Martin Molinero
113 lines
4.8 KiB
C#
113 lines
4.8 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.Collections.Generic;
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using QuantConnect.Algorithm.Framework;
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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 : QCAlgorithmFramework, 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($"{Time}: {string.Join(" | ", data.Insights)}");
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}
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public override void OnOrderEvent(OrderEvent orderEvent)
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{
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Log($"{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 Language[] Languages { get; } = { Language.CSharp, Language.Python };
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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 Trades", "394"},
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{"Average Win", "0.03%"},
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{"Average Loss", "0.00%"},
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{"Compounding Annual Return", "608.511%"},
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{"Drawdown", "1.000%"},
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{"Expectancy", "5.981"},
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{"Net Profit", "2.535%"},
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{"Sharpe Ratio", "6.099"},
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{"Loss Rate", "35%"},
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{"Win Rate", "65%"},
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{"Profit-Loss Ratio", "9.79"},
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{"Alpha", "0"},
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{"Beta", "116.165"},
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{"Annual Standard Deviation", "0.21"},
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{"Annual Variance", "0.044"},
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{"Information Ratio", "6.049"},
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{"Tracking Error", "0.21"},
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{"Treynor Ratio", "0.011"},
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{"Total Fees", "$506.02"},
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{"Total Insights Generated", "5"},
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{"Total Insights Closed", "3"},
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{"Total Insights Analysis Completed", "3"},
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{"Long Insight Count", "3"},
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{"Short Insight Count", "2"},
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{"Long/Short Ratio", "150.0%"},
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{"Estimated Monthly Alpha Value", "$799818.3566"},
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{"Total Accumulated Estimated Alpha Value", "$128859.6241"},
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{"Mean Population Estimated Insight Value", "$42953.2080"},
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{"Mean Population Direction", "100%"},
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{"Mean Population Magnitude", "0%"},
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{"Rolling Averaged Population Direction", "100%"},
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{"Rolling Averaged Population Magnitude", "0%"}
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};
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}
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}
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