Michael Handschuh
9ee61f425c
A mechanical refactoring was performed to make algorithms currently used in regression algorithms to implement IRegressionAlgorithmDefinition, which allows algorithms to define their own expected statistics and what languages should be run as part of regression. The type name of the C# type is used to determine the file/model name for python. This was for simplicity, but if needed, could later be refactored to expose more information, but for now the convention of keeping names the same makes sense and just works easily.
101 lines
3.6 KiB
C#
101 lines
3.6 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.Collections.Generic;
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using QuantConnect.Data.Market;
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using QuantConnect.Indicators;
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using QuantConnect.Parameters;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Demonstration of the parameter system of QuantConnect. Using parameters you can pass the values required into C# algorithms for optimization.
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/// </summary>
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/// <meta name="tag" content="optimization" />
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/// <meta name="tag" content="using quantconnect" />
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public class ParameterizedAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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// we place attributes on top of our fields or properties that should receive
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// their values from the job. The values 100 and 200 are just default values that
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// or only used if the parameters do not exist
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[Parameter("ema-fast")]
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public int FastPeriod = 100;
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[Parameter("ema-slow")]
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public int SlowPeriod = 200;
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public ExponentialMovingAverage Fast;
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public ExponentialMovingAverage Slow;
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public override void Initialize()
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{
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SetStartDate(2013, 10, 07);
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SetEndDate(2013, 10, 11);
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SetCash(100*1000);
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AddSecurity(SecurityType.Equity, "SPY");
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Fast = EMA("SPY", FastPeriod);
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Slow = EMA("SPY", SlowPeriod);
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}
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public void OnData(TradeBars data)
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{
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// wait for our indicators to ready
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if (!Fast.IsReady || !Slow.IsReady) return;
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if (Fast > Slow*1.001m)
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{
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SetHoldings("SPY", 1);
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}
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else if (Fast < Slow*0.999m)
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{
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Liquidate("SPY");
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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 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", "1"},
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{"Average Win", "0%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "278.616%"},
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{"Drawdown", "0.300%"},
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{"Expectancy", "0"},
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{"Net Profit", "1.717%"},
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{"Sharpe Ratio", "11.017"},
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{"Loss Rate", "0%"},
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{"Win Rate", "0%"},
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{"Profit-Loss Ratio", "0"},
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{"Alpha", "0"},
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{"Beta", "78.067"},
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{"Annual Standard Deviation", "0.078"},
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{"Annual Variance", "0.006"},
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{"Information Ratio", "10.897"},
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{"Tracking Error", "0.078"},
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{"Treynor Ratio", "0.011"},
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{"Total Fees", "$3.09"},
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};
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}
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}
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