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 QuantConnect.Data;
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using QuantConnect.Indicators;
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using QuantConnect.Interfaces;
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using System;
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using System.Collections.Generic;
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using QuantConnect.Data.Market;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// This regression algorithm asserts the consolidated US equity daily bars from the hour bars exactly matches
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/// the daily bars returned from the database
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/// </summary>
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public class ConsolidateHourBarsIntoDailyBarsRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private Symbol _spy;
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private RelativeStrengthIndex _rsi;
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private RelativeStrengthIndex _rsiTimeDelta;
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private Dictionary<DateTime, decimal> _values = new();
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private int _count;
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private bool _indicatorsCompared;
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public override void Initialize()
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{
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SetStartDate(2020, 5, 1);
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SetEndDate(2020, 6, 5);
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_spy = AddEquity("SPY", Resolution.Hour).Symbol;
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// We will use these two indicators to compare the daily consolidated bars equals
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// the ones returned from the database. We use this specific type of indicator as
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// it depends on its previous values. Thus, if at some point the bars received by
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// the indicators differ, so will their final values
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_rsi = new RelativeStrengthIndex("FIRST", 15, MovingAverageType.Wilders);
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RegisterIndicator(_spy, _rsi, Resolution.Daily, selector: (bar) =>
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{
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var tradeBar = (TradeBar)bar;
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return (tradeBar.Close + tradeBar.Open) / 2;
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});
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// We won't register this indicator as we will update it manually at the end of the
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// month, so that we can compare the values of the indicator that received consolidated
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// bars and the values of this one
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_rsiTimeDelta = new RelativeStrengthIndex("SECOND" ,15, MovingAverageType.Wilders);
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}
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public override void OnData(Slice slice)
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{
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if (IsWarmingUp) return;
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if (slice.ContainsKey(_spy) && slice[_spy] != null)
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{
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if (Time.Month == EndDate.Month)
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{
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var history = History(_spy, _count, Resolution.Daily);
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foreach (var bar in history)
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{
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var time = bar.EndTime.Date;
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var average = (bar.Close + bar.Open) / 2;
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_rsiTimeDelta.Update(bar.EndTime, average);
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if (_rsiTimeDelta.Current.Value != _values[time])
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{
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throw new RegressionTestException($"Both {_rsi.Name} and {_rsiTimeDelta.Name} should have the same values, but they differ. {_rsi.Name}: {_values[time]} | {_rsiTimeDelta.Name}: {_rsiTimeDelta.Current.Value}");
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}
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}
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_indicatorsCompared = true;
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Quit();
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}
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else
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{
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_values[Time.Date] = _rsi.Current.Value;
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// Since the symbol resolution is hour and the symbol is equity, we know the last bar received in a day will
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// be at the market close, this is 16h. We need to count how many daily bars were consolidated in order to know
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// how many we need to request from the history
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if (Time.Hour == 16)
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{
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_count++;
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}
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}
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}
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}
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public override void OnEndOfAlgorithm()
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{
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if (!_indicatorsCompared)
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{
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throw new RegressionTestException($"Indicators {_rsi.Name} and {_rsiTimeDelta.Name} should have been compared, but they were not. Please make sure the indicators are getting SPY data");
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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 => 290;
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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 => 20;
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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", "0"},
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{"Average Win", "0%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "0%"},
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{"Drawdown", "0%"},
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{"Expectancy", "0"},
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{"Start Equity", "100000"},
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{"End Equity", "100000"},
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{"Net Profit", "0%"},
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{"Sharpe Ratio", "0"},
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{"Sortino Ratio", "0"},
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{"Probabilistic Sharpe Ratio", "0%"},
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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"},
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{"Beta", "0"},
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{"Annual Standard Deviation", "0"},
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{"Annual Variance", "0"},
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{"Information Ratio", "-5.215"},
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{"Tracking Error", "0.159"},
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{"Treynor Ratio", "0"},
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{"Total Fees", "$0.00"},
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{"Estimated Strategy Capacity", "$0"},
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{"Lowest Capacity Asset", ""},
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{"Portfolio Turnover", "0%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
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};
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}
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}
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