Ricardo Andrés Marino Rojas
b4bad69772
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* First attempt to solve the bug * Enhance implementation * Enhance implementation * Simplify implementation * Rebase regression stats * Solve unit test bugs * Review * Update Rolling.Sharpe() method * Update regression stats * Update unit tests * Update missing regression algos * Update Rolling.cs --------- Co-authored-by: Martin Molinero <martin.molinero1@gmail.com> Co-authored-by: Martin-Molinero <martin@quantconnect.com>
118 lines
4.5 KiB
C#
118 lines
4.5 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.Indicators;
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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 to test the behaviour of ARMA versus AR models at the same order of differencing.
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/// In particular, an ARIMA(1,1,1) and ARIMA(1,1,0) are instantiated while orders are placed if their difference
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/// is sufficiently large (which would be due to the inclusion of the MA(1) term).
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/// </summary>
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public class AutoRegressiveIntegratedMovingAverageRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private AutoRegressiveIntegratedMovingAverage _arima;
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private AutoRegressiveIntegratedMovingAverage _ar;
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private decimal _last;
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public override void Initialize()
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{
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SetStartDate(2013, 1, 07);
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SetEndDate(2013, 12, 11);
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EnableAutomaticIndicatorWarmUp = true;
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AddEquity("SPY", Resolution.Daily);
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_arima = ARIMA("SPY", 1, 1, 1, 50);
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_ar = ARIMA("SPY", 1, 1, 0, 50);
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}
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public override void OnData(Slice slice)
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{
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if (_arima.IsReady)
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{
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if (Math.Abs(_ar.Current.Value - _arima.Current.Value) > 1) // Difference due to MA(1) being included.
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{
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if (_arima.Current.Value > _last)
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{
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MarketOrder("SPY", 1);
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}
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else
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{
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MarketOrder("SPY", -1);
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}
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}
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_last = _arima.Current.Value;
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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 Language[] Languages { get; } = { 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 => 1893;
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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 => 100;
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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", "52"},
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{"Average Win", "0.00%"},
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{"Average Loss", "0.00%"},
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{"Compounding Annual Return", "0.096%"},
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{"Drawdown", "0.100%"},
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{"Expectancy", "3.321"},
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{"Net Profit", "0.089%"},
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{"Sharpe Ratio", "-8.214"},
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{"Probabilistic Sharpe Ratio", "40.893%"},
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{"Loss Rate", "24%"},
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{"Win Rate", "76%"},
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{"Profit-Loss Ratio", "4.67"},
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{"Alpha", "-0.008"},
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{"Beta", "0.008"},
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{"Annual Standard Deviation", "0.001"},
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{"Annual Variance", "0"},
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{"Information Ratio", "-1.961"},
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{"Tracking Error", "0.092"},
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{"Treynor Ratio", "-0.826"},
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{"Total Fees", "$52.00"},
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{"Estimated Strategy Capacity", "$32000000000.00"},
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{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
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{"Portfolio Turnover", "0.02%"},
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{"OrderListHash", "cf43585a8d1781f04b53a4f1ee3380cb"}
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};
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}
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}
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