---
title: "Long Put Calendar Spread"
description: "Long Put calendar spread is a combination of a longer-term (far-leg/front-month) put and a shorter-term (near-leg/back-month) put."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/long-put-calendar-spread
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/long-put-calendar-spread.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/22%20Trading%20and%20Orders/08%20Option%20Strategies/11%20Long%20Put%20Calendar%20Spread
modified: 2026-10-31
index: https://www.quantconnect.com/llms.txt
---

# Long Put Calendar Spread

[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) > Long Put Calendar Spread

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

## Introduction

**Put calendar spread**, also known as **put horizontal spread**, is a combination of a longer-term (far-leg/front-month) put and a shorter-term (near-leg/back-month) put, where both puts have the same underlying stock and the same strike price. The long put calendar spread consists of buying a longer-term put and selling a shorter-term put. This strategy profits from a decrease in price movement. The strategy also profits from the time decay value because the theta $\theta$ (the Option price decay by 1 day closer to maturity) of the shorter-term put is larger than the longer-term put.

## Implementation

Follow these steps to implement the long put calendar spread strategy:

1. In the `initialize` method, set the start date, end date, cash, and [Option 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(500000)

       self.universe_settings.asynchronous = True
       option = self.add_option("GOOG", Resolution.MINUTE)
       self._symbol = option.symbol
       option.set_filter(lambda universe: universe.put_calendar_spread(0, 30, 60))
   ```
   The `put_calendar_spread` filter narrows the universe down to just the two contracts you need to form a long put calendar spread.
2. In the `on_data` method, select the strike price and expiration dates of the contracts in the strategy legs.
   ```python
   def on_data(self, slice: Slice) -> None:
       if self.portfolio.invested:
           return

       # Get the OptionChain
       chain = slice.option_chains.get(self._symbol, None)
       if not chain:
           return

       # Get the ATM strike
       atm_strike = sorted(chain, key=lambda x: abs(x.strike - chain.underlying.price))[0].strike

       # Select the ATM put Option contracts
       puts = [i for i in chain if i.strike == atm_strike and i.right == OptionRight.PUT]
       if len(puts) == 0:
           return

       # Select the near and far expiry dates
       expiries = sorted([x.expiry for x in puts])
       near_expiry = expiries[0]
       far_expiry = expiries[-1]
   ```
3. In the `on_data` method, select the contracts and place the orders.
   **Approach A:** Call the `OptionStrategies.put_calendar_spread` method with the details of each leg and then pass the result to the `buy` method.
   ```python
   option_strategy = OptionStrategies.put_calendar_spread(self._symbol, atm_strike, near_expiry, far_expiry)
   self.buy(option_strategy, 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
   near_expiry_put = [x for x in puts if x.expiry == near_expiry][0]
   far_expiry_put = [x for x in puts if x.expiry == far_expiry][0]

