wehub-resource-sync
128 lines
4.9 KiB
C#
128 lines
4.9 KiB
C#
/*
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* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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using System.Collections.Generic;
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using QuantConnect.Data;
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using QuantConnect.Interfaces;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Tests that splits do not cause the algorithm to report capacity estimates
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/// above or below the actual capacity due to splits. The stock HTGM is illiquid,
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/// trading only $1.2 Million per day on average with sparse trade frequencies.
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/// </summary>
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public class SplitTestingStrategy : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private Symbol _htgm;
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public override void Initialize()
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{
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SetStartDate(2020, 11, 1);
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SetEndDate(2020, 12, 5);
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SetCash(100000);
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var htgm = AddEquity("HTGM", Resolution.Hour);
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htgm.SetDataNormalizationMode(DataNormalizationMode.Raw);
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_htgm = htgm.Symbol;
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}
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public override void OnData(Slice slice)
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{
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if (!Portfolio.Invested)
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{
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SetHoldings(_htgm, 1);
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}
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else
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{
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SetHoldings(_htgm, -1);
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}
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}
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/// <summary>
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/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
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/// </summary>
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public bool CanRunLocally { get; } = false;
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/// <summary>
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/// This is used by the regression test system to indicate which languages this algorithm is written in.
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/// </summary>
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public List<Language> Languages { get; } = new() { Language.CSharp };
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/// <summary>
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/// Data Points count of all timeslices of algorithm
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/// </summary>
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public long DataPoints => 0;
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/// </summary>
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/// Data Points count of the algorithm history
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/// </summary>
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public int AlgorithmHistoryDataPoints => 0;
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/// <summary>
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/// Final status of the algorithm
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/// </summary>
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public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;
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/// <summary>
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/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
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/// </summary>
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public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
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{
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{"Total Orders", "162"},
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{"Average Win", "0.10%"},
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{"Average Loss", "-0.35%"},
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{"Compounding Annual Return", "-94.432%"},
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{"Drawdown", "30.400%"},
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{"Expectancy", "-0.564"},
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{"Net Profit", "-23.412%"},
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{"Sharpe Ratio", "-1.041"},
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{"Probabilistic Sharpe Ratio", "12.971%"},
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{"Loss Rate", "66%"},
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{"Win Rate", "34%"},
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{"Profit-Loss Ratio", "0.29"},
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{"Alpha", "-4.827"},
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{"Beta", "1.43"},
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{"Annual Standard Deviation", "0.876"},
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{"Annual Variance", "0.767"},
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{"Information Ratio", "-4.288"},
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{"Tracking Error", "0.851"},
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{"Treynor Ratio", "-0.637"},
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{"Total Fees", "$2655.91"},
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{"Estimated Strategy Capacity", "$11000.00"},
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{"Fitness Score", "0.052"},
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{"Kelly Criterion Estimate", "0"},
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{"Kelly Criterion Probability Value", "0"},
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{"Sortino Ratio", "-2.2"},
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{"Return Over Maximum Drawdown", "-3.481"},
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{"Portfolio Turnover", "0.307"},
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{"Total Insights Generated", "0"},
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{"Total Insights Closed", "0"},
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{"Total Insights Analysis Completed", "0"},
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{"Long Insight Count", "0"},
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{"Short Insight Count", "0"},
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{"Long/Short Ratio", "100%"},
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{"Estimated Monthly Alpha Value", "$0"},
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{"Total Accumulated Estimated Alpha Value", "$0"},
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{"Mean Population Estimated Insight Value", "$0"},
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{"Mean Population Direction", "0%"},
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{"Mean Population Magnitude", "0%"},
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{"Rolling Averaged Population Direction", "0%"},
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{"Rolling Averaged Population Magnitude", "0%"},
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{"OrderListHash", "54f571c11525656e9b383e235e77002e"}
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};
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}
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}
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