chore: import upstream snapshot with attribution
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# 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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class BasicTemplateIndexDailyAlgorithm(QCAlgorithm):
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def initialize(self) -> None:
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self.set_start_date(2021, 1, 1)
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self.set_end_date(2021, 1, 18)
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self.set_cash(1000000)
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# Use indicator for signal; but it cannot be traded
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self.spx = self.add_index("SPX", Resolution.DAILY).symbol
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# Trade on SPX ITM calls
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self.spx_option = Symbol.create_option(
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self.spx,
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Market.USA,
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OptionStyle.EUROPEAN,
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OptionRight.CALL,
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3200,
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datetime(2021, 1, 15)
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)
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self.add_index_option_contract(self.spx_option, Resolution.DAILY)
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self.ema_slow = self.ema(self.spx, 80)
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self.ema_fast = self.ema(self.spx, 200)
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self.ExpectedBarCount = 10
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self.BarCounter = 0
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self.settings.daily_precise_end_time = True
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def on_data(self, data: Slice):
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if not self.portfolio.invested:
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# SPX Index is not tradable, but we can trade an option
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self.market_order(self.spx_option, 1)
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else:
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self.liquidate()
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# Count how many slices we receive with SPX data in it to assert later
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if data.contains_key(self.spx):
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self.BarCounter = self.BarCounter + 1
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def OnEndOfAlgorithm(self):
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if self.BarCounter != self.ExpectedBarCount:
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raise ValueError(f"Bar Count {self.BarCounter} is not expected count of {self.ExpectedBarCount}")
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for symbol in [ self.spx_option, self.spx ]:
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history = self.history(symbol, 10)
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if len(history) != 10:
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raise ValueError(f"Unexpected history count: {len(history)}")
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if any(x for x in history.index.get_level_values('time') if x.time() != time(15, 15, 0)):
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raise ValueError(f"Unexpected history data time")
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