/*

* 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.Collections.Generic;

using QuantConnect.Data.Market;

using QuantConnect.Interfaces;

namespace QuantConnect.Algorithm.CSharp

{

/// <summary>

/// Regression test for consistency of hour data over a reverse split event in US equities.

/// </summary>

/// <meta name="tag" content="using data" />

/// <meta name="tag" content="regression test" />

public class HourReverseSplitRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition

{

private Symbol _symbol;

public override void Initialize()

{

SetStartDate(2013, 11, 7);

SetEndDate(2013, 11, 8);

SetCash(100000);

SetBenchmark(x => 0);

_symbol = AddEquity("VXX", Resolution.Hour).Symbol;

}

public void OnData(TradeBars tradeBars)

{

TradeBar bar;

if (!tradeBars.TryGetValue(_symbol, out bar)) return;

if (!Portfolio.Invested && Time.Date == EndDate.Date)

{

Buy(_symbol, 1);

}

}

/// <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>

/// 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 Trades", "1"},

{"Average Win", "0%"},

{"Average Loss", "0%"},

{"Compounding Annual Return", "-5.029%"},

{"Drawdown", "0.000%"},

{"Expectancy", "0"},

{"Net Profit", "-0.024%"},

{"Sharpe Ratio", "-11.225"},

{"Loss Rate", "0%"},

{"Win Rate", "0%"},

{"Profit-Loss Ratio", "0"},

{"Alpha", "0"},

{"Beta", "0"},

{"Annual Standard Deviation", "0.003"},

{"Annual Variance", "0"},

{"Information Ratio", "0"},

{"Tracking Error", "0"},

{"Treynor Ratio", "0"},

{"Total Fees", "$1.00"}

};

}

}