---
title: "Naked Put"
description: "A Naked Put, also known as an uncovered put, involves the sale of put Options without having a corresponding short position in the underlying security."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/naked-put
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/naked-put.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/22%20Trading%20and%20Orders/08%20Option%20Strategies/20%20Naked%20Put
modified: 2026-10-30
index: https://www.quantconnect.com/llms.txt
---

# Naked 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) > Naked Put

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

## Introduction

A **Naked Put**, also known as an **uncovered put**, consists of selling put Options without having a corresponding short position in the underlying security. It's called "naked" because you don't have any cover or protection in the form of a short position in the underlying asset, which exposes you to potentially unlimited risk. Naked puts aim to profit from the Option premium by selling puts. At any time for American Options or at expiration for European Options, if the stock moves above the strike price, you keep the premium. If the underlying price moves below 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 need sell the underlying at market price to fulfill your obligation to buy it at the strike price and keep the premium.

## Implementation

Follow these steps to implement the naked 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 naked 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.naked_put` method with the details of each leg and then pass the result to the `buy` method.
   ```python
   naked_put = OptionStrategies.naked_put(self._symbol, atm_put.strike, expiry)
   self.buy(naked_put, 1)
   ```
   **Approach B:** Call the [market_order](https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/order-types/market-orders.md) or [limit_order](https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/order-types/limit-orders.md) method.
   ```python
   self.market_order(atm_put.symbol, -1)
   ```

## Strategy Payoff

The payoff of the strategy is

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

In this example, you sold a put at a strike of $185.00 for $1.37.

The following chart shows the payoff at expiration:

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

The maximum profit is $P^{K}_0$, which occurs when the underlying price is at or above the strike price of the put at expiration.

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

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 expiration | 190.01 | - |

Therefore, the payoff is

$$ \begin{array}{rcll} P^{K}_T & = & (K - S_T)^{+}\\ & = & (185 - 190.01)^{+}\\ & = & 0.0\\ P_T & = & (P^{K}_0 - P^{K}_T)\times m - fee\\ & = & (1.37 - 0.0)\times m - fee\\ & = & 1.37 \times 100 - 1\\ & = & 136 \end{array} $$

So, the strategy gains $136.

The following algorithm implements a naked put Option strategy:

```python
class NakedPutAlgorithm(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]

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

        self.put = atm_put.symbol
```