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AddRiskManagementAlgorithm.cs
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106 lines (96 loc) · 4.48 KB
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/*
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
using System;
using System.Collections.Generic;
using QuantConnect.Algorithm.Framework.Alphas;
using QuantConnect.Algorithm.Framework.Execution;
using QuantConnect.Algorithm.Framework.Portfolio;
using QuantConnect.Algorithm.Framework.Risk;
using QuantConnect.Algorithm.Framework.Selection;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Test algorithm using <see cref="QCAlgorithm.AddRiskManagement(IRiskManagementModel)"/>
/// </summary>
public class AddRiskManagementAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
/// <summary>
/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
/// </summary>
public override void Initialize()
{
UniverseSettings.Resolution = Resolution.Minute;
SetStartDate(2013, 10, 07); //Set Start Date
SetEndDate(2013, 10, 11); //Set End Date
SetCash(100000); //Set Strategy Cash
SetUniverseSelection(new ManualUniverseSelectionModel(QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA)));
SetAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromMinutes(20), 0.025, null));
SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
SetExecution(new ImmediateExecutionModel());
AddRiskManagement(new MaximumDrawdownPercentPortfolio(0.02m));
AddRiskManagement(new MaximumUnrealizedProfitPercentPerSecurity(0.01m));
}
/// <summary>
/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
/// </summary>
public bool CanRunLocally { get; } = true;
/// <summary>
/// This is used by the regression test system to indicate which languages this algorithm is written in.
/// </summary>
public Language[] Languages { get; } = { Language.CSharp, Language.Python };
/// <summary>
/// Data Points count of all timeslices of algorithm
/// </summary>
public long DataPoints => 3943;
/// <summary>
/// Data Points count of the algorithm history
/// </summary>
public int AlgorithmHistoryDataPoints => 0;
/// <summary>
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
/// </summary>
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
{
{"Total Orders", "3"},
{"Average Win", "1.02%"},
{"Average Loss", "0%"},
{"Compounding Annual Return", "296.066%"},
{"Drawdown", "2.200%"},
{"Expectancy", "0"},
{"Start Equity", "100000"},
{"End Equity", "101775.37"},
{"Net Profit", "1.775%"},
{"Sharpe Ratio", "9.34"},
{"Sortino Ratio", "0"},
{"Probabilistic Sharpe Ratio", "68.302%"},
{"Loss Rate", "0%"},
{"Win Rate", "100%"},
{"Profit-Loss Ratio", "0"},
{"Alpha", "0.106"},
{"Beta", "1.021"},
{"Annual Standard Deviation", "0.227"},
{"Annual Variance", "0.052"},
{"Information Ratio", "25.083"},
{"Tracking Error", "0.006"},
{"Treynor Ratio", "2.079"},
{"Total Fees", "$10.33"},
{"Estimated Strategy Capacity", "$38000000.00"},
{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
{"Portfolio Turnover", "59.74%"},
{"OrderListHash", "b5a7935f37d94eb20f6bcd88578dbaee"}
};
}
}