chore: import upstream snapshot with attribution
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/*
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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 QuantConnect.Data;
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using QuantConnect.Interfaces;
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using System.Collections.Generic;
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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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/// Regression algorithm making sure the securities cache is reset correctly once it's removed from the algorithm
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/// </summary>
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public class AddRemoveSecurityCacheRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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/// <summary>
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/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
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/// </summary>
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public override void Initialize()
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{
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SetStartDate(2013, 10, 07); //Set Start Date
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SetEndDate(2013, 10, 11); //Set End Date
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SetCash(100000); //Set Strategy Cash
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AddEquity("SPY", Resolution.Minute, extendedMarketHours: true);
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}
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/// <summary>
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/// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
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/// </summary>
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/// <param name="slice">Slice object keyed by symbol containing the stock data</param>
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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("SPY", 1);
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}
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if (Time.Day == 11)
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{
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return;
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}
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if (!ActiveSecurities.ContainsKey("AIG"))
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{
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var aig = AddEquity("AIG", Resolution.Minute);
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var ticket = MarketOrder("AIG", 1);
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if (ticket.Status != OrderStatus.Invalid || aig.HasData || aig.Price != 0)
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{
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throw new RegressionTestException("Expected order to always be invalid because there is no data yet!");
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}
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}
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else
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{
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RemoveSecurity("AIG");
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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; } = true;
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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 => 15042;
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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", "19"},
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{"Average Win", "0%"},
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{"Average Loss", "0.00%"},
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{"Compounding Annual Return", "271.720%"},
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{"Drawdown", "2.500%"},
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{"Expectancy", "-1"},
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{"Start Equity", "100000"},
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{"End Equity", "101753.84"},
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{"Net Profit", "1.754%"},
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{"Sharpe Ratio", "11.954"},
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{"Sortino Ratio", "29.606"},
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{"Probabilistic Sharpe Ratio", "73.973%"},
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{"Loss Rate", "100%"},
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{"Win Rate", "0%"},
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{"Profit-Loss Ratio", "0"},
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{"Alpha", "0.616"},
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{"Beta", "0.81"},
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{"Annual Standard Deviation", "0.185"},
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{"Annual Variance", "0.034"},
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{"Information Ratio", "3.961"},
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{"Tracking Error", "0.061"},
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{"Treynor Ratio", "2.737"},
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{"Total Fees", "$21.45"},
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{"Estimated Strategy Capacity", "$830000.00"},
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{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
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{"Portfolio Turnover", "20.49%"},
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{"Drawdown Recovery", "2"},
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{"OrderListHash", "6ebe462373e2ecc22de8eb2fe114d704"}
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};
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}
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}
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