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 clr import AddReference
  AddReference("System")
  AddReference("QuantConnect.Algorithm")
  AddReference("QuantConnect.Indicators")
  AddReference("QuantConnect.Common")
  
  from System import *
  from QuantConnect import *
  from QuantConnect.Algorithm import *
  from QuantConnect.Indicators import *
  from QuantConnect.Parameters import *
  
  ### <summary>
  ### Demonstration of the parameter system of QuantConnect. Using parameters you can pass the values required into C# algorithms for optimization.
  ### </summary>
  ### <meta name="tag" content="optimization" />
  ### <meta name="tag" content="using quantconnect" />
  class ParameterizedAlgorithm(QCAlgorithm):
  
      def Initialize(self):
          '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
  
          self.SetStartDate(2013, 10, 7)   #Set Start Date
          self.SetEndDate(2013, 10, 11)    #Set End Date
          self.SetCash(100000)             #Set Strategy Cash
          # Find more symbols here: http://quantconnect.com/data
          self.AddEquity("SPY")
  
          # Receive parameters from the Job
          ema_fast = self.GetParameter("ema-fast")
          ema_slow = self.GetParameter("ema-slow")
  
          # The values 100 and 200 are just default values that only used if the parameters do not exist
          fast_period = 100 if ema_fast is None else int(ema_fast)
          slow_period = 200 if ema_slow is None else int(ema_slow)
  
          self.fast = self.EMA("SPY", fast_period)
          self.slow = self.EMA("SPY", slow_period)
  
  
      def OnData(self, data):
          '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.'''
  
          # wait for our indicators to ready
          if not self.fast.IsReady or not self.slow.IsReady:
              return
  
          fast = self.fast.Current.Value
          slow = self.slow.Current.Value
  
          if fast > slow * 1.001:
              self.SetHoldings("SPY", 1)
          elif fast < slow * 0.999:
              self.Liquidate("SPY")