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.
63 lines
3.1 KiB
Python
63 lines
3.1 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.Indicators")
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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 QuantConnect.Data.Custom import *
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from QuantConnect.Python import PythonQuandl
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from datetime import datetime, timedelta
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### <summary>
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### Using the underlying dynamic data class "Quandl" QuantConnect take care of the data
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### importing and definition for you. Simply point QuantConnect to the Quandl Short Code.
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### The Quandl object has properties which match the spreadsheet headers.
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### If you have multiple quandl streams look at data.Symbol to distinguish them.
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### </summary>
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### <meta name="tag" content="custom data" />
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="quandl" />
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class QuandlImporterAlgorithm(QCAlgorithm):
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def Initialize(self):
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'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
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self.quandlCode = "SSE/YHO"
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Quandl.SetAuthCode("JjAt5_5Ggmmoe5zUKipm")
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self.SetStartDate(2014,4,1) #Set Start Date
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self.SetEndDate(datetime.today() - timedelta(1)) #Set End Date
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self.SetCash(25000) #Set Strategy Cash
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self.AddData(QuandlCustomColumns, self.quandlCode, Resolution.Daily, TimeZones.NewYork)
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self.sma = self.SMA(self.quandlCode, 14)
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def OnData(self, data):
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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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if not self.Portfolio.HoldStock:
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self.SetHoldings(self.quandlCode, 1)
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self.Debug("Purchased {0} >> {1}".format(self.quandlCode, self.Time))
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self.Plot(self.quandlCode, "PriceSMA", self.sma.Current.Value)
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# Quandl often doesn't use close columns so need to tell LEAN which is the "value" column.
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class QuandlCustomColumns(PythonQuandl):
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'''Custom quandl data type for setting customized value column name. Value column is used for the primary trading calculations and charting.'''
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def __init__(self):
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# Define ValueColumnName: cannot be None, Empty or non-existant column name
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self.ValueColumnName = "last"
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