Martin Molinero
113 lines
4.3 KiB
C#
113 lines
4.3 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 QuantConnect.Data.UniverseSelection;
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using QuantConnect.Interfaces;
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using QuantConnect.Orders.Fees;
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using System;
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using System.Collections.Generic;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// In this algorithm we demonstrate how to use the UniverseSettings
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/// to define the data normalization mode (raw)
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/// </summary>
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/// <meta name="tag" content="using data" />
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/// <meta name="tag" content="universes" />
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/// <meta name="tag" content="coarse universes" />
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/// <meta name="tag" content="regression test" />
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public class RawPricesUniverseRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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public override void Initialize()
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{
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// what resolution should the data *added* to the universe be?
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UniverseSettings.Resolution = Resolution.Daily;
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// Use raw prices
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UniverseSettings.DataNormalizationMode = DataNormalizationMode.Raw;
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SetStartDate(2014,3,24);
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SetEndDate(2014,4,7);
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SetCash(50000);
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// Set the security initializer with zero fees
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SetSecurityInitializer(x => x.SetFeeModel(new ConstantFeeModel(0)));
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AddUniverse("MyUniverse", Resolution.Daily, SelectionFunction);
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}
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public IEnumerable<string> SelectionFunction(DateTime dateTime)
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{
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return dateTime.Day % 2 == 0
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? new[] { "SPY", "IWM", "QQQ" }
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: new[] { "AIG", "BAC", "IBM" };
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}
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// this event fires whenever we have changes to our universe
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public override void OnSecuritiesChanged(SecurityChanges changes)
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{
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foreach (var security in changes.RemovedSecurities)
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{
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if (security.Invested)
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{
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Liquidate(security.Symbol);
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}
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}
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// we want 20% allocation in each security in our universe
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foreach (var security in changes.AddedSecurities)
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{
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SetHoldings(security.Symbol, 0.2m);
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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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/// 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", "27"},
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{"Average Win", "0.21%"},
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{"Average Loss", "-0.21%"},
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{"Compounding Annual Return", "-7.431%"},
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{"Drawdown", "0.800%"},
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{"Expectancy", "0.003"},
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{"Net Profit", "-0.317%"},
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{"Sharpe Ratio", "-1.31"},
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{"Loss Rate", "50%"},
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{"Win Rate", "50%"},
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{"Profit-Loss Ratio", "1.01"},
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{"Alpha", "-0.006"},
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{"Beta", "0.278"},
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{"Annual Standard Deviation", "0.043"},
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{"Annual Variance", "0.002"},
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{"Information Ratio", "1.57"},
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{"Tracking Error", "0.079"},
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{"Treynor Ratio", "-0.202"},
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{"Total Fees", "$0.00"}
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};
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}
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} |