---
title: "Bear Put Ladder"
description: "Bear put ladder consists of buying a put and selling two puts with decreasing strike price order and the same expiry."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/bear-put-ladder
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/bear-put-ladder.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/22%20Trading%20and%20Orders/08%20Option%20Strategies/35%20Bear%20Put%20Ladder
modified: 2026-10-30
index: https://www.quantconnect.com/llms.txt
---

# Bear Put Ladder

[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 Ladder

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

## Introduction

**Bear put ladder**, also known as **long put ladder**, is a combination of a [bear put spread](https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/bear-put-spread.md) and short put with a lower strike price than the 2 legs of the put spread. All puts have the same underlying Equity and expiration date. This strategy profits from low volatility of the underlying asset. For instance, the underlying price stays similar to its current price.

## Implementation

Follow these steps to implement the bear put ladder 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(1000000)

       self.universe_settings.asynchronous = True
       option = self.add_option("GOOG", Resolution.MINUTE)
       self._symbol = option.symbol
       option.set_filter(lambda universe: universe.put_ladder(30, 5, 0, -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 and i.right == OptionRight.PUT]
       if not puts:
           return

       # Select the strike prices from the remaining contracts
       strikes = sorted(set(x.strike for x in puts))
       if len(strikes) < 3:
           return

       low_strike = strikes[0]
       middle_strike = strikes[1]
       high_strike = strikes[2]
   ```
3. In the `on_data` method, select the contracts and place the orders.
   **Approach A:** Put the `OptionStrategies.bear_put_ladder` method with the details of each leg and then pass the result to the `buy` method.
   ```python
   option_strategy = OptionStrategies.bear_put_ladder(self._symbol, high_strike, middle_strike, low_strike, expiry)
   self.buy(option_strategy, 1)
   ```
   **Approach B:** Create a list of `Leg` objects and then put 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))
   middle_strike_put = next(filter(lambda x: x.strike == middle_strike, puts))
   high_strike_put = next(filter(lambda x: x.strike == high_strike, puts))

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

## Strategy Payoff

The bear put ladding is an limited-profit strategy. The payoff is

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

In this example, the underlying stock price at expiration is $843.25. You traded a lower-strike put at $822.50 for $3.80, a middle-strike put at $825.00 for $4.70, and a higher-strike put at $827.50 for $7.80.

The following chart shows the payoff at expiration:

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

The maximum profit is $K^{high} - K^{mid} + P^{low}_0 + P^{mid}_0 - P^{high}_0$, which occurs when the underlying price is between the two lower strike prices.

The maximum loss is $K^{high} - K^{mid} - K^{low} + P^{low}_0 + P^{mid}_0 - P^{high}_0$, which occurs when the underlying price decreases to $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 ($) | Strike ($) |
| --- | --- | --- |
| Lower-Strike put | 3.80 | 822.50 |
| Middle-strike put | 4.70 | 825.00 |
| Higher-strike put | 7.80 | 827.50 |
| Underlying Equity at expiration | 843.25 | - |

Therefore, the payoff is

$$ \begin{array}{rcll} P^{low}_T & = & (K^{low} - S_T)^{+}\\ & = & (822.50-843.25)^{+}\\ & = & 0\\ P^{mid}_T & = & (K^{mid} - S_T)^{+}\\ & = & (825.00-843.25)^{+}\\ & = & 0\\ P^{high}_T & = & (K^{high} - S_T)^{+}\\ & = & (827.50-843.25)^{+}\\ & = & 0\\ Payoff_T & = & (P^{low}_0 - P^{low}_T + P^{mid}_0 - P^{mid}_T + P^{high}_T - P^{high}_0)\times m - fee\\ & = & (3.80 - 0 + 4.70 - 0 + 0 - 7.80)\times100-1.00\times3\\ & = & 67\\ \end{array} $$

So, the strategy gains $67.

The following algorithm implements a bear put ladder Option strategy:

```python
class BearPutLadderOptionStrategy(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", Resolution.MINUTE)
        self._symbol = option.symbol

        # set our strike/expiry filter for this option chain
        option.set_filter(lambda x: x.put_ladder(30, 5, 0, -5))

    def on_data(self, 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 and i.right == OptionRight.PUT]
        if not puts:
            return

        # Select the strike prices from the remaining contracts
        strikes = sorted(set(x.strike for x in puts))
        if len(strikes) < 3:
            return

        low_strike = strikes[0]
        middle_strike = strikes[1]
        high_strike = strikes[2]

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