Files
quantconnect--lean/Algorithm.CSharp/ProcessSplitSymbolsRegressionAlgorithm.cs
T
2020-04-06 10:32:59 -03:00

121 lines
4.9 KiB
C#

/*
* 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.Data;
using QuantConnect.Interfaces;
using QuantConnect.Securities;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// This test algorithm reproduces GH issue 2848 where an exception is thrown
/// in the AlgorithmManager.ProcessSplitSymbols when removing the equity having a split
/// </summary>
public class ProcessSplitSymbolsRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private Security _aapl;
private Security _goog;
/// <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()
{
SetStartDate(2014, 06, 05); //Set Start Date
SetEndDate(2014, 06, 09); //Set End Date
SetCash(100000); //Set Strategy Cash
_aapl = AddEquity("AAPL", Resolution.Daily);
_goog = AddEquity("GOOG", Resolution.Daily);
}
/// <summary>
/// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
/// </summary>
/// <param name="data">Slice object keyed by symbol containing the stock data</param>
public override void OnData(Slice data)
{
if (data.Time == new DateTime(2014, 06, 06))
{
RemoveSecurity(_aapl.Symbol);
}
if (!Portfolio.Invested)
{
SetHoldings(_goog.Symbol, 1);
Debug("Purchased Stock");
}
}
/// <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 };
/// <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", "716.979%"},
{"Drawdown", "0%"},
{"Expectancy", "0"},
{"Net Profit", "2.919%"},
{"Sharpe Ratio", "51.393"},
{"Probabilistic Sharpe Ratio", "0%"},
{"Loss Rate", "0%"},
{"Win Rate", "0%"},
{"Profit-Loss Ratio", "0"},
{"Alpha", "4.403"},
{"Beta", "1.691"},
{"Annual Standard Deviation", "0.1"},
{"Annual Variance", "0.01"},
{"Information Ratio", "56.522"},
{"Tracking Error", "0.083"},
{"Treynor Ratio", "3.045"},
{"Total Fees", "$1.00"},
{"Fitness Score", "0.245"},
{"Kelly Criterion Estimate", "0"},
{"Kelly Criterion Probability Value", "0"},
{"Sortino Ratio", "79228162514264337593543950335"},
{"Return Over Maximum Drawdown", "79228162514264337593543950335"},
{"Portfolio Turnover", "0.245"},
{"Total Insights Generated", "0"},
{"Total Insights Closed", "0"},
{"Total Insights Analysis Completed", "0"},
{"Long Insight Count", "0"},
{"Short Insight Count", "0"},
{"Long/Short Ratio", "100%"},
{"Estimated Monthly Alpha Value", "$0"},
{"Total Accumulated Estimated Alpha Value", "$0"},
{"Mean Population Estimated Insight Value", "$0"},
{"Mean Population Direction", "0%"},
{"Mean Population Magnitude", "0%"},
{"Rolling Averaged Population Direction", "0%"},
{"Rolling Averaged Population Magnitude", "0%"},
{"OrderListHash", "995235961"}
};
}
}