Constructing and Closing Long and Short Call Butterfly Spreads

# LongAndShortButterflyCallStrategiesAlgorithm


# LongAndShortButterflyCallStrategiesAlgorithm









This algorithm demonstrate how to use OptionStrategies helper class to batch send orders for common strategies. In this case, the algorithm tests the Butterfly Call and Short Butterfly Call strategies.

## Source (Apache-2.0)

```python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from AlgorithmImports import *

import itertools

from OptionStrategyFactoryMethodsBaseAlgorithm import *

### <summary>
### This algorithm demonstrate how to use OptionStrategies helper class to batch send orders for common strategies.
### In this case, the algorithm tests the Butterfly Call and Short Butterfly Call strategies.
### </summary>
class LongAndShortButterflyCallStrategiesAlgorithm(OptionStrategyFactoryMethodsBaseAlgorithm):

    def expected_orders_count(self) -> int:
        return 6

    def trade_strategy(self, chain: OptionChain, option_symbol: Symbol):
        call_contracts = (contract for contract in chain if contract.right == OptionRight.CALL)

        for expiry, group in itertools.groupby(call_contracts, lambda x: x.expiry):
            contracts = list(group)
            if len(contracts) < 3:
                continue

            strikes = sorted([contract.strike for contract in contracts])
            atm_strike = min(strikes, key=lambda strike: abs(strike - chain.underlying.price))
            spread = min(atm_strike - strikes[0], strikes[-1] - atm_strike)
            itm_strike = atm_strike - spread
            otm_strike = atm_strike + spread

            if otm_strike in strikes and itm_strike in strikes:
                # Ready to trade
                self._butterfly_call = OptionStrategies.butterfly_call(option_symbol, otm_strike, atm_strike, itm_strike, expiry)
                self._short_butterfly_call = OptionStrategies.short_butterfly_call(option_symbol, otm_strike, atm_strike, itm_strike, expiry)
                self.buy(self._butterfly_call, 2)
                return

    def assert_strategy_position_group(self, position_group: IPositionGroup, option_symbol: Symbol):
        positions = list(position_group.positions)
        if len(positions) != 3:
            raise AssertionError(f"Expected position group to have 3 positions. Actual: {len(positions)}")

        higher_strike = max(leg.strike for leg in self._butterfly_call.option_legs)
        higher_strike_position = next((position for position in positions
                                      if position.symbol.id.option_right == OptionRight.CALL and position.symbol.id.strike_price == higher_strike),
                                     None)

        if higher_strike_position and higher_strike_position.quantity != 2:
            raise AssertionError(f"Expected higher strike position quantity to be 2. Actual: {higher_strike_position.quantity}")

        lower_strike = min(leg.strike for leg in self._butterfly_call.option_legs)
        lower_strike_position = next((position for position in positions
                                    if position.symbol.id.option_right == OptionRight.CALL and position.symbol.id.strike_price == lower_strike),
                                   None)

        if lower_strike_position and lower_strike_position.quantity != 2:
            raise AssertionError(f"Expected lower strike position quantity to be 2. Actual: {lower_strike_position.quantity}")

        middle_strike = [leg.strike for leg in self._butterfly_call.option_legs if leg.strike < higher_strike and leg.strike > lower_strike][0]
        middle_strike_position = next((position for position in positions
                                     if position.symbol.id.option_right == OptionRight.CALL and position.symbol.id.strike_price == middle_strike),
                                    None)

        if middle_strike_position and middle_strike_position.quantity != -4:
            raise AssertionError(f"Expected middle strike position quantity to be -4. Actual: {middle_strike_position.quantity}")

    def liquidate_strategy(self):
        # We should be able to close the position using the inverse strategy (a short butterfly call)
        self.buy(self._short_butterfly_call, 2)

```