jonathanwu906
77caa034e3
Port the C# regression algorithm demonstrating IndicatorVolatilityModel usage, including how to reset and warm up the indicator on splits and dividends to avoid volatility jumps from price discontinuities, and enable the Python variant in the regression test suite. Closes #6375 Claude-Session: https://claude.ai/code/session_01R7LGdW3eC9za8WMrtssHGr Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
86 lines
3.5 KiB
Python
86 lines
3.5 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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### Algorithm illustrating the usage of the IndicatorVolatilityModel and
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### how to handle splits and dividends to avoid price discontinuities
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### </summary>
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class IndicatorVolatilityModelAlgorithm(QCAlgorithm):
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_indicator_periods = 7
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_data_normalization_mode = DataNormalizationMode.RAW
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def initialize(self):
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self.set_start_date(2014, 1, 1)
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self.set_end_date(2014, 12, 31)
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self.set_cash(100000)
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equity = self.add_equity("AAPL", Resolution.DAILY, data_normalization_mode=self._data_normalization_mode)
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self._aapl = equity.symbol
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std = StandardDeviation(self._indicator_periods)
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mean = SimpleMovingAverage(self._indicator_periods)
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self._indicator = IndicatorExtensions.over(std, mean)
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def update_indicator(security, data, indicator):
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if data.price > 0:
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std.update(data.time, data.price)
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mean.update(data.time, data.price)
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self._volatility_model = IndicatorVolatilityModel(self._indicator, update_indicator)
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equity.set_volatility_model(self._volatility_model)
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self._splits_and_dividends_count = 0
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self._volatility_checked = False
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def on_data(self, slice):
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if slice.splits.contains_key(self._aapl) or slice.dividends.contains_key(self._aapl):
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self._splits_and_dividends_count += 1
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# On a split or dividend event, we need to reset and warm the indicator up as Lean does to BaseVolatilityModel's
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# to avoid big jumps in volatility due to price discontinuities
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self._indicator.reset()
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equity = self.securities[self._aapl]
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VolatilityModelExtensions.warm_up(
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self._volatility_model,
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self,
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equity,
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equity.resolution,
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self._indicator_periods,
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self._data_normalization_mode
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)
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def on_end_of_day(self, symbol):
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if symbol != self._aapl or not self._indicator.is_ready:
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return
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self._volatility_checked = True
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# This is expected only in this case, 0.05 is not a magical number of any kind.
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# Just making sure we don't get big jumps on volatility
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volatility = self.securities[self._aapl].volatility_model.volatility
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if volatility <= 0 or volatility > 0.05:
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raise RegressionTestException(
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"Expected volatility to stay less than 0.05 (not big jumps due to price discontinuities on splits and dividends), "
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f"but got {volatility}")
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def on_end_of_algorithm(self):
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if self._splits_and_dividends_count == 0:
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raise RegressionTestException("Expected to get at least one split or dividend event")
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if not self._volatility_checked:
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raise RegressionTestException("Expected to check volatility at least once")
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