   legs = [
       Leg.create(near_expiry_put.symbol, -1),
       Leg.create(far_expiry_put.symbol, 1)
   ]
   self.combo_market_order(legs, 1)
   ```

## Strategy Payoff

The long put calendar spread is a limited-reward-limited-risk strategy. The payoff is taken at the shorter-term expiration. The payoff is

$$ \begin{array}{rcll} P^{\textrm{short-term}}_T & = & (K - S_T)^{+}\\ P_T & = & (P^{\textrm{long-term}}_T - P^{\textrm{short-term}}_T + P^{\textrm{short-term}}_0 - P^{\textrm{long-term}}_0)\times m - fee \end{array} $$ $$ \begin{array}{rcll} \textrm{where} & P^{\textrm{short-term}}_T & = & \textrm{Shorter term put value at time T}\\ & P^{\textrm{long-term}}_T & = & \textrm{Longer term put value at time T}\\ & S_T & = & \textrm{Underlying asset price at time T}\\ & K & = & \textrm{Strike price}\\ & P_T & = & \textrm{Payout total at time T}\\ & P^{\textrm{short-term}}_0 & = & \textrm{Shorter term put value at position opening (credit received)}\\ & P^{\textrm{long-term}}_0 & = & \textrm{Longer term put value at position opening (debit paid)}\\ & m & = & \textrm{Contract multiplier}\\ & T & = & \textrm{Time of shorter term put expiration} \end{array} $$

In this example, you bought a longer-term put at a strike of $800.00 for $19.30 and sold a shorter-term put at the same strike for $11.30.

The following chart shows the payoff at expiration:

![Long Put Calendar Spread strategy payoff at expiration](https://cdn.quantconnect.com/docs/i/writing-algorithms/trading-and-orders/option-strategies/long-put-calendar-spread.png)

The maximum profit is undetermined because it depends on the underlying volatility.  It occurs when $S_T = S_0$ and the spread of the puts are at their maximum.

The maximum loss is the net debit paid, $P^{\textrm{short-term}}_0 - P^{\textrm{long-term}}_0$. It occurs when the underlying price moves very deep ITM or OTM so the values of both puts are close 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. Naked long puts pose risk of losing all the debit paid if you don't close the position with short put together and the price drops below its strike.

## Example

The following table shows the price details of the assets in the long put calendar spread algorithm:

| Asset | Price ($) | Strike ($) |
| --- | --- | --- |
| Shorter-term put at position opening | 11.30 | 800.00 |
| Longer-term put at position opening | 19.30 | 800.00 |
| Longer-term put at shorter-term expiration | 3.50 | 800.00 |
| Underlying Equity at shorter-term expiration | 828.07 | - |

Therefore, the payoff is

$$ \begin{array}{rcll} P^{\textrm{short-term}}_T & = & (K - S_T)^{+}\\ & = & (800.00-828.07)^{+}\\ & = & 0\\ P_T & = & (P^{\textrm{long-term}}_T - P^{\textrm{short-term}}_T + P^{\textrm{short-term}}_0 - P^{\textrm{long-term}}_0)\times m - fee\\ & = & (3.50-0+11.30-19.30)\times100-1.00\times2\\ & = & -452\\ \end{array} $$

So, the strategy loses $452.

The following algorithm implements a long put calendar spread Option strategy:

```python
class PutCalendarSpreadStrategy(QCAlgorithm):
    def initialize(self) -> None:
        self.set_start_date(2024, 9, 1)
        self.set_end_date(2024, 12, 31)
        self.set_cash(500000)

        option = self.add_option("GOOG", Resolution.MINUTE)
        self.symbol = option.symbol
        option.set_filter(self.universe_func)

    def universe_func(self, universe: OptionFilterUniverse) -> OptionFilterUniverse:
        return universe.put_calendar_spread(0, 30, 60)

    def on_data(self, data: Slice) -> None:
        # avoid extra orders
        if self.portfolio.invested: return

        # Get the OptionChain of the self.symbol
        chain = data.option_chains.get(self.symbol, None)
        if not chain: return

        # get at-the-money strike
        atm_strike = sorted(chain, key=lambda x: abs(x.strike - chain.underlying.price))[0].strike

        # filter the put options from the contracts which is ATM in the option chain.
        puts = [i for i in chain if i.strike == atm_strike and i.right == OptionRight.PUT]
        if len(puts) == 0: return

        # sorted the optionchain by expiration date
        expiries = sorted([x.expiry for x in puts], key = lambda x: x)

        # select the farest expiry as far-leg expiry, and the nearest expiry as near-leg expiry
        near_expiry = expiries[0]
        far_expiry = expiries[-1]

        option_strategy = OptionStrategies.put_calendar_spread(self.symbol, atm_strike, near_expiry, far_expiry)
        # We open a position with 1 unit of the option strategy
        self.buy(option_strategy, 1)
        # self.sell(option_strategy, 1) if short put calendar spread

    def on_end_of_algorithm(self):
        for symbol, sec in self.securities.items():
            self.log(f"{symbol} :: {sec.price}")
```

### Other Examples

For more examples, see the following algorithms:

**Demonstration Algorithm**

- [IndexOptionPutCalendarSpreadAlgorithm.py](https://github.com/QuantConnect/Lean/blob/master/Algorithm.Python/IndexOptionPutCalendarSpreadAlgorithm.py) (Python)
- [LongAndShortPutCalendarSpreadStrategiesAlgorithm.py](https://github.com/QuantConnect/Lean/blob/master/Algorithm.Python/LongAndShortPutCalendarSpreadStrategiesAlgorithm.py) (Python)