wehub-resource-sync
132 lines
4.8 KiB
C#
132 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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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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using QuantConnect.Interfaces;
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using QuantConnect.Data;
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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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// are only used if the parameters do not exist.
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[Parameter("ema-fast")]
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private int _fastPeriod = 100;
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[Parameter("ema-slow")]
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private int _slowPeriod = 200;
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private ExponentialMovingAverage _fast;
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private 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 override void OnData(Slice 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 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 List<Language> Languages { get; } = new() { 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 => 3943;
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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 => 0;
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/// <summary>
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/// Final status of the algorithm
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/// </summary>
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public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;
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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 Orders", "1"},
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{"Average Win", "0%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "286.047%"},
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{"Drawdown", "0.300%"},
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{"Expectancy", "0"},
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{"Start Equity", "100000"},
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{"End Equity", "101742.04"},
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{"Net Profit", "1.742%"},
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{"Sharpe Ratio", "23.023"},
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{"Sortino Ratio", "0"},
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{"Probabilistic Sharpe Ratio", "0%"},
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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", "1.266"},
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{"Beta", "0.356"},
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{"Annual Standard Deviation", "0.086"},
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{"Annual Variance", "0.007"},
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{"Information Ratio", "-0.044"},
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{"Tracking Error", "0.147"},
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{"Treynor Ratio", "5.531"},
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{"Total Fees", "$3.45"},
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{"Estimated Strategy Capacity", "$48000000.00"},
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{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
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{"Portfolio Turnover", "19.72%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "1fd15c0ef2042df5cd6e6d590000318e"}
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};
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}
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}
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