Louis Szeto
020cf013df
* CustomDataRegressionAlgorithm * DescendingCustomDataObjectStoreRegressionAlgorithm * CustomDataPropertiesRegressionAlgorithm * DateTime -> should be datetime * KerasNeuralNetworkAlgorithm * OptionIndicatorsMirrorContractsRegressionAlgorithm * BybitCustomDataCryptoRegressionAlgorithm * DropboxBaseDataUniverseSelectionAlgorithm * UserDefinedUniverseAlgorithm * CompleteOrderTagUpdateAlgorithm * BasicTemplateOptionEquityStrategyAlgorithm hint * ETFConstituentUniverseFrameworkRegressionAlgorithm * FutureStopMarketOrderOnExtendedHoursRegressionAlgorithm * SecurityDynamicPropertyPythonClassAlgorithm * hint * hinting * CallbackCommandRegressionAlgorithm * CustomWarmUpPeriodIndicatorAlgorithm * CrunchDAOSignalExportDemonstrationAlgorithm * ExpiryHelperAlphaModelFrameworkAlgorithm * ClassicRenkoConsolidatorAlgorithm * SmaCrossUniverseSelectionAlgorithm * PEP8 Fix: Assigning to a Method * SliceGetByTypeRegressionAlgorithm * MarketOnCloseOrderBufferExtendedMarketHoursRegressionAlgorithm * MarketOnCloseOrderBufferRegressionAlgorithm * CustomIndicatorAlgorithm * ScheduledQueuingAlgorithm * ComboOrdersFillModelAlgorithm * CustomIndicatorWithExtensionAlgorithm * IndicatorWithRenkoBarsRegressionAlgorithm * CoarseFineOptionUniverseChainRegressionAlgorithm * NumeraiSignalExportDemonstrationAlgorithm * DropboxUniverseSelectionAlgorithm * WeeklyUniverseSelectionRegressionAlgorithm * AutoRegressiveIntegratedMovingAverageRegressionAlgorithm * DropboxBaseDataUniverseSelectionAlgorithm * IronCondorStrategyAlgorithm * LongAndShortButterflyPutStrategiesAlgorithm * FutureStopMarketOrderOnExtendedHoursRegressionAlgorithm * LongAndShortCallCalendarSpreadStrategiesAlgorithm * KerasNeuralNetworkAlgorithm * LongAndShortPutCalendarSpreadStrategiesAlgorithm * OptionPriceModelForOptionStylesBaseRegressionAlgorithm * TensorFlowNeuralNetworkAlgorithm * MarketOnCloseOrderBufferRegressionAlgorithm * MarketOnCloseOrderBufferExtendedMarketHoursRegressionAlgorithm * typing * ComboOrderTicketDemoAlgorithm * PytorchNeuralNetworkAlgorithm * MultipleSymbolConsolidationAlgorithm * fixes * revert getattr mypy syntax * address peer review * Addresses Peer-Review --------- Co-authored-by: Alexandre Catarino <AlexCatarino@users.noreply.github.com>
75 lines
3.1 KiB
Python
75 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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from collections import deque
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### <summary>
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### Demonstrates how to create a custom indicator and register it for automatic updated
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### </summary>
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### <meta name="tag" content="indicators" />
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### <meta name="tag" content="indicator classes" />
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### <meta name="tag" content="custom indicator" />
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class CustomIndicatorAlgorithm(QCAlgorithm):
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def initialize(self) -> None:
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self.set_start_date(2013,10,7)
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self.set_end_date(2013,10,11)
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self.add_equity("SPY", Resolution.SECOND)
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# Create a QuantConnect indicator and a python custom indicator for comparison
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self._sma = self.sma("SPY", 60, Resolution.MINUTE)
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self._custom = CustomSimpleMovingAverage('custom', 60)
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# The python custom class must inherit from PythonIndicator to enable Updated event handler
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self._custom.updated += self._custom_updated
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self._custom_window = RollingWindow[IndicatorDataPoint](5)
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self.register_indicator("SPY", self._custom, Resolution.MINUTE)
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self.plot_indicator('CSMA', self._custom)
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def custom_updated(self, sender: object, updated: IndicatorDataPoint) -> None:
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self._custom_window.add(updated)
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def on_data(self, data: Slice) -> None:
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if not self.portfolio.invested:
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self.set_holdings("SPY", 1)
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if self.time.second == 0:
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self.log(f" sma -> IsReady: {self._sma.is_ready}. Value: {self._sma.current.value}")
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self.log(f"custom -> IsReady: {self._custom.is_ready}. Value: {self._custom.value}")
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# Regression test: test fails with an early quit
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diff = abs(self._custom.value - self._sma.current.value)
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if diff > 1e-10:
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self.quit(f"Quit: indicators difference is {diff}")
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def on_end_of_algorithm(self) -> None:
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for item in self._custom_window:
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self.log(f'{item}')
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# Python implementation of SimpleMovingAverage.
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# Represents the traditional simple moving average indicator (SMA).
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class CustomSimpleMovingAverage(PythonIndicator):
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def __init__(self, name: str, period: int) -> None:
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super().__init__()
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self.name = name
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self.value = 0
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self._queue = deque(maxlen=period)
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# Update method is mandatory
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def update(self, input: IndicatorDataPoint) -> bool:
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self._queue.appendleft(input.value)
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count = len(self._queue)
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self.value = np.sum(self._queue) / count
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return count == self._queue.maxlen
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