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 QuantConnect.Data;
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using QuantConnect.Interfaces;
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using QuantConnect.Indicators;
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using QuantConnect.Data.Market;
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using System.Collections.Generic;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Regression algorithm asserting warming up with a lower resolution for speed is respected
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/// </summary>
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public class WarmupDailyResolutionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private long _previousSampleCount;
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private bool _warmedUpTradeBars;
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private bool _warmedUpQuoteBars;
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protected SimpleMovingAverage Sma { get; set; }
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protected TimeSpan ExpectedDataSpan { get; set; }
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protected TimeSpan ExpectedWarmupDataSpan { get; set; }
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public override void Initialize()
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{
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SetStartDate(2013, 10, 10);
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SetEndDate(2013, 10, 11);
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AddEquity("SPY", Resolution.Hour);
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ExpectedDataSpan = Resolution.Hour.ToTimeSpan();
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SetWarmUp(TimeSpan.FromDays(3), Resolution.Daily);
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ExpectedWarmupDataSpan = TimeSpan.FromHours(6.5);
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Sma = SMA("SPY", 2);
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}
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public override void OnData(Slice slice)
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{
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if (Sma.Samples <= _previousSampleCount)
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{
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throw new RegressionTestException("Indicator was not updated!");
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}
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_previousSampleCount = Sma.Samples;
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var tradeBars = slice.Get<TradeBar>();
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tradeBars.TryGetValue("SPY", out var trade);
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var quoteBars = slice.Get<QuoteBar>();
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quoteBars.TryGetValue("SPY", out var quote);
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var expectedPeriod = ExpectedDataSpan;
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if (Time <= StartDate)
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{
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expectedPeriod = ExpectedWarmupDataSpan;
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if (trade != null && trade.IsFillForward || quote != null && quote.IsFillForward)
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{
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throw new RegressionTestException("Unexpected fill forwarded data!");
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}
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}
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if (expectedPeriod == TimeSpan.FromHours(6.5))
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{
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// let's assert the data's time are what we expect
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if (trade != null && trade.EndTime.Hour != 16)
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{
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throw new RegressionTestException($"Unexpected data end time! {trade.EndTime}");
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}
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if (quote != null && quote.EndTime.Hour != 16)
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{
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throw new RegressionTestException($"Unexpected data end time! {quote.EndTime}");
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}
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}
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else
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{
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// let's assert the data's time are what we expect
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if (trade != null && trade.EndTime.Ticks % expectedPeriod.Ticks != 0)
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{
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throw new RegressionTestException($"Unexpected data end time! {trade.EndTime}");
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}
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if (quote != null && quote.EndTime.Ticks % expectedPeriod.Ticks != 0)
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{
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throw new RegressionTestException($"Unexpected data end time! {quote.EndTime}");
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}
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}
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if (trade != null)
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{
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_warmedUpTradeBars |= IsWarmingUp;
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if (trade.Period != expectedPeriod)
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{
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throw new RegressionTestException($"Unexpected period for trade data point {trade.Period} expected {expectedPeriod}. IsWarmingUp: {IsWarmingUp}");
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}
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}
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if (quote != null)
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{
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_warmedUpQuoteBars |= IsWarmingUp;
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if (quote.Period != expectedPeriod)
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{
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throw new RegressionTestException($"Unexpected period for quote data point {quote.Period} expected {expectedPeriod}. IsWarmingUp: {IsWarmingUp}");
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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 (!_warmedUpTradeBars)
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{
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throw new RegressionTestException("Did not assert data during warmup!");
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}
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if (ExpectedWarmupDataSpan == TimeSpan.FromHours(6.5))
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{
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if (_warmedUpQuoteBars)
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{
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throw new RegressionTestException("We should of not gotten any quote bar during warmup for daily resolution!");
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}
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}
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else if (!_warmedUpQuoteBars)
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{
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throw new RegressionTestException("Did not assert data during warmup!");
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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 };
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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 virtual long DataPoints => 36;
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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 virtual 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 virtual 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", "0"},
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{"Tracking Error", "0"},
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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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