Rename UncorrelatedToSPYFrameworkAlgorithm.py to UncorrelatedToBenchmarkFrameworkAlgorithm.py

Algorithm.Python/UncorrelatedToBenchmarkFrameworkAlgorithm.py

@@ -0,0 +1,74 @@
+# 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.Algorithm")
+AddReference("QuantConnect.Algorithm.Framework")
+AddReference("QuantConnect.Common")
+
+from System import *
+from QuantConnect import *
+from QuantConnect.Orders import *
+from QuantConnect.Algorithm import *
+from QuantConnect.Algorithm.Framework import *
+from QuantConnect.Algorithm.Framework.Alphas import *
+from QuantConnect.Algorithm.Framework.Portfolio import EqualWeightingPortfolioConstructionModel
+from QuantConnect.Algorithm.Framework.Execution import ImmediateExecutionModel
+from Selection.UncorrelatedToBenchmarkUniverseSelectionModel import UncorrelatedToBenchmarkUniverseSelectionModel
+from datetime import timedelta
+
+class UncorrelatedToBenchmarkFrameworkAlgorithm(QCAlgorithmFramework):
+
+    def Initialize(self):
+        
+        self.UniverseSettings.Resolution = Resolution.Daily
+        
+        self.SetStartDate(2017,1,1)   # Set Start Date
+        self.SetEndDate(2017,3,1)     # Set End Date
+        self.SetCash(1000000)         # Set Strategy Cash
+
+        benchmark = Symbol.Create("SPY", SecurityType.Equity, Market.USA)
+        self.SetUniverseSelection(UncorrelatedToBenchmarkUniverseSelectionModel(benchmark))
+        self.SetAlpha(UncorrelatedToBenchmarkAlphaModel())
+        self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
+        self.SetExecution(ImmediateExecutionModel())
+
+
+class UncorrelatedToBenchmarkAlphaModel(AlphaModel):
+    '''Uses ranking of intraday percentage difference between open price and close price to create magnitude and direction prediction for insights'''
+
+    def __init__(self, numberOfStocks = 10, predictionInterval = timedelta(1)): 
+        self.predictionInterval = predictionInterval
+        self.numberOfStocks = numberOfStocks
+
+    def Update(self, algorithm, data):
+        symbolsRet = dict()
+
+        for kvp in algorithm.ActiveSecurities:
+            security = kvp.Value
+            if security.HasData:
+                open = security.Open
+                if open != 0:
+                    symbolsRet[security.Symbol] = security.Close / open - 1
+
+        # Rank on the absolute value of price change
+        symbolsRet = dict(sorted(symbolsRet.items(), key=lambda kvp: abs(kvp[1]),reverse=True)[:self.numberOfStocks])
+
+        insights = []
+        for symbol, price_change in symbolsRet.items():
+            # Emit "up" insight if the price change is positive and "down" otherwise
+            direction = InsightDirection.Up if price_change > 0 else InsightDirection.Down
+            insights.append(Insight.Price(symbol, self.predictionInterval, direction, abs(price_change), None))
+
+        return insights