Enables custom volatility models in python algorithms

Creates a python wrapper for volatility models created in python algorithms and adds a method to the Security object to set such models. Adds an algorithm to show how volatility models can be implemented.
2018-01-29 23:27:58 +00:00
parent 82d403afd3
commit dd14821e75
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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.
+
+from clr import AddReference
+AddReference("System")
+AddReference("QuantConnect.Algorithm")
+AddReference("QuantConnect.Common")
+
+from System import *
+from QuantConnect import *
+from QuantConnect.Algorithm import *
+from QuantConnect.Indicators import *
+from datetime import datetime, timedelta
+import numpy as np
+
+### <summary>
+### Example of custom volatility model 
+### </summary>
+### <meta name="tag" content="using quantconnect" />
+### <meta name="tag" content="indicators" />
+### <meta name="tag" content="reality modelling" />
+class CustomVolatilityModelAlgorithm(QCAlgorithm):
+
+    def Initialize(self):
+        self.SetStartDate(2013,10,07)  #Set Start Date
+        self.SetEndDate(2015,07,15)    #Set End Date
+        self.SetCash(100000)           #Set Strategy Cash
+        # Find more symbols here: http://quantconnect.com/data
+        self.equity = self.AddEquity("SPY", Resolution.Daily)
+        self.equity.SetVolatilityModel(CustomVolatilityModel(10))
+
+
+    def OnData(self, data):
+        if not self.Portfolio.Invested and self.equity.VolatilityModel.Volatility > 0:
+            self.SetHoldings("SPY", 1)
+
+
+# Python implementation of StandardDeviationOfReturnsVolatilityModel
+# Computes the annualized sample standard deviation of daily returns as the volatility of the security
+# https://github.com/QuantConnect/Lean/blob/master/Common/Securities/Volatility/StandardDeviationOfReturnsVolatilityModel.cs
+class CustomVolatilityModel():
+    def __init__(self, periods):
+        self.lastUpdate = datetime.min
+        self.lastPrice = 0
+        self.needsUpdate = False
+        self.periodSpan = timedelta(1)
+        self.window = RollingWindow[float](periods)
+
+        # Volatility is a mandatory attribute
+        self.Volatility = 0
+
+    # Updates this model using the new price information in the specified security instance
+    # Update is a mandatory method
+    def Update(self, security, data):
+        timeSinceLastUpdate = data.EndTime - self.lastUpdate
+        if timeSinceLastUpdate >= self.periodSpan and data.Price > 0:
+            if self.lastPrice > 0:
+                self.window.Add(float(data.Price / self.lastPrice) - 1.0)
+                self.needsUpdate = self.window.IsReady
+            self.lastUpdate = data.EndTime
+            self.lastPrice = data.Price
+
+        if self.window.Count < 2:
+            self.Volatility = 0
+            return
+
+        if self.needsUpdate:
+            self.needsUpdate = False
+            std = np.std([ x for x in self.window ])
+            self.Volatility = std * np.sqrt(252.0)
+
+    # Returns history requirements for the volatility model expressed in the form of history request
+    # GetHistoryRequirements is a mandatory method
+    def GetHistoryRequirements(self, security, utcTime):
+        # For simplicity's sake, we will not set a history requirement 
+        return None