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* Implement a prototype of the maximum recovery time function. * Add unit test skeletons. * Add failing test * Issue #4581: Implement MaxDrawdownRecoveryTime. * Issue 4581: Add DTO for Drawdown Percentage, Drawdown Enddate, and High Value * Issue 4581: Fix bgu for when lDrawdowns list is empty. * Issue 4581: Change names of tests. Change name of file. * Issue 4581: Make adjustements to flow of adding drawdowns to lDrawdowns. * Issue 4581: Add multiple unit tests. * Issue #4581: Change name of unit test * Issue #4581: Add to PerformanceMetrics * Issue #4581: Add Maximum Drawdown Recovery to PortolioStatistics class. * Issue #4581: Add to portolfio statistics class. * Issue #4581: Add to statistics builder. * Issue #4581: Add report key. * Case #4581: Convert to decimal. * Issue #4581: Correct comment. * Issue #4581: Correct performance metrics view model string. * Case #4581: Correct statistics builder view model string..again. * Issue #4581: Placed DradownDradownDateHighValueDTO at the end of the file for simpler diff. * Issue #4581: Add 2 new tests. * Issue #4581: Change algorithm so that when multiple maximum drawdowns occur, the longest of all recoveries is reported. * Issue #4581: Add unit test. * Issue #4581: Remove reportkey. Change dto name. * Issue #4581: Change summary. * Issue #4581: Change comment. * Add max drawdown recovery calculation with unit tests * Update regression algorithms with the new metric * Solve review comments * Update regression algorithms * Add TryGet to safely get the key: MaximumDrawdownRecovery * Ignore MaximumDrawdownRecovery metric in OptimizationBacktest Json * Revert changes in Messaging * Update regression algorithms * Add test case: TakesLongestRecoveryAmongMultipleDrawdowns * Use integer days for MaximumDrawdownRecovery * Add MaximumDrawdownRecoveryReportElement * Use more explicit names * Rename files and variables for consistency * Update regression algorithms --------- Co-authored-by: Alain Schaerer <aschaerer@pcatg.com>
155 lines
5.9 KiB
C#
155 lines
5.9 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 QuantConnect.Data;
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using QuantConnect.Orders;
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using System.Globalization;
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using QuantConnect.Interfaces;
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using QuantConnect.Data.Market;
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using System.Collections.Generic;
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using QuantConnect.Securities.Future;
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using Futures = QuantConnect.Securities.Futures;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Regression algorithm asserting that the new symbol, on a security changed event,
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/// is added to the securities collection and is tradable.
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/// This specific algorithm tests the manual rollover with the symbol changed event
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/// that is received in the slice in <see cref="OnData(Slice)"/>.
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/// </summary>
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public class ManualContinuousFuturesPositionRolloverRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private Future _continuousContract;
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public override void Initialize()
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{
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SetStartDate(2013, 7, 1);
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SetEndDate(2014, 1, 1);
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_continuousContract = AddFuture(Futures.Indices.SP500EMini,
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dataNormalizationMode: DataNormalizationMode.BackwardsRatio,
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dataMappingMode: DataMappingMode.LastTradingDay,
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contractDepthOffset: 0
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);
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}
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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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Order(_continuousContract.Mapped, 1);
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}
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else
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{
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ManualPositionsRollover(slice.SymbolChangedEvents);
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}
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}
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protected void ManualPositionsRollover(SymbolChangedEvents symbolChangedEvents)
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{
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foreach (var changedEvent in symbolChangedEvents.Values)
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{
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Debug($"{Time} - SymbolChanged event: {changedEvent}");
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// This access will throw if any of the symbols are not in the securities collection
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var oldSecurity = Securities[changedEvent.OldSymbol];
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var newSecurity = Securities[changedEvent.NewSymbol];
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if (!oldSecurity.Invested) continue;
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// Rolling over: liquidate any position of the old mapped contract and switch to the newly mapped contract
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var quantity = oldSecurity.Holdings.Quantity;
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var tag = $"Rollover - Symbol changed at {Time.ToString(CultureInfo.GetCultureInfo("en-US"))}: {changedEvent.OldSymbol} -> {changedEvent.NewSymbol}";
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Liquidate(symbol: oldSecurity.Symbol, tag: tag);
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Order(newSecurity.Symbol, quantity, tag: tag);
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}
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}
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public override void OnOrderEvent(OrderEvent orderEvent)
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{
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Debug($"{orderEvent}");
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}
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public override void OnEndOfAlgorithm()
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{
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if (Transactions.OrdersCount < 3)
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{
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throw new RegressionTestException("Expected at least 3 orders.");
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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 long DataPoints => 162575;
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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", "3"},
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{"Average Win", "7.01%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "15.617%"},
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{"Drawdown", "1.600%"},
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{"Expectancy", "0"},
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{"Start Equity", "100000"},
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{"End Equity", "107578.9"},
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{"Net Profit", "7.579%"},
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{"Sharpe Ratio", "1.706"},
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{"Sortino Ratio", "0.919"},
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{"Probabilistic Sharpe Ratio", "88.924%"},
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{"Loss Rate", "0%"},
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{"Win Rate", "100%"},
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{"Profit-Loss Ratio", "0"},
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{"Alpha", "0.08"},
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{"Beta", "0.094"},
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{"Annual Standard Deviation", "0.059"},
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{"Annual Variance", "0.003"},
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{"Information Ratio", "-1.246"},
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{"Tracking Error", "0.094"},
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{"Treynor Ratio", "1.06"},
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{"Total Fees", "$6.45"},
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{"Estimated Strategy Capacity", "$2900000000.00"},
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{"Lowest Capacity Asset", "ES VMKLFZIH2MTD"},
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{"Portfolio Turnover", "1.37%"},
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{"Drawdown Recovery", "16"},
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{"OrderListHash", "7591ea8b91c4aa958b305555fea96862"}
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};
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}
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}
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