wehub-resource-sync
82 lines
4.4 KiB
Python
82 lines
4.4 KiB
Python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from AlgorithmImports import *
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import itertools
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from OptionStrategyFactoryMethodsBaseAlgorithm import *
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### <summary>
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### This algorithm demonstrate how to use OptionStrategies helper class to batch send orders for common strategies.
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### In this case, the algorithm tests the Butterfly Call and Short Butterfly Call strategies.
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### </summary>
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class LongAndShortButterflyCallStrategiesAlgorithm(OptionStrategyFactoryMethodsBaseAlgorithm):
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def expected_orders_count(self) -> int:
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return 6
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def trade_strategy(self, chain: OptionChain, option_symbol: Symbol):
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call_contracts = (contract for contract in chain if contract.right == OptionRight.CALL)
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for expiry, group in itertools.groupby(call_contracts, lambda x: x.expiry):
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contracts = list(group)
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if len(contracts) < 3:
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continue
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strikes = sorted([contract.strike for contract in contracts])
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atm_strike = min(strikes, key=lambda strike: abs(strike - chain.underlying.price))
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spread = min(atm_strike - strikes[0], strikes[-1] - atm_strike)
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itm_strike = atm_strike - spread
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otm_strike = atm_strike + spread
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if otm_strike in strikes and itm_strike in strikes:
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# Ready to trade
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self._butterfly_call = OptionStrategies.butterfly_call(option_symbol, otm_strike, atm_strike, itm_strike, expiry)
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self._short_butterfly_call = OptionStrategies.short_butterfly_call(option_symbol, otm_strike, atm_strike, itm_strike, expiry)
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self.buy(self._butterfly_call, 2)
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return
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def assert_strategy_position_group(self, position_group: IPositionGroup, option_symbol: Symbol):
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positions = list(position_group.positions)
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if len(positions) != 3:
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raise AssertionError(f"Expected position group to have 3 positions. Actual: {len(positions)}")
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higher_strike = max(leg.strike for leg in self._butterfly_call.option_legs)
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higher_strike_position = next((position for position in positions
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if position.symbol.id.option_right == OptionRight.CALL and position.symbol.id.strike_price == higher_strike),
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None)
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if higher_strike_position and higher_strike_position.quantity != 2:
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raise AssertionError(f"Expected higher strike position quantity to be 2. Actual: {higher_strike_position.quantity}")
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lower_strike = min(leg.strike for leg in self._butterfly_call.option_legs)
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lower_strike_position = next((position for position in positions
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if position.symbol.id.option_right == OptionRight.CALL and position.symbol.id.strike_price == lower_strike),
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None)
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if lower_strike_position and lower_strike_position.quantity != 2:
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raise AssertionError(f"Expected lower strike position quantity to be 2. Actual: {lower_strike_position.quantity}")
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middle_strike = [leg.strike for leg in self._butterfly_call.option_legs if leg.strike < higher_strike and leg.strike > lower_strike][0]
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middle_strike_position = next((position for position in positions
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if position.symbol.id.option_right == OptionRight.CALL and position.symbol.id.strike_price == middle_strike),
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None)
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if middle_strike_position and middle_strike_position.quantity != -4:
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raise AssertionError(f"Expected middle strike position quantity to be -4. Actual: {middle_strike_position.quantity}")
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def liquidate_strategy(self):
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# We should be able to close the position using the inverse strategy (a short butterfly call)
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self.buy(self._short_butterfly_call, 2)
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