Martin-Molinero
bbbab6d9a8
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* Refactor alpha statistics - Refactor alpha statistics, cleaning up and simplifying no longer required calculations and scoring - Adding new InsightEvaluator abstraction, adding C# & PY regression algorithms * Optimization backtest result json converter update * Address reviews - Remove IAlphaHandler, move insight storage responsability to IResultHandler and centralizing insight collection on the QCAlgorithm.Insights to be reused by the framework models - Fix portfolio turnover single day backtests and duplicate time sampling handling. Updating regression algorithms * Add InsightCollection tests and minor fixes * Adding more & improved tests
79 lines
3.5 KiB
Python
79 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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### Regression algorithm showing how to define a custom insight scoring function and using the insight manager
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### </summary>
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class InsightScoringRegressionAlgorithm(QCAlgorithm):
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'''Regression algorithm showing how to define a custom insight evaluator'''
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def Initialize(self):
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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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self.SetStartDate(2013,10,7)
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self.SetEndDate(2013,10,11)
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symbols = [ Symbol.Create("SPY", SecurityType.Equity, Market.USA) ]
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self.SetUniverseSelection(ManualUniverseSelectionModel(symbols))
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self.SetAlpha(ConstantAlphaModel(InsightType.Price, InsightDirection.Up, timedelta(minutes = 20), 0.025, None))
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self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel(Resolution.Daily))
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self.SetExecution(ImmediateExecutionModel())
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self.SetRiskManagement(MaximumDrawdownPercentPerSecurity(0.01))
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# we specify a custom insight evaluator
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self.Insights.SetInsightScoreFunction(CustomInsightScoreFunction(self.Securities))
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def OnEndOfAlgorithm(self):
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allInsights = self.Insights.GetInsights(lambda insight: True)
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if len(allInsights) != 100 or len(self.Insights.GetInsights()) != 100:
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raise ValueError(f'Unexpected insight count found {allInsights.Count}')
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if sum(1 for insight in allInsights if insight.Score.Magnitude == 0 or insight.Score.Direction == 0) < 5:
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raise ValueError(f'Insights not scored!')
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if sum(1 for insight in allInsights if insight.Score.IsFinalScore) < 99:
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raise ValueError(f'Insights not finalized!')
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class CustomInsightScoreFunction():
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def __init__(self, securities):
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self._securities = securities
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self._openInsights = {}
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def Score(self, insightManager, utcTime):
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openInsights = insightManager.GetActiveInsights(utcTime)
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for insight in openInsights:
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self._openInsights[insight.Id] = insight
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toRemove = []
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for openInsight in self._openInsights.values():
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security = self._securities[openInsight.Symbol]
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openInsight.ReferenceValueFinal = security.Price
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score = openInsight.ReferenceValueFinal - openInsight.ReferenceValue
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openInsight.Score.SetScore(InsightScoreType.Direction, score, utcTime)
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openInsight.Score.SetScore(InsightScoreType.Magnitude, score * 2, utcTime)
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openInsight.EstimatedValue = score * 100
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if openInsight.IsExpired(utcTime):
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openInsight.Score.Finalize(utcTime)
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toRemove.append(openInsight)
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# clean up
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for insightToRemove in toRemove:
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self._openInsights.pop(insightToRemove.Id)
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