wehub-resource-sync
131 lines
5.2 KiB
C#
131 lines
5.2 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.Algorithm.Framework.Alphas;
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using QuantConnect.Algorithm.Framework.Portfolio;
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using QuantConnect.Algorithm.Framework.Risk;
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using QuantConnect.Algorithm.Framework.Selection;
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using QuantConnect.Data.Fundamental;
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using QuantConnect.Data.UniverseSelection;
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using QuantConnect.Orders;
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using QuantConnect.Interfaces;
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using System;
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using System.Collections.Generic;
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using System.Linq;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// This example algorithm defines its own custom coarse/fine fundamental selection model
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/// with equally weighted portfolio and a maximum sector exposure
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/// </summary>
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public class SectorExposureRiskFrameworkAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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public override void Initialize()
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{
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// Set requested data resolution
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UniverseSettings.Resolution = Resolution.Daily;
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SetStartDate(2014, 03, 25);
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SetEndDate(2014, 04, 07);
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SetCash(100000);
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SetUniverseSelection(new FineFundamentalUniverseSelectionModel(SelectCoarse, SelectFine));
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SetAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, QuantConnect.Time.OneDay));
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SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
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SetRiskManagement(new MaximumSectorExposureRiskManagementModel());
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}
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public override void OnOrderEvent(OrderEvent orderEvent)
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{
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if (orderEvent.Status.IsFill())
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{
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Debug($"Order event: {orderEvent}. Holding value: {Securities[orderEvent.Symbol].Holdings.AbsoluteHoldingsValue}");
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}
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}
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private IEnumerable<Symbol> SelectCoarse(IEnumerable<CoarseFundamental> coarse)
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{
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var tickers = Time.Date < new DateTime(2014, 4, 1)
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? new[] { "AAPL", "AIG", "IBM" }
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: new[] { "GOOG", "BAC", "SPY" };
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return tickers.Select(x => QuantConnect.Symbol.Create(x, SecurityType.Equity, Market.USA));
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}
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private IEnumerable<Symbol> SelectFine(IEnumerable<FineFundamental> fine) => fine.Select(f => f.Symbol);
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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 => 7246;
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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", "16"},
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{"Average Win", "0.00%"},
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{"Average Loss", "-0.09%"},
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{"Compounding Annual Return", "-89.499%"},
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{"Drawdown", "8.300%"},
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{"Expectancy", "-0.831"},
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{"Start Equity", "100000"},
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{"End Equity", "91718.76"},
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{"Net Profit", "-8.281%"},
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{"Sharpe Ratio", "-3.238"},
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{"Sortino Ratio", "-2.445"},
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{"Probabilistic Sharpe Ratio", "0.000%"},
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{"Loss Rate", "83%"},
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{"Win Rate", "17%"},
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{"Profit-Loss Ratio", "0.02"},
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{"Alpha", "-0.762"},
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{"Beta", "0.276"},
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{"Annual Standard Deviation", "0.252"},
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{"Annual Variance", "0.063"},
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{"Information Ratio", "-2.402"},
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{"Tracking Error", "0.26"},
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{"Treynor Ratio", "-2.954"},
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{"Total Fees", "$25.93"},
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{"Estimated Strategy Capacity", "$54000000.00"},
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{"Lowest Capacity Asset", "AIG R735QTJ8XC9X"},
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{"Portfolio Turnover", "11.09%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "370ce70c920470fa54d855d700a7bf48"}
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};
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}
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}
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