HalldorAndersen
122 lines
5.3 KiB
Python
122 lines
5.3 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 clr import AddReference
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AddReference("System")
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AddReference("QuantConnect.Common")
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AddReference("QuantConnect.Algorithm")
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AddReference("QuantConnect.Indicators")
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AddReference("QuantConnect.Algorithm.Framework")
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from System import *
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from QuantConnect import *
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from QuantConnect.Orders import *
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from QuantConnect.Algorithm import QCAlgorithm
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from QuantConnect.Data.UniverseSelection import *
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from QuantConnect.Indicators import *
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from QuantConnect.Orders.Fees import ConstantFeeModel
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#
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# Academic research suggests that stock market participants generally place their orders at the market open and close.
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# Intraday trading volume is J-Shaped, where the minimum trading volume of the day is during lunch-break. Stocks become
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# more volatile as order flow is reduced and tend to mean-revert during lunch-break.
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#
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# This alpha aims to capture the mean-reversion effect of ETFs during lunch-break by ranking 20 ETFs
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# on their return between the close of the previous day to 12:00 the day after and predicting mean-reversion
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# in price during lunch-break.
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#
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# Source: Lunina, V. (June 2011). The Intraday Dynamics of Stock Returns and Trading Activity: Evidence from OMXS 30 (Master's Essay, Lund University).
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# Retrieved from http://lup.lub.lu.se/luur/download?func=downloadFile&recordOId=1973850&fileOId=1973852
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#
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# This alpha is part of the Benchmark Alpha Series created by QuantConnect which are open sourced so the community
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# and client funds can see an example of an alpha.
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#
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class MeanReversionLunchBreakAlphaAlgorithm(QCAlgorithmFramework):
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def Initialize(self):
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self.SetStartDate(2018, 1, 1)
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self.SetCash(100000)
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# Set zero transaction fees
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self.SetSecurityInitializer(lambda security: security.SetFeeModel(ConstantFeeModel(0)))
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# Use Hourly Data For Simplicity
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self.UniverseSettings.Resolution = Resolution.Hour
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self.SetUniverseSelection(CoarseFundamentalUniverseSelectionModel(self.CoarseSelectionFunction))
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# Use MeanReversionLunchBreakAlphaModel to establish insights
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self.SetAlpha(MeanReversionLunchBreakAlphaModel())
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# Equally weigh securities in portfolio, based on insights
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self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
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## Set immediate execution
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self.SetExecution(ImmediateExecutionModel())
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## Set null risk management
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self.SetRiskManagement(NullRiskManagementModel())
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# Sort the data by daily dollar volume and take the top '20' ETFs
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def CoarseSelectionFunction(self, coarse):
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sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
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filtered = [ x.Symbol for x in sortedByDollarVolume if not x.HasFundamentalData ]
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return filtered[:20]
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class MeanReversionLunchBreakAlphaModel(AlphaModel):
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'''Uses the price return between the close of previous day to 12:00 the day after to
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predict mean-reversion of stock price during lunch break and creates direction prediction
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for insights accordingly.'''
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def __init__(self, *args, **kwargs):
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self.lookback = kwargs['lookback'] if 'lookback' in kwargs else 1
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self.resolution = Resolution.Hour
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self.predictionInterval = Time.Multiply(Extensions.ToTimeSpan(self.resolution), self.lookback)
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def Update(self, algorithm, data):
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insights = []
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if algorithm.Time.hour != 12:
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return []
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# Retrieve symbols for active securities that have data
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symbols = [x.Key for x in algorithm.ActiveSecurities]
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# Retrieve price history for all securities in the security universe
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hist = algorithm.History(symbols, 4, self.resolution)
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# Return 'None' if no history exists
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if hist.empty:
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algorithm.Log(f"No data on {algorithm.Time}")
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return []
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# Get close price for securities
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hist = hist.close.unstack(level=0)
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# Retrieve the price change from close price the previous day
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returns=hist.pct_change(periods=3).tail(1).reset_index(drop=True).to_dict()
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# Retrieve the mean value of returns for magnitude prediction
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mean=hist.pct_change().mean().to_dict()
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for symbol in list(returns):
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# Emit "down" insight for the securities that increased in value and
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# emit "up" insight for securities that have decreased in value
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direction = InsightDirection.Down if returns[symbol][0] > 0 else InsightDirection.Up
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insights.append(Insight.Price(symbol, self.predictionInterval, direction, -mean[symbol], None))
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return insights
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