Michael Handschuh
8402b6f01e
It's important that we keep the factor files consistent with respect to the date that they were generated. This enables us to run the regression algorithms in the cloud and get the same results by using the factor files from the correct date.
87 lines
3.5 KiB
C#
87 lines
3.5 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.Collections.Generic;
|
|
using QuantConnect.Data;
|
|
|
|
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="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");
|
|
}
|
|
}
|
|
|
|
/// <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", "247.299%"},
|
|
{"Drawdown", "1.100%"},
|
|
{"Expectancy", "0"},
|
|
{"Net Profit", "3.470%"},
|
|
{"Sharpe Ratio", "6.013"},
|
|
{"Loss Rate", "0%"},
|
|
{"Win Rate", "0%"},
|
|
{"Profit-Loss Ratio", "0"},
|
|
{"Alpha", "0.731"},
|
|
{"Beta", "15.45"},
|
|
{"Annual Standard Deviation", "0.161"},
|
|
{"Annual Variance", "0.026"},
|
|
{"Information Ratio", "5.92"},
|
|
{"Tracking Error", "0.161"},
|
|
{"Treynor Ratio", "0.063"},
|
|
{"Total Fees", "$3.27"}
|
|
};
|
|
}
|
|
}
|