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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.Data;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Shows how to set a custom benchmark for you algorithms
/// </summary>
/// <meta name="tag" content="using data" />
/// <meta name="tag" content="benchmarks" />
public class CustomBenchmarkAlgorithm : 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()
{
SetStartDate(2013, 10, 07); //Set Start Date
SetEndDate(2013, 10, 11); //Set End Date
SetCash(100000); //Set Strategy Cash
// Find more symbols here: http://quantconnect.com/data
AddSecurity(SecurityType.Equity, "SPY", Resolution.Second);
SetBenchmark("AAPL");
}
/// <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 (!Portfolio.Invested)
{
SetHoldings("SPY", 1);
Debug("Purchased Stock");
}
Symbol symbol;
if (SymbolCache.TryGetSymbol("AAPL", out symbol))
{
throw new Exception("Benchmark Symbol is not expected to be added to the Symbol cache");
}
}
/// <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", "233.235%"},
{"Drawdown", "2.200%"},
{"Expectancy", "0"},
{"Net Profit", "1.663%"},
{"Sharpe Ratio", "4.076"},
{"Probabilistic Sharpe Ratio", "68.499%"},
{"Loss Rate", "0%"},
{"Win Rate", "0%"},
{"Profit-Loss Ratio", "0"},
{"Alpha", "0.494"},
{"Beta", "0.244"},
{"Annual Standard Deviation", "0.173"},
{"Annual Variance", "0.03"},
{"Information Ratio", "-0.788"},
{"Tracking Error", "0.203"},
{"Treynor Ratio", "2.889"},
{"Total Fees", "$3.26"}
};
}
}