Michael Handschuh
fdc866fda0
We didn't experience the expected performance improvements. Locally under unit test there was aboout an order of magnitude throughput increase, but when run against the history benchmark, this new approach was 60% slower. We're reverting this for now to perform further analysis and better understand the performance profiling of the python history stack.
86 lines
3.5 KiB
Python
86 lines
3.5 KiB
Python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from clr import AddReference
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AddReference("System")
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AddReference("QuantConnect.Algorithm")
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AddReference("QuantConnect.Common")
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from System import *
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from QuantConnect import *
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from QuantConnect.Algorithm import *
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from QuantConnect.Indicators import *
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from datetime import datetime, timedelta
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import numpy as np
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### <summary>
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### Example of custom volatility model
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### </summary>
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### <meta name="tag" content="using quantconnect" />
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### <meta name="tag" content="indicators" />
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### <meta name="tag" content="reality modelling" />
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class CustomVolatilityModelAlgorithm(QCAlgorithm):
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def Initialize(self):
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self.SetStartDate(2013,10,7) #Set Start Date
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self.SetEndDate(2015,7,15) #Set End Date
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self.SetCash(100000) #Set Strategy Cash
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# Find more symbols here: http://quantconnect.com/data
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self.equity = self.AddEquity("SPY", Resolution.Daily)
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self.equity.SetVolatilityModel(CustomVolatilityModel(10))
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def OnData(self, data):
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if not self.Portfolio.Invested and self.equity.VolatilityModel.Volatility > 0:
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self.SetHoldings("SPY", 1)
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# Python implementation of StandardDeviationOfReturnsVolatilityModel
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# Computes the annualized sample standard deviation of daily returns as the volatility of the security
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# https://github.com/QuantConnect/Lean/blob/master/Common/Securities/Volatility/StandardDeviationOfReturnsVolatilityModel.cs
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class CustomVolatilityModel():
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def __init__(self, periods):
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self.lastUpdate = datetime.min
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self.lastPrice = 0
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self.needsUpdate = False
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self.periodSpan = timedelta(1)
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self.window = RollingWindow[float](periods)
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# Volatility is a mandatory attribute
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self.Volatility = 0
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# Updates this model using the new price information in the specified security instance
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# Update is a mandatory method
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def Update(self, security, data):
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timeSinceLastUpdate = data.EndTime - self.lastUpdate
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if timeSinceLastUpdate >= self.periodSpan and data.Price > 0:
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if self.lastPrice > 0:
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self.window.Add(float(data.Price / self.lastPrice) - 1.0)
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self.needsUpdate = self.window.IsReady
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self.lastUpdate = data.EndTime
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self.lastPrice = data.Price
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if self.window.Count < 2:
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self.Volatility = 0
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return
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if self.needsUpdate:
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self.needsUpdate = False
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std = np.std([ x for x in self.window ])
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self.Volatility = std * np.sqrt(252.0)
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# Returns history requirements for the volatility model expressed in the form of history request
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# GetHistoryRequirements is a mandatory method
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def GetHistoryRequirements(self, security, utcTime):
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# For simplicity's sake, we will not set a history requirement
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return None |