Ricardo Andrés Marino Rojas
101 lines
3.9 KiB
C#
101 lines
3.9 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;
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using System.Collections.Generic;
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using QuantConnect.Algorithm;
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using QuantConnect.Algorithm.Framework.Alphas;
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using QuantConnect.Algorithm.Framework.Portfolio;
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using QuantConnect.Interfaces;
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namespace QuantConnect.DataLibrary.Tests
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{
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/// <summary>
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/// Example algorithm of using MeanReversionPortfolioConstructionModel
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/// </summary>
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public class MeanReversionPortfolioAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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public override void Initialize()
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{
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SetStartDate(2020, 9, 1);
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SetEndDate(2021, 2, 28);
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SetCash(100000);
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SetSecurityInitializer(security => security.SetMarketPrice(GetLastKnownPrice(security)));
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foreach (var ticker in new List<string>{"SPY", "AAPL"})
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{
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AddEquity(ticker, Resolution.Daily);
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}
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AddAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromDays(1)));
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SetPortfolioConstruction(new MeanReversionPortfolioConstructionModel());
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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 };
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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 => 1115;
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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 => 57;
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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", "63"},
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{"Average Win", "2.31%"},
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{"Average Loss", "-0.41%"},
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{"Compounding Annual Return", "20.043%"},
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{"Drawdown", "12.300%"},
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{"Expectancy", "1.841"},
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{"Net Profit", "9.375%"},
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{"Sharpe Ratio", "0.636"},
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{"Sortino Ratio", "0.722"},
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{"Probabilistic Sharpe Ratio", "36.899%"},
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{"Loss Rate", "57%"},
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{"Win Rate", "43%"},
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{"Profit-Loss Ratio", "5.63"},
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{"Alpha", "-0.02"},
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{"Beta", "1.3"},
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{"Annual Standard Deviation", "0.246"},
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{"Annual Variance", "0.061"},
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{"Information Ratio", "0.126"},
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{"Tracking Error", "0.163"},
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{"Treynor Ratio", "0.12"},
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{"Total Fees", "$122.78"},
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{"Estimated Strategy Capacity", "$370000000.00"},
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{"Lowest Capacity Asset", "AAPL R735QTJ8XC9X"},
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{"Portfolio Turnover", "17.55%"},
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{"OrderListHash", "3becde2bc13398d50d231a2d2e6de495"}
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};
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}
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}
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