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

122 lines
5.0 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.Data.Consolidators;
using QuantConnect.Indicators;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// This algorithm reproduces GH issue 2404, exception: `This is a forward only indicator`
/// </summary>
public class WarmupIndicatorRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private Symbol _spy;
/// <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, 11, 1);
SetEndDate(2013, 12, 10); //Set End Date
SetWarmup(TimeSpan.FromDays(30));
_spy = AddEquity("SPY", Resolution.Daily).Symbol;
var renkoConsolidator = new RenkoConsolidator(2m);
renkoConsolidator.DataConsolidated += (sender, consolidated) =>
{
if (IsWarmingUp) return;
if (!Portfolio.Invested)
{
SetHoldings(_spy, 1.0);
}
Log($"CLOSE - {consolidated.Time:o} - {consolidated.Open} {consolidated.Close}");
};
var sma = new SimpleMovingAverage("SMA", 3);
RegisterIndicator(_spy, sma, renkoConsolidator);
}
/// <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)
{
}
/// <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", "21.640%"},
{"Drawdown", "1.200%"},
{"Expectancy", "0"},
{"Net Profit", "2.149%"},
{"Sharpe Ratio", "3.2"},
{"Probabilistic Sharpe Ratio", "79.148%"},
{"Loss Rate", "0%"},
{"Win Rate", "0%"},
{"Profit-Loss Ratio", "0"},
{"Alpha", "0.16"},
{"Beta", "0.057"},
{"Annual Standard Deviation", "0.054"},
{"Annual Variance", "0.003"},
{"Information Ratio", "-0.47"},
{"Tracking Error", "0.089"},
{"Treynor Ratio", "3.035"},
{"Total Fees", "$3.08"},
{"Fitness Score", "0.028"},
{"Kelly Criterion Estimate", "0"},
{"Kelly Criterion Probability Value", "0"},
{"Sortino Ratio", "11.51"},
{"Return Over Maximum Drawdown", "18.205"},
{"Portfolio Turnover", "0.029"},
{"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", "1135438756"}
};
}
}