wehub-resource-sync
240 lines
10 KiB
C#
240 lines
10 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 System.Collections.Generic;
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using System.Linq;
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using QuantConnect.Data;
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using QuantConnect.Data.Consolidators;
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using QuantConnect.Data.Market;
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using QuantConnect.Indicators;
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using QuantConnect.Interfaces;
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using QuantConnect.Securities;
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using QuantConnect.Securities.Future;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Regression algorithm reproducing data type bugs in the Consolidate API. Related to GH 4205.
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/// </summary>
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public class ConsolidateRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private List<int> _consolidationCounts;
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private List<int> _expectedConsolidationCounts;
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private List<SimpleMovingAverage> _smas;
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private List<DateTime> _lastSmaUpdates;
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private int _customDataConsolidatorCount;
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private Future _future;
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/// <summary>
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/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
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/// </summary>
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public override void Initialize()
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{
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SetStartDate(2020, 01, 05);
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SetEndDate(2020, 01, 20);
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var SP500 = QuantConnect.Symbol.Create(Futures.Indices.SP500EMini, SecurityType.Future, Market.CME);
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var symbol = FuturesChain(SP500).OrderBy(x => x.Symbol.ID.Date).First();
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_future = AddFutureContract(symbol);
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var tradableDatesCount = QuantConnect.Time.EachTradeableDayInTimeZone(_future.Exchange.Hours,
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StartDate,
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EndDate,
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_future.Exchange.TimeZone,
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false).Count();
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_expectedConsolidationCounts = new(10);
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Consolidate<QuoteBar>(symbol, time => new CalendarInfo(time.RoundDown(TimeSpan.FromDays(1)), TimeSpan.FromDays(1)),
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bar => UpdateQuoteBar(bar, 0));
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// The consolidator will respect the full 1 day bar span and will not consolidate the last tradable date,
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// since scan will not be called at 202/01/21 12am
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_expectedConsolidationCounts.Add(tradableDatesCount - 1);
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Consolidate<QuoteBar>(symbol, time => new CalendarInfo(time.RoundDown(TimeSpan.FromDays(1)), TimeSpan.FromDays(1)),
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TickType.Quote, bar => UpdateQuoteBar(bar, 1));
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_expectedConsolidationCounts.Add(tradableDatesCount - 1);
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Consolidate<QuoteBar>(symbol, TimeSpan.FromDays(1), bar => UpdateQuoteBar(bar, 2));
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_expectedConsolidationCounts.Add(tradableDatesCount - 1);
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Consolidate(symbol, Resolution.Daily, TickType.Quote, (Action<QuoteBar>)(bar => UpdateQuoteBar(bar, 3)));
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_expectedConsolidationCounts.Add(tradableDatesCount);
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Consolidate(symbol, TimeSpan.FromDays(1), bar => UpdateTradeBar(bar, 4));
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_expectedConsolidationCounts.Add(tradableDatesCount - 1);
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Consolidate<TradeBar>(symbol, TimeSpan.FromDays(1), bar => UpdateTradeBar(bar, 5));
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_expectedConsolidationCounts.Add(tradableDatesCount - 1);
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// Test using abstract T types, through defining a 'BaseData' handler
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Consolidate(symbol, Resolution.Daily, null, (Action<BaseData>)(bar => UpdateBar(bar, 6)));
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_expectedConsolidationCounts.Add(tradableDatesCount);
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Consolidate(symbol, TimeSpan.FromDays(1), null, (Action<BaseData>)(bar => UpdateBar(bar, 7)));
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_expectedConsolidationCounts.Add(tradableDatesCount - 1);
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Consolidate(symbol, TimeSpan.FromDays(1), (Action<BaseData>)(bar => UpdateBar(bar, 8)));
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_expectedConsolidationCounts.Add(tradableDatesCount - 1);
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_consolidationCounts = Enumerable.Repeat(0, _expectedConsolidationCounts.Count).ToList();
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_smas = _consolidationCounts.Select(_ => new SimpleMovingAverage(10)).ToList();
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_lastSmaUpdates = _consolidationCounts.Select(_ => DateTime.MinValue).ToList();
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// custom data
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var customSecurity = AddData<CustomDataRegressionAlgorithm.Bitcoin>("BTC", Resolution.Minute);
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Consolidate<TradeBar>(customSecurity.Symbol, TimeSpan.FromDays(1), bar => _customDataConsolidatorCount++);
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try
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{
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Consolidate<QuoteBar>(customSecurity.Symbol, TimeSpan.FromDays(1), bar => { UpdateQuoteBar(bar, -1); });
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throw new RegressionTestException($"Expected {nameof(ArgumentException)} to be thrown");
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}
