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.
84 lines
3.5 KiB
Python
84 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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AddReference("QuantConnect.Indicators")
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from System import *
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from QuantConnect import *
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from QuantConnect.Indicators import *
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from QuantConnect.Data import *
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from QuantConnect.Data.Market import *
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from QuantConnect.Data.Custom import *
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from QuantConnect.Algorithm import *
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from QuantConnect.Python import PythonQuandl
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### <summary>
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### The algorithm creates new indicator value with the existing indicator method by Indicator Extensions
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### Demonstration of using the external custom datasource Quandl to request the VIX and VXV daily data
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="using quantconnect" />
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### <meta name="tag" content="custom data" />
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### <meta name="tag" content="indicators" />
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### <meta name="tag" content="indicator classes" />
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### <meta name="tag" content="plotting indicators" />
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### <meta name="tag" content="charting" />
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class CustomDataIndicatorExtensionsAlgorithm(QCAlgorithm):
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# Initialize the data and resolution you require for your strategy
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def Initialize(self):
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self.SetStartDate(2014,1,1)
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self.SetEndDate(2018,1,1)
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self.SetCash(25000)
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self.vix = 'CBOE/VIX'
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self.vxv = 'CBOE/VXV'
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# Define the symbol and "type" of our generic data
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self.AddData(QuandlVix, self.vix, Resolution.Daily)
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self.AddData(Quandl, self.vxv, Resolution.Daily)
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# Set up default Indicators, these are just 'identities' of the closing price
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self.vix_sma = self.SMA(self.vix, 1, Resolution.Daily)
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self.vxv_sma = self.SMA(self.vxv, 1, Resolution.Daily)
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# This will create a new indicator whose value is smaVXV / smaVIX
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self.ratio = IndicatorExtensions.Over(self.vxv_sma, self.vix_sma)
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# Plot indicators each time they update using the PlotIndicator function
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self.PlotIndicator("Ratio", self.ratio)
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self.PlotIndicator("Data", self.vix_sma, self.vxv_sma)
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# OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
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def OnData(self, data):
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# Wait for all indicators to fully initialize
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if not (self.vix_sma.IsReady and self.vxv_sma.IsReady and self.ratio.IsReady): return
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if not self.Portfolio.Invested and self.ratio.Current.Value > 1:
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self.MarketOrder(self.vix, 100)
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elif self.ratio.Current.Value < 1:
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self.Liquidate()
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# In CBOE/VIX data, there is a "vix close" column instead of "close" which is the
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# default column namein LEAN Quandl custom data implementation.
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# This class assigns new column name to match the the external datasource setting.
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class QuandlVix(PythonQuandl):
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def __init__(self):
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self.ValueColumnName = "VIX Close" |