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>
44 lines
2.0 KiB
Python
44 lines
2.0 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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class IndicatorRibbonBenchmark(QCAlgorithm):
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# Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
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def initialize(self):
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self.set_start_date(2010, 1, 1) #Set Start Date
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self.set_end_date(2018, 1, 1) #Set End Date
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self._spy = self.add_equity("SPY", Resolution.MINUTE).symbol
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count = 50
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offset = 5
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period = 15
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self._ribbon = []
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# define our sma as the base of the ribbon
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self._sma = SimpleMovingAverage(period)
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for x in range(count):
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# define our offset to the zero sma, these various offsets will create our 'displaced' ribbon
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delay = Delay(offset*(x+1))
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# define an indicator that takes the output of the sma and pipes it into our delay indicator
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delayed_sma = IndicatorExtensions.of(delay, self._sma)
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# register our new 'delayed_sma' for automatic updates on a daily resolution
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self.register_indicator(self._spy, delayed_sma, Resolution.DAILY)
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self._ribbon.append(delayed_sma)
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def on_data(self, data):
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# wait for our entire ribbon to be ready
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if not all(x.is_ready for x in self._ribbon): return
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for x in self._ribbon:
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value = x.current.value
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