wehub-resource-sync
124 lines
5.0 KiB
C#
124 lines
5.0 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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*/
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using System.Linq;
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using System.Collections.Generic;
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using QuantConnect.Algorithm.Framework.Alphas;
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using QuantConnect.Algorithm.Framework.Execution;
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using QuantConnect.Algorithm.Framework.Portfolio;
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using QuantConnect.Algorithm.Framework.Selection;
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using QuantConnect.Orders;
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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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/// Regression algorithm for the VolumeWeightedAveragePriceExecutionModel.
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/// This algorithm shows how the execution model works to split up orders and submit them only when
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/// the price is on the favorable side of the intraday VWAP.
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/// </summary>
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public class VolumeWeightedAveragePriceExecutionModelRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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public override void Initialize()
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{
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UniverseSettings.Resolution = Resolution.Minute;
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SetStartDate(2013, 10, 07);
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SetEndDate(2013, 10, 11);
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SetCash(1000000);
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SetUniverseSelection(new ManualUniverseSelectionModel(
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QuantConnect.Symbol.Create("AIG", SecurityType.Equity, Market.USA),
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QuantConnect.Symbol.Create("BAC", SecurityType.Equity, Market.USA),
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QuantConnect.Symbol.Create("IBM", SecurityType.Equity, Market.USA),
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QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA)
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));
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// using hourly rsi to generate more insights
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SetAlpha(new RsiAlphaModel(14, Resolution.Hour));
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SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
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SetExecution(new VolumeWeightedAveragePriceExecutionModel());
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InsightsGenerated += (algorithm, data) => Log($"{Time}: {string.Join(" | ", data.Insights.Select(insight => insight))}");
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}
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public override void OnOrderEvent(OrderEvent orderEvent)
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{
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Log($"{Time}: {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; } = 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, Language.Python };
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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 => 15643;
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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 => 56;
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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", "239"},
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{"Average Win", "0.05%"},
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{"Average Loss", "-0.01%"},
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{"Compounding Annual Return", "434.257%"},
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{"Drawdown", "1.300%"},
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{"Expectancy", "1.938"},
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{"Start Equity", "1000000"},
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{"End Equity", "1021655.71"},
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{"Net Profit", "2.166%"},
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{"Sharpe Ratio", "11.638"},
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{"Sortino Ratio", "0"},
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{"Probabilistic Sharpe Ratio", "70.184%"},
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{"Loss Rate", "31%"},
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{"Win Rate", "69%"},
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{"Profit-Loss Ratio", "3.26"},
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{"Alpha", "0.85"},
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{"Beta", "1.059"},
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{"Annual Standard Deviation", "0.253"},
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{"Annual Variance", "0.064"},
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{"Information Ratio", "10.466"},
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{"Tracking Error", "0.092"},
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{"Treynor Ratio", "2.778"},
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{"Total Fees", "$399.15"},
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{"Estimated Strategy Capacity", "$470000.00"},
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{"Lowest Capacity Asset", "AIG R735QTJ8XC9X"},
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{"Portfolio Turnover", "130.79%"},
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{"Drawdown Recovery", "1"},
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{"OrderListHash", "7a14c40f79d36294f931cd4b1f9e7179"}
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};
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}
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}
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