9b7af08b3e
Build & Test Lean / build (push) Has been cancelled
Regression Tests / build (push) Has been cancelled
* Fixes Market On Open Fill of Equity Fill Model Only use trade data (Tick with Trade type or TradeBar) to get the open price, since MOO is filled with the opening action price. Ensure that this method doesn't use trade data from before the market opens for high-resolution data case. Fix unit tests to show that the new implementation only fills with trade data from the current open market. Change regression tests to reflect the bug fix. In the `ExtendedMarketHoursHistoryRegressionAlgorithm`, MOO was filled with extended market hours. * Fix Bug for Tick Resolution Case For tick susbcription, the tick with the open price information is the the first valid (non-zero) tick of trade type from an open market. Addresses peer-review by moving the if-condition for data belonging to the open market where the subscriscribed types are checked. * Fix Bug for Low Resolution Edge Case For the edge case where the order is placed after the trade bar is open, for example, order places at 1 pm with daily-resolution data. The fill model will not use the open of the bar that will close at midnight, since this value is prior to the order. Adds unit test. Change regression tests to reflect the bug fix. In the `RegressionAlgorithm`, MOO was filled with open prior to the order. The algorithm now has one order less, since the last MOO would need to wait another day to be filled. * Implements SaleCondition and Exchange Check For Tick - Adds additional unit tests for MOO * Fixes Regression Test in DataConsolidatorPythonWrapperTests * Addresses Peer-Review - Adds new unit test cases.
235 lines
9.2 KiB
C#
235 lines
9.2 KiB
C#
/*
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* 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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*/
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using System;
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using NUnit.Framework;
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using Python.Runtime;
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using System.Collections.Generic;
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using QuantConnect.Data.Market;
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using QuantConnect.Python;
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namespace QuantConnect.Tests.Python
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{
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[TestFixture]
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public class DataConsolidatorPythonWrapperTests
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{
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[Test]
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public void UpdatePyConsolidator()
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{
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using (Py.GIL())
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{
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var module = PythonEngine.ModuleFromString(Guid.NewGuid().ToString(),
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"from AlgorithmImports import *\n" +
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"class CustomConsolidator():\n" +
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" def __init__(self):\n" +
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" self.UpdateWasCalled = False\n" +
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" self.InputType = QuoteBar\n" +
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" self.OutputType = QuoteBar\n" +
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" self.Consolidated = None\n" +
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" self.WorkingData = None\n" +
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" def Update(self, data):\n" +
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" self.UpdateWasCalled = True\n");
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var customConsolidator = module.GetAttr("CustomConsolidator").Invoke();
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var wrapper = new DataConsolidatorPythonWrapper(customConsolidator);
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var time = DateTime.Today;
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var period = TimeSpan.FromMinutes(1);
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var bar1 = new QuoteBar
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{
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Time = time,
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Symbol = Symbols.SPY,
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Bid = new Bar(1, 2, 0.75m, 1.25m),
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LastBidSize = 3,
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Ask = null,
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LastAskSize = 0,
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Value = 1,
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Period = period
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};
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wrapper.Update(bar1);
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bool called;
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customConsolidator.GetAttr("UpdateWasCalled").TryConvert(out called);
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Assert.True(called);
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}
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}
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[Test]
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public void ScanPyConsolidator()
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{
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using (Py.GIL())
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{
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var module = PythonEngine.ModuleFromString(Guid.NewGuid().ToString(),
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"from AlgorithmImports import *\n" +
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"class CustomConsolidator():\n" +
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" def __init__(self):\n" +
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" self.ScanWasCalled = False\n" +
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" self.InputType = QuoteBar\n" +
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" self.OutputType = QuoteBar\n" +
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" self.Consolidated = None\n" +
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" self.WorkingData = None\n" +
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" def Scan(self,time):\n" +
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" self.ScanWasCalled = True\n");
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var customConsolidator = module.GetAttr("CustomConsolidator").Invoke();
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var wrapper = new DataConsolidatorPythonWrapper(customConsolidator);
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var time = DateTime.Today;
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var period = TimeSpan.FromMinutes(1);
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wrapper.Scan(DateTime.Now);
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bool called;
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customConsolidator.GetAttr("ScanWasCalled").TryConvert(out called);
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Assert.True(called);
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}
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}
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[Test]
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public void InputTypePyConsolidator()
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{
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using (Py.GIL())
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{
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var module = PythonEngine.ModuleFromString(Guid.NewGuid().ToString(),
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"from AlgorithmImports import *\n" +
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"class CustomConsolidator():\n" +
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" def __init__(self):\n" +
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" self.InputType = QuoteBar\n" +
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" self.OutputType = QuoteBar\n" +
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" self.Consolidated = None\n" +
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" self.WorkingData = None\n");
