Adalyat Nazirov
7bb143b215
* Calculate both raw and adjuasted prices for backtesting * disable second price factoring * move and reuse method * test coverage for new methods * reuse scaling method * reuse subscriptionData.Create method * removed unused code * regression test * switch to aapl * fix regression test output * more asserts * fix comments - reduce shortcuts and abbrevation * more comments * merge parameters * reduce number of getting price factors * fix tests * fix tests * fix regression tests * calculate TotalReturn on demand * include TotalReturn calculations * perf tuning * more unit tests for SubscriptionData.Create * simplify things - store and return only raw and precalculated data * fix regression tests; change it back * factor equals 1 for Raw data * small changes * follow code style * implement backward compatibility
59 lines
2.0 KiB
C#
59 lines
2.0 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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*/
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using NUnit.Framework;
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using QuantConnect.Data;
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using QuantConnect.Data.Market;
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using QuantConnect.Lean.Engine.DataFeeds;
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using QuantConnect.Securities;
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using System;
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namespace QuantConnect.Tests.Common.Util
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{
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[TestFixture]
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public class SubscriptionDataConfigExtensionsTests
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{
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private SubscriptionDataConfig _config;
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[SetUp]
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public void Setup()
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{
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_config = new SubscriptionDataConfig(typeof(TradeBar),
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Symbols.SPY,
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Resolution.Daily,
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TimeZones.NewYork,
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TimeZones.NewYork,
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true,
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true,
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false);
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}
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[TestCase(DataNormalizationMode.Raw, 0.5, 100, 100)]
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[TestCase(DataNormalizationMode.Adjusted, 0.5, 100, 50)]
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[TestCase(DataNormalizationMode.SplitAdjusted, 0.5, 100, 50)]
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[TestCase(DataNormalizationMode.TotalReturn, 0.5, 100, 150)]
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public void NormalizePrice(DataNormalizationMode mode, decimal factor, decimal dividents, decimal expected)
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{
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_config.DataNormalizationMode = mode;
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_config.PriceScaleFactor = factor;
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_config.SumOfDividends = dividents;
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Assert.AreEqual(expected, _config.GetNormalizedPrice(100));
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}
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}
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}
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