CSharp and Python versions of new alpha, MortgageRateVolatilityAlpha

2019-04-03 15:52:30 -07:00
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+# 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.
+
+'''
+    This Alpha Model uses Wells Fargo 30-year Fixed Rate Mortgage data from Quandl to 
+    generate Insights about the movement of Real Estate ETFs. Mortgage rates can provide information 
+    regarding the general price trend of real estate, and ETFs provide good continuous-time instruments 
+    to measure the impact against. Volatility in mortgage rates tends to put downward pressure on real 
+    estate prices, whereas stable mortgage rates, regardless of true rate, lead to stable or higher real
+    estate prices. This Alpha model seeks to take advantage of this correlation by emitting insights
+    based on volatility and rate deviation from its historic mean.
+
+    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.
+'''
+
+from clr import AddReference
+AddReference("QuantConnect.Common")
+AddReference("QuantConnect.Algorithm")
+AddReference("QuantConnect.Algorithm.Framework")
+AddReference("QuantConnect.Indicators")
+
+from QuantConnect import *
+from QuantConnect.Algorithm import *
+from QuantConnect.Indicators import *
+from QuantConnect.Algorithm.Framework import *
+from QuantConnect.Algorithm.Framework.Risk import *
+from QuantConnect.Algorithm.Framework.Portfolio import *
+from QuantConnect.Algorithm.Framework.Execution import *
+from QuantConnect.Algorithm.Framework.Selection import *
+
+
+class MortgageRateVolatilityAlgorithm(QCAlgorithmFramework):
+
+    def Initialize(self):
+
+        # Set requested data resolution
+        self.SetStartDate(2017, 1, 1)   #Set Start Date
+        self.SetCash(100000)           #Set Strategy Cash
+
+        self.UniverseSettings.Resolution = Resolution.Daily
+        
+        ## Universe of six liquid real estate ETFs
+        etfs = ['VNQ', 'REET', 'TAO', 'FREL', 'SRET', 'HIPS']
+        symbols = [ Symbol.Create(etf, SecurityType.Equity, Market.USA) for etf in etfs ]
+        self.SetSecurityInitializer(lambda security: security.SetFeeModel(ConstantFeeModel(0)))
+        self.SetUniverseSelection( ManualUniverseSelectionModel(symbols) )
+            
+        self.SetAlpha(MortgageRateVolatilityAlphaModel(self))
+        
+        self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel()) 
+        
+        self.SetExecution(ImmediateExecutionModel())
+        
+        self.SetRiskManagement(NullRiskManagementModel())
+
+
+class MortgageRateVolatilityAlphaModel(AlphaModel):
+    
+    def __init__(self, algorithm, indicatorPeriod = 15, insightMagnitude = 0.005, deviations = 2):
+        ## Add Quandl data for a Well's Fargo 30-year Fixed Rate mortgage
+        self.mortgageRate = algorithm.AddData(QuandlMortgagePriceColumns, 'WFC/PR_GOV_30YFIXEDVA_APR').Symbol
+        self.indicatorPeriod = indicatorPeriod
+        self.insightDuration = TimeSpan.FromDays(indicatorPeriod)
+        self.insightMagnitude = insightMagnitude
+        self.deviations = deviations
+        
+        ## Add indicators for the mortgage rate -- Standard Deviation and Simple Moving Average
+        self.mortgageRateStd = algorithm.STD(self.mortgageRate.Value, indicatorPeriod)
+        self.mortgageRateSma = algorithm.SMA(self.mortgageRate.Value, indicatorPeriod)
+        
+        ## Use a history call to warm-up the indicators
+        self.WarmupIndicators(algorithm)
+    
+    def Update(self, algorithm, data):
+        insights = []
+        
+        ## Return empty list if data slice doesn't contain monrtgage rate data
+        if self.mortgageRate not in data.Keys:
+            return []
+
+        ## Extract current mortgage rate, the current STD indicator value, and current SMA value
+        mortgageRate = data[self.mortgageRate].Value
+        deviation = self.deviations * self.mortgageRateStd.Current.Value
+        sma = self.mortgageRateSma.Current.Value
+        
+        ## Loop through all Active Securities to emit insights
+        for security in algorithm.ActiveSecurities.Keys:
+            ## Mortgage rate Symbol will be in the collection, so skip it
+            if security == self.mortgageRate: continue
+            
+            ## If volatility in mortgage rates is high, then we emit an Insight to sell
+            if (mortgageRate < sma - deviation) or (mortgageRate > sma + deviation):
+                insights.append(Insight(security, self.insightDuration, InsightType.Price, InsightDirection.Down, self.insightMagnitude, None))
+            
+            ## If volatility in mortgage rates is low, then we emit an Insight to buy
+            if (mortgageRate < sma - deviation/2) or (mortgageRate > sma + deviation/2):
+                insights.append(Insight(security, self.insightDuration, InsightType.Price, InsightDirection.Up, self.insightMagnitude, None))
+
+        return insights
+    
+    def WarmupIndicators(self, algorithm):
+        ## Make a history call and update the indicators
+        history = algorithm.History(self.mortgageRate, self.indicatorPeriod, Resolution.Daily)
+        for index, row in history.iterrows():
+            self.mortgageRateStd.Update(index[1], row['value'])
+            self.mortgageRateSma.Update(index[1], row['value'])
+    
+    def OnSecuritiesChanged(self, algorithm, changes):
+        ## No need to do anything here -- static Universe
+        pass
+    
+class QuandlMortgagePriceColumns(PythonQuandl):
+    
+    def __init__(self):
+        ## Rename the Quandl object column to the data we want, which is the 'Value' column
+        ## of the CSV that our API call returns
+        self.ValueColumnName = "Value"