at $835.00 for $8.50.

The following chart shows the payoff at expiration:

![Long Put Backspread strategy payoff at expiration](https://cdn.quantconnect.com/docs/i/writing-algorithms/trading-and-orders/option-strategies/long-put-backspread.png)

The maximum profit is unlimited, which occurs when the underlying price increases indefinitely.

The maximum loss is $K^{low} - K^{high} + P^{high}_0 - P^{low}_0 \times 2$, which occurs when the underlying price is exactly at the lower strike at expiry.

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 ($) | Strike ($) |
| --- | --- | --- |
| Lower-Strike put | 6.90 | 825.00 |
| Higher-strike put | 8.50 | 835.00 |
| Underlying Equity at expiration | 843.19 | - |

Therefore, the payoff is

$$ \begin{array}{rcll} P^{low}_T & = & (K^{low} - S_T)^{+}\\ & = & (825.00-843.19)^{+}\\ & = & 0.00\\ P^{high}_T & = & (K^{high} - S_T)^{+}\\ & = & (835.00-843.19)^{+}\\ & = & 0.00\\ Payoff_T & = & (P^{high}_0 - P^{high}_T + P^{low}_T \times 2 - P^{low}_0 \times 2)\times m - fee\\ & = & (8.50 - 0.00 + 0.00\times2 - 6.90\times2)\times100-2.30\\ & = & -532.30\\ \end{array} $$

So, the strategy loses $532.30.

The following algorithm implements a long put backspread Option strategy:

```python
class BackspreadOptionStrategyAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self.set_start_date(2024, 9, 1)
        self.set_end_date(2024, 12, 31)
        self.set_cash(1000000)

        self.universe_settings.asynchronous = True
        option = self.add_option("GOOG", Resolution.MINUTE)
        self._symbol = option.symbol
        option.set_filter(lambda universe: universe.put_spread(20, 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

        # Select the call Option contracts with the furthest expiry
        expiry = max([x.expiry for x in chain])
        puts = [i for i in chain if i.expiry == expiry]
        if not puts:
            return

        # Select the strike prices from the remaining contracts
        strikes = sorted(set(x.strike for x in puts))
        if len(strikes) < 2:
            return

        low_strike = strikes[0]
        high_strike = strikes[1]

        option_strategy = OptionStrategies.put_backspread(self._symbol, high_strike, low_strike, expiry)
        self.buy(option_strategy, 1)
```