---
title: "Covered Put"
description: "A Covered Call consists of buying a stock and selling call Options for the same amount of stock."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/covered-put
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/covered-put.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/22%20Trading%20and%20Orders/08%20Option%20Strategies/14%20Covered%20Put
modified: 2026-10-30
index: https://www.quantconnect.com/llms.txt
---

# Covered 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) > Covered Put

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

## Introduction

A **Covered Put** consists of a short position in a stock and a short position in put Options for the same amount of stock. Covered puts aim to profit from the Option premium by selling puts written on the stock you already shorted. 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 mean you buy the stock at the strike price but you will still keep the premium. Another risk of a covered put comes from the short stock position, which can drop in value.

## Implementation

Follow these steps to implement the covered 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 covered 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.covered_put` method with the details of each leg and then pass the result to the `buy` method.
   ```python
   covered_put = OptionStrategies.covered_put(self._symbol, atm_put.strike, expiry)
   self.buy(covered_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

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

In this example, the underlying stock traded at $186.94 at expiration. You held the underlying and sold a put at a strike of $185.00 for $1.37.

The following chart shows the payoff at expiration:

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

The maximum profit is $S_T - K + P^{K}_0$. It occurs when the underlying price is at or below the strike price of the put at expiration.

If the underlying price increase, the maximum loss is unlimited.

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 ($) | Strike ($) |
| --- | --- | --- |
| Put | 1.37 | 185.00 |
| Underlying Equity at start of the trade | 186.94 | - |
| Underlying Equity at expiration | 190.01 | - |

Therefore, the payoff is

$$ \begin{array}{rcll} P^{K}_T & = & (K - S_T)^{+}\\ & = & (185 - 190.01)^{+}\\ & = & 0\\ P_T & = & (S_0 - S_T + P^{K}_0 - P^{K}_T)\times m - fee\\ & = & (186.94 - 190.01 + 1.37 - 0)\times m - fee\\ & = & -1.70 \times 100 - 2\\ & = & -172 \end{array} $$

So, the strategy loses $172.

The following algorithm implements a covered put strategy:

```python
class CoveredputAlgorithm(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]

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

        self.put = atm_put.symbol
```