wehub-resource-sync
129 lines
5.1 KiB
C#
129 lines
5.1 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 QuantConnect.Algorithm.Framework.Alphas;
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using QuantConnect.Algorithm.Framework.Execution;
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using QuantConnect.Algorithm.Framework.Portfolio;
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using QuantConnect.Algorithm.Framework.Selection;
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using QuantConnect.Orders;
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using QuantConnect.Interfaces;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Regression algorithm which reproduced GH issue 3759 (performing 26 trades).
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/// </summary>
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public class FreePortfolioValueRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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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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UniverseSettings.Resolution = Resolution.Daily;
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SetStartDate(2007, 10, 1);
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SetEndDate(2018, 2, 1);
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SetCash(1000000);
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UniverseSettings.Leverage = 1;
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SetUniverseSelection(
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new ManualUniverseSelectionModel(QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA))
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);
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SetAlpha(
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new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, QuantConnect.Time.OneDay, 0.025, null)
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);
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SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
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SetExecution(new ImmediateExecutionModel());
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}
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public override void OnEndOfAlgorithm()
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{
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var freePortfolioValue = Portfolio.TotalPortfolioValue - Portfolio.TotalPortfolioValueLessFreeBuffer;
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if (freePortfolioValue != Portfolio.TotalPortfolioValue * Settings.FreePortfolioValuePercentage)
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{
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throw new RegressionTestException($"Unexpected FreePortfolioValue value: {freePortfolioValue}");
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}
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}
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public override void OnOrderEvent(OrderEvent orderEvent)
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{
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Debug($"OnOrderEvent: {orderEvent}");
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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 => 20812;
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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 virtual Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
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{
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{"Total Orders", "4"},
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{"Average Win", "0.06%"},
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{"Average Loss", "-0.01%"},
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{"Compounding Annual Return", "8.174%"},
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{"Drawdown", "55.100%"},
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{"Expectancy", "2.639"},
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{"Start Equity", "1000000"},
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{"End Equity", "2254609.41"},
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{"Net Profit", "125.461%"},
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{"Sharpe Ratio", "0.36"},
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{"Sortino Ratio", "0.365"},
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{"Probabilistic Sharpe Ratio", "0.570%"},
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{"Loss Rate", "50%"},
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{"Win Rate", "50%"},
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{"Profit-Loss Ratio", "6.28"},
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{"Alpha", "-0"},
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{"Beta", "0.998"},
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{"Annual Standard Deviation", "0.164"},
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{"Annual Variance", "0.027"},
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{"Information Ratio", "-0.192"},
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{"Tracking Error", "0.001"},
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{"Treynor Ratio", "0.059"},
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{"Total Fees", "$45.46"},
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{"Estimated Strategy Capacity", "$480000000.00"},
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{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
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{"Portfolio Turnover", "0.03%"},
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{"Drawdown Recovery", "1772"},
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{"OrderListHash", "bc1c4bb38b3c1c39eb3d1aba5a671bba"}
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};
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}
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}
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