---
title: "Protective Call"
description: "A Protective Call consists of selling a stock and buying call Options for the same amount of stock."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/protective-call
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/protective-call.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/22%20Trading%20and%20Orders/08%20Option%20Strategies/21%20Protective%20Call
modified: 2026-10-31
index: https://www.quantconnect.com/llms.txt
---

# Protective Call

[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 Call

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

## Introduction

A **Protective Call** consists of a short position in a stock and a long position in a call Option for the same amount of stock. Protective calls aim to hedge the short position of a stock with a long ATM or slightly OTM call Option. At any time for American Options or at expiration for European Options, if the stock moves below the strike price, the Option contract becomes worthless but the short position acquires an unrealized gain. If the underlying price moves above 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) and receive the underlying Equity, which closes your short position.

## Implementation

Follow these steps to implement the protective call strategy:

1. In the `initialize` method, set the start date, end date, 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_call(30, 0))
   ```
   The `naked_call` filter narrows the universe down to just the one contract you need to form a protective call.
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 call with the farthest expiry
       expiry = max([x.expiry for x in chain])
       call_contracts = sorted([x for x in chain
           if x.right == OptionRight.CALL and x.expiry == expiry],
           key=lambda x: abs(chain.underlying.price - x.strike))

       if not call_contracts:
           return

       atm_call = call_contracts[0]
   ```
3. In the `on_data` method, place the orders.
   **Approach A:** Call the `OptionStrategies.protective_call` method with the details of each leg and then pass the result to the `buy` method.
   ```python
   protective_call = OptionStrategies.protective_call(self._symbol, atm_call.strike, expiry)
   self.buy(protective_call, 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_call.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

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

In this example, the underlying stock traded at $186.94 at expiration. You held the underlying position and bought a call at a strike of $185.00 for $3.50.

The following chart shows the payoff at expiration:

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

The maximum profit is $S_0 - C^{K}_0$, which occurs when the underlying price is $0$.

The maximum loss is $S_0 - K - C^{K}_0$, which occurs when the underlying price is above the strike price.

## Example

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

| Asset | Price ($) | Strike ($) |
| --- | --- | --- |
| Call | 3.50 | 185.00 |
| Underlying Equity at start of the trade | 186.94 | - |
| Underlying Equity at expiration | 190.01 | - |

Therefore, the payoff is

$$ \begin{array}{rcll} C^{K}_T & = & (S_T - K)^{+}\\ & = & (190.01 - 185)^{+}\\ & = & 5.01\\ P_T & = & (S_0 - S_T + C^{K}_T - C^{K}_0)\times m - fee\\ & = & (186.94 - 190.01 + 5.01 - 3.50)\times m - fee\\ & = & -1.56 \times 100 - 2\\ & = & -158 \end{array} $$

So, the strategy loses $158.

The following algorithm implements a protective call Option strategy:

```python
class ProtectiveCallAlgorithm(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_call(30, 0))

        self.call = None

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

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

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

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

        if not call_contracts:
            return

        atm_call = call_contracts[0]

        protective_call = OptionStrategies.protective_call(self.symbol, atm_call.strike, expiry)
        self.buy(protective_call, 1)

        self.call = atm_call.symbol
```