chore: import upstream snapshot with attribution
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wehub-resource-sync
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/*
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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 System.Collections.Generic;
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using QuantConnect.Configuration;
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using QuantConnect.Data;
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using QuantConnect.Data.Auxiliary;
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using QuantConnect.Interfaces;
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using QuantConnect.Util;
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namespace QuantConnect.Algorithm.CSharp
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{
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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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public class RawDataRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private const string Ticker = "GOOGL";
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private CorporateFactorProvider _factorFile;
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private readonly IEnumerator<decimal> _expectedRawPrices = new List<decimal> { 1158.72m,
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1131.97m, 1114.28m, 1120.15m, 1114.51m, 1134.89m, 1135.1m, 571.50m, 545.25m, 540.63m }.GetEnumerator();
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private Symbol _googl;
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public override void Initialize()
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{
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SetStartDate(2014, 3, 25); //Set Start Date
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SetEndDate(2014, 4, 7); //Set End Date
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SetCash(100000); //Set Strategy Cash
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// Set our DataNormalizationMode to raw
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UniverseSettings.DataNormalizationMode = DataNormalizationMode.Raw;
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_googl = AddEquity(Ticker, Resolution.Daily).Symbol;
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// Get our factor file for this regression
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var dataProvider =
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Composer.Instance.GetExportedValueByTypeName<IDataProvider>(Config.Get("data-provider",
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"DefaultDataProvider"));
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var mapFileProvider = new LocalDiskMapFileProvider();
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mapFileProvider.Initialize(dataProvider);
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var factorFileProvider = new LocalDiskFactorFileProvider();
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factorFileProvider.Initialize(mapFileProvider, dataProvider);
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_factorFile = factorFileProvider.Get(_googl) as CorporateFactorProvider;
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// Prime our expected values
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_expectedRawPrices.MoveNext();
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}
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/// <summary>
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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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/// </summary>
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/// <param name="data">Slice object keyed by symbol containing the stock data</param>
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public override void OnData(Slice slice)
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{
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if (!Portfolio.Invested)
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{
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SetHoldings(_googl, 1);
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}
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if (slice.Bars.ContainsKey(_googl))
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{
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var googlData = slice.Bars[_googl];
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// Assert our volume matches what we expected
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if (_expectedRawPrices.Current != googlData.Close)
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{
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// Our values don't match lets try and give a reason why
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var dayFactor = _factorFile.GetPriceFactor(googlData.Time, DataNormalizationMode.Adjusted);
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var probableRawPrice = googlData.Close / dayFactor; // Undo adjustment
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if (_expectedRawPrices.Current == probableRawPrice)
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{
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throw new RegressionTestException($"Close price was incorrect; it appears to be the adjusted value");
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}
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else
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{
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throw new RegressionTestException($"Close price was incorrect; Data may have changed.");
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}
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}
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// Move to our next expected value
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_expectedRawPrices.MoveNext();
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}
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}
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/// <summary>
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/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
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/// </summary>
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public bool CanRunLocally { get; } = true;
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/// <summary>
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/// This is used by the regression test system to indicate which languages this algorithm is written in.
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/// </summary>
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public List<Language> Languages { get; } = new() { Language.CSharp, Language.Python };
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/// <summary>
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/// Data Points count of all timeslices of algorithm
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/// </summary>
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public long DataPoints => 91;
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/// <summary>
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/// Data Points count of the algorithm history
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/// </summary>
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public int AlgorithmHistoryDataPoints => 0;
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/// <summary>
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/// Final status of the algorithm
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/// </summary>
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public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;
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/// <summary>
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/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
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/// </summary>
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public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
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{
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{"Total Orders", "1"},
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{"Average Win", "0%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "-85.376%"},
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{"Drawdown", "6.900%"},
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{"Expectancy", "0"},
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{"Start Equity", "100000"},
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{"End Equity", "93054.5"},
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{"Net Profit", "-6.946%"},
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{"Sharpe Ratio", "-2.925"},
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{"Sortino Ratio", "-2.881"},
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{"Probabilistic Sharpe Ratio", "3.572%"},
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{"Loss Rate", "0%"},
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{"Win Rate", "0%"},
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{"Profit-Loss Ratio", "0"},
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{"Alpha", "-0.379"},
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{"Beta", "1.959"},
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{"Annual Standard Deviation", "0.257"},
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{"Annual Variance", "0.066"},
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{"Information Ratio", "-2.874"},
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{"Tracking Error", "0.195"},
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{"Treynor Ratio", "-0.384"},
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{"Total Fees", "$1.00"},
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{"Estimated Strategy Capacity", "$140000000.00"},
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{"Lowest Capacity Asset", "GOOG T1AZ164W5VTX"},
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{"Portfolio Turnover", "7.33%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "2284e1b9e7d44577d77987dfe56d3e8d"}
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};
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}
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}
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