wehub-resource-sync
132 lines
4.6 KiB
C#
132 lines
4.6 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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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Checks that the Tick BidPrice and AskPrices are adjusted like Value.
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/// </summary>
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public class EquityTickQuoteAdjustedModeRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private Symbol _ibm;
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private bool _bought;
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private bool _sold;
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public override void Initialize()
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{
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SetStartDate(2013, 10, 7);
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SetEndDate(2013, 10, 11);
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SetCash(100000);
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_ibm = AddEquity("IBM", Resolution.Tick).Symbol;
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}
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public override void OnData(Slice slice)
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{
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if (!slice.Ticks.ContainsKey(_ibm))
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{
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return;
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}
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var security = Securities[_ibm];
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if (!security.HasData)
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{
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return;
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}
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foreach (var tick in slice.Ticks[_ibm])
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{
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if (tick.BidPrice != 0 && !_bought && ((tick.Value - tick.BidPrice) <= 0.05m))
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{
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SetHoldings(_ibm, 1);
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_bought = true;
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return;
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}
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if (tick.AskPrice != 0 && _bought && !_sold && Math.Abs((double)tick.Value - (double)tick.AskPrice) <= 0.05)
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{
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Liquidate(_ibm);
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_sold = true;
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return;
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}
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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 => 694806;
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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", "2"},
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{"Average Win", "0%"},
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{"Average Loss", "-0.12%"},
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{"Compounding Annual Return", "-9.135%"},
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{"Drawdown", "0.100%"},
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{"Expectancy", "-1"},
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{"Start Equity", "100000"},
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{"End Equity", "99877.60"},
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{"Net Profit", "-0.122%"},
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{"Sharpe Ratio", "0"},
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{"Sortino Ratio", "0"},
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{"Probabilistic Sharpe Ratio", "0%"},
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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"},
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{"Beta", "0"},
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{"Annual Standard Deviation", "0"},
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{"Annual Variance", "0"},
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{"Information Ratio", "-8.91"},
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{"Tracking Error", "0.223"},
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{"Treynor Ratio", "0"},
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{"Total Fees", "$7.34"},
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{"Estimated Strategy Capacity", "$0"},
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{"Lowest Capacity Asset", "IBM R735QTJ8XC9X"},
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{"Portfolio Turnover", "39.89%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "d2af4746a4d01ca4d0ce0b0c44f30451"}
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};
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}
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}
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