Added new alpha: Lunch Break Mean Reversion Alpha (#2931)

* Create MeanReversionLunchBreakAlpha.py
2019-02-20 18:58:53 -08:00
parent 42bd32b15c
commit bc2d1c1963
2 changed files with 122 additions and 0 deletions
@@ -0,0 +1,121 @@
+# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from clr import AddReference
+AddReference("System")
+AddReference("QuantConnect.Common")
+AddReference("QuantConnect.Algorithm")
+AddReference("QuantConnect.Indicators")
+AddReference("QuantConnect.Algorithm.Framework")
+
+from System import *
+from QuantConnect import *
+from QuantConnect.Orders import *
+from QuantConnect.Algorithm import QCAlgorithm
+from QuantConnect.Data.UniverseSelection import *
+from QuantConnect.Indicators import *
+from QuantConnect.Orders.Fees import ConstantFeeModel
+
+#
+# Academic research suggests that stock market participants generally place their orders at the market open and close.
+# Intraday trading volume is J-Shaped, where the minimum trading volume of the day is during lunch-break. Stocks become 
+# more volatile as order flow is reduced and tend to mean-revert during lunch-break.
+#
+# This alpha aims to capture the mean-reversion effect of ETFs during lunch-break by ranking 20 ETFs
+# on their return between the close of the previous day to 12:00 the day after and predicting mean-reversion 
+# in price during lunch-break.
+#
+# Source:  Lunina, V. (June 2011). The Intraday Dynamics of Stock Returns and Trading Activity: Evidence from OMXS 30 (Master's Essay, Lund University). 
+# Retrieved from http://lup.lub.lu.se/luur/download?func=downloadFile&recordOId=1973850&fileOId=1973852
+#
+# This alpha is part of the Benchmark Alpha Series created by QuantConnect which are open sourced so the community 
+# and client funds can see an example of an alpha. 
+#
+
+class MeanReversionLunchBreakAlphaAlgorithm(QCAlgorithmFramework):
+
+    def Initialize(self):
+
+        self.SetStartDate(2018, 1, 1)
+
+        self.SetCash(100000)
+        
+        # Set zero transaction fees
+        self.SetSecurityInitializer(lambda security: security.SetFeeModel(ConstantFeeModel(0)))
+        
+        # Use Hourly Data For Simplicity
+        self.UniverseSettings.Resolution = Resolution.Hour
+        self.SetUniverseSelection(CoarseFundamentalUniverseSelectionModel(self.CoarseSelectionFunction))
+        
+        # Use MeanReversionLunchBreakAlphaModel to establish insights
+        self.SetAlpha(MeanReversionLunchBreakAlphaModel())
+
+        # Equally weigh securities in portfolio, based on insights
+        self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
+        
+        ## Set immediate execution
+        self.SetExecution(ImmediateExecutionModel())
+
+        ## Set null risk management
+        self.SetRiskManagement(NullRiskManagementModel()) 
+        
+    # Sort the data by daily dollar volume and take the top '20' ETFs
+    def CoarseSelectionFunction(self, coarse):
+        sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True) 
+        filtered = [ x.Symbol for x in sortedByDollarVolume if not x.HasFundamentalData ]
+        return filtered[:20]
+
+class MeanReversionLunchBreakAlphaModel(AlphaModel):
+    '''Uses the price return between the close of previous day to 12:00 the day after to 
+    predict mean-reversion of stock price during lunch break and creates direction prediction 
+    for insights accordingly.'''
+
+    def __init__(self, *args, **kwargs): 
+        self.lookback = kwargs['lookback'] if 'lookback' in kwargs else 1
+        self.resolution = Resolution.Hour
+        self.predictionInterval = Time.Multiply(Extensions.ToTimeSpan(self.resolution), self.lookback)
+
+    def Update(self, algorithm, data):
+        
+        insights = []
+        
+        if algorithm.Time.hour != 12:
+            return []
+        
+        # Retrieve symbols for active securities that have data
+        symbols = [x.Key for x in algorithm.ActiveSecurities]
+
+        # Retrieve price history for all securities in the security universe
+        hist = algorithm.History(symbols, 4, self.resolution)
+        
+        # Return 'None' if no history exists
+        if hist.empty:
+            algorithm.Log(f"No data on {algorithm.Time}")
+            return []
+    
+        # Get close price for securities 
+        hist = hist.close.unstack(level=0)
+
+        # Retrieve the price change from close price the previous day
+        returns=hist.pct_change(periods=3).tail(1).reset_index(drop=True).to_dict()
+        
+        # Retrieve the mean value of returns for magnitude prediction
+        mean=hist.pct_change().mean().to_dict()
+        
+        for symbol in list(returns):
+            # Emit "down" insight for the securities that increased in value and
+            # emit "up" insight for securities that have decreased in value
+            direction = InsightDirection.Down if returns[symbol][0] > 0 else InsightDirection.Up
+            insights.append(Insight.Price(symbol, self.predictionInterval, direction, -mean[symbol], None))
+        
+        return insights
@@ -38,6 +38,7 @@
  <ItemGroup>
    <Content Include="Alphas\ForexCalendarAlpha.py" />
    <Content Include="Alphas\GlobalEquityMeanReversionIBSAlpha.py" />
+    <Content Include="Alphas\MeanReversionLunchBreakAlpha.py" />
    <Content Include="Alphas\RebalancingLeveragedETFAlpha.py" />
    <Content Include="BasicSetAccountCurrencyAlgorithm.py" />
    <Content Include="BasicTemplateFuturesFrameworkAlgorithm.py" />