wehub-resource-sync
125 lines
4.3 KiB
C#
125 lines
4.3 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;
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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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using QuantConnect.Orders;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// This regression test algorithm reproduces issue reported in GB issue https://github.com/QuantConnect/Lean/issues/2655
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/// fixed in PR https://github.com/QuantConnect/Lean/pull/2659
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/// </summary>
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public class DailyResolutionSplitRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private Symbol _symbol;
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public override void Initialize()
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{
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SetStartDate(2018, 2, 13); //Set Start Date
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SetEndDate(2018, 06, 01); //Set End Date
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SetCash(100000); //Set Strategy Cash
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_symbol = AddEquity("UPRO", Resolution.Daily).Symbol;
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}
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public override void OnData(Slice slice)
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{
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if (Time.Date == new DateTime(2018, 05, 22).Date)
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{
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MarketOrder(_symbol, 100);
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}
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if (Time.Date == new DateTime(2018, 05, 23).Date)
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{
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MarketOrder(_symbol, 100);
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}
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if (Time.Date == new DateTime(2018, 05, 24).Date)
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{
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MarketOrder(_symbol, 100);
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}
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if (Time.Date == new DateTime(2018, 05, 25).Date)
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{
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MarketOrder(_symbol, 100);
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}
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if (Time.Date == new DateTime(2018, 05, 29).Date)
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{
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Liquidate();
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}
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}
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public override void OnOrderEvent(OrderEvent orderEvent)
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{
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Log($"{orderEvent}");
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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", "4"},
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{"Average Win", "0%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "0.520%"},
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{"Drawdown", "0.800%"},
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{"Expectancy", "0"},
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{"Net Profit", "0.155%"},
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{"Sharpe Ratio", "0.242"},
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{"Loss Rate", "0%"},
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{"Win Rate", "0%"},
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{"Profit-Loss Ratio", "0"},
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{"Alpha", "0.118"},
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{"Beta", "-5.794"},
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{"Annual Standard Deviation", "0.022"},
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{"Annual Variance", "0"},
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{"Information Ratio", "-0.644"},
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{"Tracking Error", "0.022"},
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{"Treynor Ratio", "-0.001"},
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{"Total Fees", "$4.00"}
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};
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}
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}
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