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catch (ArgumentException)
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{
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// will try to use BaseDataConsolidator for which input is TradeBars not QuoteBars
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}
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}
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private void UpdateBar(BaseData tradeBar, int position)
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{
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if (!(tradeBar is TradeBar))
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{
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throw new RegressionTestException("Expected a TradeBar");
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}
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_consolidationCounts[position]++;
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_smas[position].Update(tradeBar.EndTime, tradeBar.Value);
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_lastSmaUpdates[position] = tradeBar.EndTime;
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}
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private void UpdateTradeBar(TradeBar tradeBar, int position)
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{
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_consolidationCounts[position]++;
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_smas[position].Update(tradeBar.EndTime, tradeBar.High);
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_lastSmaUpdates[position] = tradeBar.EndTime;
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}
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private void UpdateQuoteBar(QuoteBar quoteBar, int position)
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{
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_consolidationCounts[position]++;
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_smas[position].Update(quoteBar.EndTime, quoteBar.High);
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_lastSmaUpdates[position] = quoteBar.EndTime;
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}
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public override void OnEndOfAlgorithm()
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{
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for (var i = 0; i < _consolidationCounts.Count; i++)
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{
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var consolidationCount = _consolidationCounts[i];
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var expectedConsolidationCount = _expectedConsolidationCounts[i];
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if (consolidationCount != expectedConsolidationCount)
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{
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throw new RegressionTestException($"Expected {expectedConsolidationCount} consolidations for consolidator {i} but received {consolidationCount}");
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}
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}
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if (_customDataConsolidatorCount == 0)
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{
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throw new RegressionTestException($"Unexpected custom data consolidation count: {_customDataConsolidatorCount}");
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}
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for (var i = 0; i < _smas.Count; i++)
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{
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if (_smas[i].Samples != _expectedConsolidationCounts[i])
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{
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throw new RegressionTestException($"Expected {_expectedConsolidationCounts} samples in each SMA but found {_smas[i].Samples} in SMA in index {i}");
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}
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if (_smas[i].Current.Time != _lastSmaUpdates[i])
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{
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throw new RegressionTestException($"Expected SMA in index {i} to have been last updated at {_lastSmaUpdates[i]} but was {_smas[i].Current.Time}");
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}
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}
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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 && _future.HasData)
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{
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SetHoldings(_future.Symbol, 0.5);
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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 => 14228;
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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 => 1;
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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", "665.524%"},
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{"Drawdown", "1.500%"},
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{"Expectancy", "0"},
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{"Start Equity", "100000"},
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{"End Equity", "109332.4"},
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{"Net Profit", "9.332%"},
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{"Sharpe Ratio", "9.805"},
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{"Sortino Ratio", "0"},
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{"Probabilistic Sharpe Ratio", "92.778%"},
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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", "3.164"},
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{"Beta", "0.957"},
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{"Annual Standard Deviation", "0.383"},
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{"Annual Variance", "0.146"},
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{"Information Ratio", "8.29"},
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{"Tracking Error", "0.379"},
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{"Treynor Ratio", "3.917"},
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{"Total Fees", "$15.05"},
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{"Estimated Strategy Capacity", "$2100000000.00"},
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{"Lowest Capacity Asset", "ES XCZJLC9NOB29"},
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{"Portfolio Turnover", "64.34%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "d814db6d5a9c97ee6de477ea06cd3834"}
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};
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}
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}
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