/*
* 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
{
///
/// Test algorithm using
///
public class AddRiskManagementAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
///
/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
///
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));
}
///
/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
///
public bool CanRunLocally { get; } = true;
///
/// This is used by the regression test system to indicate which languages this algorithm is written in.
///
public Language[] Languages { get; } = { Language.CSharp, Language.Python };
///
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
///
public Dictionary ExpectedStatistics => new Dictionary
{
{"Total Trades", "3"},
{"Average Win", "1.02%"},
{"Average Loss", "0%"},
{"Compounding Annual Return", "289.117%"},
{"Drawdown", "2.200%"},
{"Expectancy", "0"},
{"Net Profit", "1.752%"},
{"Sharpe Ratio", "4.948"},
{"Probabilistic Sharpe Ratio", "67.707%"},
{"Loss Rate", "0%"},
{"Win Rate", "100%"},
{"Profit-Loss Ratio", "0"},
{"Alpha", "0.028"},
{"Beta", "1.023"},
{"Annual Standard Deviation", "0.225"},
{"Annual Variance", "0.051"},
{"Information Ratio", "8.21"},
{"Tracking Error", "0.006"},
{"Treynor Ratio", "1.089"},
{"Total Fees", "$9.77"},
{"Fitness Score", "0.747"},
{"Kelly Criterion Estimate", "39.573"},
{"Kelly Criterion Probability Value", "0.226"},
{"Sortino Ratio", "79228162514264337593543950335"},
{"Return Over Maximum Drawdown", "108.728"},
{"Portfolio Turnover", "0.747"},
{"Total Insights Generated", "100"},
{"Total Insights Closed", "99"},
{"Total Insights Analysis Completed", "99"},
{"Long Insight Count", "100"},
{"Short Insight Count", "0"},
{"Long/Short Ratio", "100%"},
{"Estimated Monthly Alpha Value", "$158418.3850"},
{"Total Accumulated Estimated Alpha Value", "$25522.9620"},
{"Mean Population Estimated Insight Value", "$257.8077"},
{"Mean Population Direction", "54.5455%"},
{"Mean Population Magnitude", "54.5455%"},
{"Rolling Averaged Population Direction", "59.8056%"},
{"Rolling Averaged Population Magnitude", "59.8056%"},
{"OrderListHash", "1162594724"}
};
}
}