text: # 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 AlgorithmImports import *
  
  ### <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(2013,10,7)   #Set Start Date
          self.SetEndDate(2015,7,15)     #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
  