--- title: "Bear Call Spread" description: "Bear call spread, also known as short call spread, consists of selling an ITM call and buying an OTM call." canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/bear-call-spread markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/bear-call-spread.md language: python source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/22%20Trading%20and%20Orders/08%20Option%20Strategies/01%20Bear%20Call%20Spread modified: 2026-11-01 index: https://www.quantconnect.com/llms.txt --- # Bear Call 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) > Bear Call Spread > Code examples are shown in Python. Append `?lang=csharp` to this URL for the C# version of this page. ## Introduction **Bear call spread**, also known as **short call spread**, consists of selling an ITM call and buying an OTM call. Both calls have the same underlying Equity and the same expiration date. The ITM call serves as a hedge for the OTM call. The bear call spread profits from a drop in underlying asset price. ## Implementation Follow these steps to implement the bear call 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.call_spread(30, 5)) ``` The `call_spread` filter narrows the universe down to just the two contracts you need to form a bear call spread. 2. In the `on_data` method, select the expiration and strikes 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 # Select the call Option contracts with the furthest expiry expiry = max([x.expiry for x in chain]) calls = [i for i in chain if i.expiry == expiry and i.right == OptionRight.CALL] if not calls == 0: return # Select the ITM and OTM contract strike prices from the remaining contracts strikes = [x.strike for x in calls] otm_strike = max(strikes) itm_strike = min(strikes) ``` 3. In the `on_data` method, select the contracts and place the orders. **Approach A:** Call the `OptionStrategies.bear_call_spread` method with the details of each leg and then pass the result to the `buy` method. ```python option_strategy = OptionStrategies.bear_call_spread(self._symbol, itm_strike, otm_strike, 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 itm_call = [x for x in calls if x.strike == itm_strike][0] otm_call = [x for x in calls if x.strike == otm_strike][0] legs = [ Leg.create(itm_call.symbol, -1), Leg.create(otm_call.symbol, 1) ] self.combo_market_order(legs, 1) ``` ## Strategy Payoff The bear call spread is a limited-reward-limited-risk strategy. The payoff is $$ \begin{array}{rcll} C^{OTM}_T & = & (S_T - K^{OTM})^{+}\\ C^{ITM}_T & = & (S_T - K^{ITM})^{+}\\ P_T & = & (C^{OTM}_T - C^{ITM}_T + C^{ITM}_0 - C^{OTM}_0)\times m - fee\\ \end{array} $$ $$ \begin{array}{rcll} \textrm{where} & C^{OTM}_T & = & \textrm{OTM call value at time T}\\ & C^{ITM}_T & = & \textrm{ITM call value at time T}\\ & S_T & = & \textrm{Underlying asset price at time T}\\ & K^{OTM} & = & \textrm{OTM call strike price}\\ & K^{ITM} & = & \textrm{ITM call strike price}\\ & P_T & = & \textrm{Payout total at time T}\\ & C^{ITM}_0 & = & \textrm{ITM call value at position opening (debit paid)}\\ & C^{OTM}_0 & = & \textrm{OTM call value at position opening (credit received)}\\ & m & = & \textrm{Contract multiplier}\\ & T & = & \textrm{Time of expiration} \end{array} $$ In this example, the underlying stock price is $1,078.92 at expiration. You sold an ITM call at a strike of $1,125.00 for $57.80 and bought an OTM call at a strike of $1,197.50 for $26.90. The following chart shows the payoff at expiration: ![Bear Call Spread strategy payoff at expiration](https://cdn.quantconnect.com/docs/i/writing-algorithms/trading-and-orders/option-strategies/bear-call-spread.png) The maximum profit is the net credit you receive from opening the trade, $C^{ITM}_0 - C^{OTM}_0$. If the price declines, both calls expire worthless. The maximum loss is $K^{OTM} - K^{ITM} + C^{ITM}_0 - C^{OTM}_0$, which occurs when the underlying price is above the strike prices of both call Option contracts. 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 ($) | Strike ($) | | --- | --- | --- | | OTM call | 26.90 | 1197.50 | | ITM call | 57.80 | 1125.00 | | Underlying Equity at expiration | 1078.92 | - | Therefore, the payoff is $$ \begin{array}{rcll} C^{OTM}_T & = & (S_T - K^{OTM})^{+}\\ & = & (1078.92-1197.50)^{+}\\ & = & 0\\ C^{ITM}_T & = & (S_T - K^{ITM})^{+}\\ & = & (1078.92-1125.00)^{+}\\ & = & 0\\ P_T & = & (C^{OTM}_T - C^{ITM}_T + C^{ITM}_0 - C^{OTM}_0)\times m - fee\\ & = & (0-0+57.80-26.90)\times100-1.00\times2\\ & = & 3088\\ \end{array} $$ So, the strategy profits $3,088. The following algorithm implements a bear call spread Option strategy: ```python class BearCallSpreadStrategy(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( lambda universe: universe.call_spread(30, 5) ) 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 furthest expiry date of the contracts expiry = sorted(chain, key=lambda x: x.expiry, reverse=True)[0].expiry # Select the call Option contracts with the furthest expiry calls = [i for i in chain if i.expiry == expiry and i.right == OptionRight.CALL] if len(calls) == 0: return # Select the ITM and OTM contracts ordered_calls = sorted(calls, key=lambda x: x.strike) itm_call = ordered_calls[0] otm_call = ordered_calls[-1] # Place the order option_strategy = OptionStrategies.bear_call_spread( self._symbol, itm_call.strike, otm_call.strike, expiry ) self.buy(option_strategy, 1) ``` ### Other Examples For more examples, see the following algorithms: **Demonstration Algorithm** - [IndexOptionBearCallSpreadAlgorithm.py](https://github.com/QuantConnect/Lean/blob/master/Algorithm.Python/IndexOptionBearCallSpreadAlgorithm.py) (Python)