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var customConsolidator = module.GetAttr("CustomConsolidator").Invoke();
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var wrapper = new DataConsolidatorPythonWrapper(customConsolidator);
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var time = DateTime.Today;
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var period = TimeSpan.FromMinutes(1);
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var type = wrapper.InputType;
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Assert.True(type == typeof(QuoteBar));
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}
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}
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[Test]
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public void OutputTypePyConsolidator()
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{
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using (Py.GIL())
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{
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var module = PythonEngine.ModuleFromString(Guid.NewGuid().ToString(),
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"from AlgorithmImports import *\n" +
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"class CustomConsolidator():\n" +
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" def __init__(self):\n" +
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" self.InputType = QuoteBar\n" +
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" self.OutputType = QuoteBar\n" +
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" self.Consolidated = None\n" +
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" self.WorkingData = None\n");
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var customConsolidator = module.GetAttr("CustomConsolidator").Invoke();
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var wrapper = new DataConsolidatorPythonWrapper(customConsolidator);
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var time = DateTime.Today;
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var period = TimeSpan.FromMinutes(1);
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var type = wrapper.OutputType;
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Assert.True(type == typeof(QuoteBar));
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}
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}
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[Test]
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public void RunRegressionAlgorithm()
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{
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var parameter = new RegressionTests.AlgorithmStatisticsTestParameters("CustomConsolidatorRegressionAlgorithm",
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new Dictionary<string, string> {
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{"Total Trades", "49"},
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{"Average Win", "0.25%"},
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{"Average Loss", "-0.01%"},
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{"Compounding Annual Return", "65.750%"},
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{"Drawdown", "0.300%"},
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{"Expectancy", "2.577"},
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{"Net Profit", "1.067%"},
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{"Sharpe Ratio", "8.793"},
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{"Probabilistic Sharpe Ratio", "89.147%"},
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{"Loss Rate", "80%"},
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{"Win Rate", "20%"},
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{"Profit-Loss Ratio", "16.88"},
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{"Alpha", "0.522"},
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{"Beta", "0.345"},
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{"Annual Standard Deviation", "0.081"},
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{"Annual Variance", "0.007"},
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{"Information Ratio", "1.136"},
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{"Tracking Error", "0.144"},
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{"Treynor Ratio", "2.061"},
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{"Total Fees", "$69.81"}
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},
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Language.Python,
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AlgorithmStatus.Completed);
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AlgorithmRunner.RunLocalBacktest(parameter.Algorithm,
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parameter.Statistics,
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parameter.AlphaStatistics,
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parameter.Language,
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parameter.ExpectedFinalStatus);
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}
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[Test]
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public void AttachAndTriggerEvent()
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{
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using (Py.GIL())
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{
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var module = PythonEngine.ModuleFromString(Guid.NewGuid().ToString(),
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"from AlgorithmImports import *\n" +
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"class ImplementingClass():\n" +
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" def __init__(self):\n" +
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" self.EventCalled = False\n" +
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" self.Consolidator = CustomConsolidator(timedelta(minutes=1))\n" +
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" self.Consolidator.DataConsolidated += self.ConsolidatorEvent\n" +
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" def ConsolidatorEvent(self, sender, bar):\n" +
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" self.EventCalled = True\n" +
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"class CustomConsolidator(QuoteBarConsolidator):\n" +
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" def __init__(self,span):\n" +
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" self.Span = span");
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var implementingClass = module.GetAttr("ImplementingClass").Invoke();
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var customConsolidator = implementingClass.GetAttr("Consolidator");
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var wrapper = new DataConsolidatorPythonWrapper(customConsolidator);
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bool called;
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implementingClass.GetAttr("EventCalled").TryConvert(out called);
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Assert.False(called);
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var time = DateTime.Today;
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var period = TimeSpan.FromMinutes(1);
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var bar1 = new QuoteBar
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{
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Time = time,
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Symbol = Symbols.SPY,
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Bid = new Bar(1, 2, 0.75m, 1.25m),
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LastBidSize = 3,
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Ask = null,
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LastAskSize = 0,
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Value = 1,
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Period = period
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};
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wrapper.Update(bar1);
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wrapper.Scan(time.AddMinutes(1));
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implementingClass.GetAttr("EventCalled").TryConvert(out called);
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Assert.True(called);
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}
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}
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}
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}
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