Jasper van Merle
68 lines
2.9 KiB
Python
68 lines
2.9 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.Core")
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AddReference("QuantConnect.Common")
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AddReference("QuantConnect.Algorithm")
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from System import *
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from QuantConnect import *
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from QuantConnect.Algorithm import QCAlgorithm
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from QuantConnect.Data.Auxiliary import FactorFile
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from QuantConnect.Data.UniverseSelection import *
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from QuantConnect.Orders import OrderStatus
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from QuantConnect.Orders.Fees import ConstantFeeModel
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_ticker = "GOOGL";
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_factorFile = FactorFile.Read(_ticker, Market.USA);
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_expectedRawPrices = [ 1158.1100, 1158.7200,
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1131.7800, 1114.2800, 1119.6100, 1114.5500, 1135.3200, 567.59000, 571.4900, 545.3000, 540.6400 ]
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# <summary>
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# In this algorithm we demonstrate how to use the raw data for our securities
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# and verify that the behavior is correct.
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# </summary>
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# <meta name="tag" content="using data" />
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# <meta name="tag" content="regression test" />
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class RawDataRegressionAlgorithm(QCAlgorithm):
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def Initialize(self):
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self.SetStartDate(2014, 3, 25);
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self.SetEndDate(2014, 4, 7);
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self.SetCash(100000);
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# Set our DataNormalizationMode to raw
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self.UniverseSettings.DataNormalizationMode = DataNormalizationMode.Raw;
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self._googl = self.AddEquity(_ticker, Resolution.Daily).Symbol;
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def OnData(self, data):
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if not self.Portfolio.Invested:
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self.SetHoldings(self._googl, 1);
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if (data.Bars.ContainsKey(self._googl)):
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googlData = data.Bars[self._googl];
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# Assert our volume matches what we expected
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if _expectedRawPrices.pop(0) != googlData.Close:
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# Our values don't match lets try and give a reason why
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dayFactor = _factorFile.GetPriceScaleFactor(googlData.Time);
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probableRawPrice = googlData.Close / dayFactor; # Undo adjustment
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if _expectedRawPrices.Current == probableRawPrice:
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raise Exception("Close price was incorrect; it appears to be the adjusted value")
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else:
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raise Exception("Close price was incorrect; Data may have changed.")
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