YadavKapil
4249165f99
* Fix option assignment trade statistics Resolve the security from each order event when updating TradeBuilder so physically settled underlying fills use the underlying multiplier and conversion rate. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> Copilot-Session: 716a0df4-0117-458b-b4ac-7d8aeeb9bf48 * Resolve order event security from the event symbol Option exercises emit the underlying fill under the option's order id, so resolving the security from the order symbol handed the option's contract multiplier and quote currency conversion rate to the underlying fill, inflating closed trade statistics. Extend the option assignment regression algorithm, in both C# and Python, to assert every closed trade's profit and loss against its own security's contract multiplier. --------- Co-authored-by: Kapil Yadav <kapyadav@microsoft.com> Co-authored-by: Jhonathan Abreu <jdabreu25@gmail.com>
69 lines
3.1 KiB
Python
69 lines
3.1 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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### <summary>
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### This regression algorithm verifies automatic option contract assignment behavior.
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### </summary>
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class OptionAssignmentRegressionAlgorithm(QCAlgorithm):
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def initialize(self):
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self.set_start_date(2015, 12, 23)
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self.set_end_date(2015, 12, 28)
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self.set_cash(100000)
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self.stock = self.add_equity("GOOG", Resolution.MINUTE)
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contracts = list(self.option_chain(self.stock.symbol))
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self.put_option_symbol = sorted(
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[c for c in contracts if c.id.option_right == OptionRight.PUT and c.id.strike_price == 800],
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key=lambda c: c.id.date
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)[0]
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self.call_option_symbol = sorted(
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[c for c in contracts if c.id.option_right == OptionRight.CALL and c.id.strike_price == 600],
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key=lambda c: c.id.date
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)[0]
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self.put_option = self.add_option_contract(self.put_option_symbol)
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self.call_option = self.add_option_contract(self.call_option_symbol)
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def on_data(self, data):
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if not self.portfolio.invested and self.stock.price != 0 and self.put_option.price != 0 and self.call_option.price != 0:
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#this gets executed on start and after each auto-assignment, finally ending with expiration assignment
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if self.time < self.put_option_symbol.id.date:
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self.market_order(self.put_option_symbol, -1)
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if self.time < self.call_option_symbol.id.date:
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self.market_order(self.call_option_symbol, -1)
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def get_security(self, symbol):
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if symbol == self.stock.symbol:
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return self.stock
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if symbol == self.call_option_symbol:
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return self.call_option
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if symbol == self.put_option_symbol:
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return self.put_option
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raise RegressionTestException(f"Unexpected symbol: {symbol}")
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def on_end_of_algorithm(self):
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for trade in self.trade_builder.closed_trades:
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symbol, = trade.symbols
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direction = 1 if trade.direction == TradeDirection.LONG else -1
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multiplier = self.get_security(symbol).symbol_properties.contract_multiplier
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expected_profit_loss = round((trade.exit_price - trade.entry_price) * trade.quantity * direction * multiplier, 2)
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if trade.profit_loss != expected_profit_loss:
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raise RegressionTestException(f"Expected underlying trade profit/loss to be {expected_profit_loss}. Actual: {trade.profit_loss}")
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