---
title: "Covered Call"
description: "A Covered Put consists of selling a stock and selling put Options for the same amount of stock."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/covered-call
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/covered-call.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/22%20Trading%20and%20Orders/08%20Option%20Strategies/13%20Covered%20Call
modified: 2026-10-30
index: https://www.quantconnect.com/llms.txt
---

# Covered 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) > Covered Call

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

## Introduction

A **Covered Call** consists of a long position in a stock and a short position in call Options for the same amount of stock. Covered calls aim to profit from the Option premium by selling calls written on the stock you already own. At any time for American Options or at expiration for European Options, if the stock moves below the strike price, you keep the premium and still maintain the underlying Equity position. If the underlying price moves above the strike, the Option buyer can [exercise the Options contract](https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/order-types/option-exercise-orders.md), which means you sell your stock at the strike price and keep the premium. Another risk of a covered call comes from the long stock position, which can drop in value.

## Implementation

Follow these steps to implement the covered call 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_call(30, 0))
   ```
   The `naked_call` filter narrows the universe down to just the one contract you need to form a covered 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.covered_call` method with the details of each leg and then pass the result to the `buy` method.
   ```python
   covered_call = OptionStrategies.covered_call(self._symbol, atm_call.strike, expiry)
   self.buy(covered_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

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

In this example, the underlying stock traded at $187.07 at expiration. You held the underlying and sold a call at a strike of $185.00 for $3.35.

The following chart shows the payoff at expiration:

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

The maximum profit is $K - S_T + C^{K}_0$, which occurs when the underlying price is at or above the strike price of the call at expiration.

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

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 ($) | Strike ($) |
| --- | --- | --- |
| Call | 3.35 | 185.00 |
| Underlying Equity at start of the trade | 187.07 | - |
| Underlying Equity at expiration | 190.01 | - |

Therefore, the payoff is

$$ \begin{array}{rcll} C^{K}_T & = & (S_T - K)^{+}\\ & = & (190.01 - 185)^{+}\\ & = & 5.01\\ P_T & = & (S_T - S_0 + C^{K}_0 - C^{K}_T)\times m - fee\\ & = & (190.01 - 187.07 + 3.35 - 5.01)\times m - fee\\ & = & 1.28 \times 100 - 2\\ & = & 126 \end{array} $$

So, the strategy gains $126.

The following algorithm implements a covered call strategy:

```python
class CoveredCallAlgorithm(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]

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

        self.call = atm_call.symbol
```