---
title: "Protective Put"
description: "A Protective Put consists of buying a stock and buying put Options for the same amount of stock."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/protective-put
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/protective-put.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/22%20Trading%20and%20Orders/08%20Option%20Strategies/22%20Protective%20Put
modified: 2026-10-31
index: https://www.quantconnect.com/llms.txt
---

# Protective Put

[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) > Protective Put

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

## Introduction

A **Protective Put** consists of buying a long position in a stock and a long position in put Options for the same amount of stock. Protective puts aim to hedge the long position of a stock with a long ATM or slightly OTM put Option. At any time for American Options or at expiration for European Options, if the stock moves above the strike price, the Option contract becomes worthless but the long stock position acquires an unrealized gain. If the underlying price moves below the strike, you can [exercise the Options contract](https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/order-types/option-exercise-orders.md), which sells your underlying position at the put Option strike price.

## Implementation

Follow these steps to implement the protective put strategy:

1. In the `initialize` method, set the start date, end date, starting cash, and [Options 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("IBM")
       self._symbol = option.symbol
       option.set_filter(lambda universe: universe.naked_put(30, 0))
   ```
   The `naked_put` filter narrows the universe down to just the one contract you need to form a protective put.
2. In the `on_data` method, select the Option contract.
   ```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 ATM put with the farthest expiry
       expiry = max([x.expiry for x in chain])
       put_contracts = sorted([x for x in chain
           if x.right == OptionRight.PUT and x.expiry == expiry],
           key=lambda x: abs(chain.underlying.price - x.strike))

       if not put_contracts:
           return

       atm_put = put_contracts[0]
   ```
3. In the `on_data` method, place the orders.
   **Approach A:** Call the `OptionStrategies.protective_put` method with the details of each leg and then pass the result to the `buy` method.
   ```python
   protective_put = OptionStrategies.protective_put(self._symbol, atm_put.strike, expiry)
   self.buy(protective_put, 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(atm_put.symbol, 1),
       Leg.create(chain.underlying.symbol, chain.underlying.symbol_properties.contract_multiplier)
   ]
   self.combo_market_order(legs, 1)
   ```

## Strategy Payoff

The payoff of the strategy is

$$ \begin{array}{rcll} P^{K}_T & = & (K - S_T)^{+}\\ P_T & = & (S_T - S_0 + P^{K}_T - P^{K}_0)\times m - fee \end{array} $$ $$ \begin{array}{rcll} \textrm{where} & P^{K}_T & = & \textrm{Put value at time T}\\ & S_T & = & \textrm{Underlying asset price at time T}\\ & K & = & \textrm{Put strike price}\\ & P_T & = & \textrm{Payout total at time T}\\ & S_0 & = & \textrm{Underlying asset price when the trade opened}\\ & P^{K}_0 & = & \textrm{Put price when the trade opened (credit received)}\\ & m & = & \textrm{Contract multiplier}\\ & T & = & \textrm{Time of expiration} \end{array} $$

In this example, the underlying stock traded at $187.07 at expiration. You held the underlying position and bought a put at a strike of $185.00 for $1.53.

The following chart shows the payoff at expiration:

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

The maximum profit is $S_T - S_0 - P^{K}_0$, which occurs when the underlying price is above the $S_0 + P^{K}_0$.

The maximum loss is $P^{K}_0$, which occurs when the underlying price drops.

## Example

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

| Asset | Price ($) | Strike ($) |
| --- | --- | --- |
| Put | 1.53 | 185.00 |
| Underlying Equity at start of the trade | 187.07 | - |
| Underlying Equity at expiration | 190.01 | - |

Therefore, the payoff is

$$ \begin{array}{rcll} P^{K}_T & = & (K - S_T)^{+}\\ & = & (185 - 190.1)^{+}\\ & = & 0\\ P_T & = & (S_T - S_0 + P^{K}_T - P^{K}_0)\times m - fee\\ & = & (190.01 - 187.07 + 0 - 1.53)\times m - fee\\ & = & 1.41 \times 100 - 2\\ & = & 139 \end{array} $$

So, the strategy gains $139.

The following algorithm implements a protective put Option strategy:

```python
class ProtectivePutAlgorithm(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("IBM")
        self.symbol = option.symbol
        option.set_filter(lambda universe: universe.naked_put(30, 0))

        self.put = None

        # use the underlying equity as the benchmark
        self.set_benchmark(self.symbol.underlying)

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

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

        # Find ATM put with the farthest expiry
        expiry = max([x.expiry for x in chain])
        put_contracts = sorted([x for x in chain
            if x.right == OptionRight.PUT and x.expiry == expiry],
            key=lambda x: abs(chain.underlying.price - x.strike))

        if not put_contracts:
            return

        atm_put = put_contracts[0]

        protective_put = OptionStrategies.protective_put(self.symbol, atm_put.strike, expiry)
        self.buy(protective_put, 1)

        self.put = atm_put.symbol
```