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BasicTemplateDailyAlgorithm.cs
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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.Collections.Generic;
using QuantConnect.Data;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Demonstration of requesting daily resolution data for US Equities.
/// This is a simple regression test algorithm using a skeleton algorithm and requesting daily data.
/// </summary>
/// <meta name="tag" content="using data" />
public class BasicTemplateDailyAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private Symbol _spy = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
/// <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, 08); //Set Start Date
SetEndDate(2013, 10, 17); //Set End Date
SetCash(100000); //Set Strategy Cash
// Find more symbols here: http://quantconnect.com/data
AddEquity("SPY", 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="slice">Slice object keyed by symbol containing the stock data</param>
public override void OnData(Slice slice)
{
if (!Portfolio.Invested)
{
SetHoldings(_spy, 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 List<Language> Languages { get; } = new() { Language.CSharp, Language.Python };
/// <summary>
/// Data Points count of all timeslices of algorithm
/// </summary>
public long DataPoints => 72;
/// <summary>
/// Data Points count of the algorithm history
/// </summary>
public int AlgorithmHistoryDataPoints => 0;
/// <summary>
/// Final status of the algorithm
/// </summary>
public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;
/// <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", "1"},
{"Average Win", "0%"},
{"Average Loss", "0%"},
{"Compounding Annual Return", "424.375%"},
{"Drawdown", "0.800%"},
{"Expectancy", "0"},
{"Start Equity", "100000"},
{"End Equity", "104486.22"},
{"Net Profit", "4.486%"},
{"Sharpe Ratio", "17.304"},
{"Sortino Ratio", "35.217"},
{"Probabilistic Sharpe Ratio", "96.835%"},
{"Loss Rate", "0%"},
{"Win Rate", "0%"},
{"Profit-Loss Ratio", "0"},
{"Alpha", "-0.249"},
{"Beta", "1.015"},
{"Annual Standard Deviation", "0.141"},
{"Annual Variance", "0.02"},
{"Information Ratio", "-19"},
{"Tracking Error", "0.011"},
{"Treynor Ratio", "2.403"},
{"Total Fees", "$3.49"},
{"Estimated Strategy Capacity", "$1200000000.00"},
{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
{"Portfolio Turnover", "10.01%"},
{"Drawdown Recovery", "1"},
{"OrderListHash", "70f21e930175a2ec9d465b21edc1b6d9"}
};
}
}