chore: import upstream snapshot with attribution
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wehub-resource-sync
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/*
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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.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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using QuantConnect.Securities;
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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 sector weighted portfolio
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/// </summary>
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public class SectorWeightingFrameworkAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private readonly Dictionary<Symbol, decimal> _targets = new Dictionary<Symbol, decimal>();
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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, 04, 02);
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SetEndDate(2014, 04, 06);
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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 SectorWeightingPortfolioConstructionModel());
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Func<string, Symbol> toSymbol = t => QuantConnect.Symbol.Create(t, SecurityType.Equity, Market.USA);
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_targets.Add(toSymbol("AAPL"), .25m);
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_targets.Add(toSymbol("AIG"), .5m);
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_targets.Add(toSymbol("IBM"), .25m);
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_targets.Add(toSymbol("GOOG"), .5m);
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_targets.Add(toSymbol("BAC"), .5m);
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_targets.Add(toSymbol("SPY"), 0);
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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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var symbol = orderEvent.Symbol;
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var security = Securities[symbol];
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var absoluteBuyingPower = security.BuyingPowerModel
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.GetReservedBuyingPowerForPosition(new ReservedBuyingPowerForPositionParameters(security))
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.AbsoluteUsedBuyingPower // See GH issue 4107
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* security.BuyingPowerModel.GetLeverage(security);
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var portfolioShare = absoluteBuyingPower / Portfolio.TotalPortfolioValue;
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Debug($"Order event: {orderEvent}. Absolute buying power: {absoluteBuyingPower}");
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// Checks whether the portfolio share of a given symbol matches its target
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// Only considers the buy orders, because holding value is zero otherwise
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if (Math.Abs(_targets[symbol] - portfolioShare) > 0.01m && orderEvent.Direction == OrderDirection.Buy)
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{
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throw new RegressionTestException($"Target for {symbol}: expected {_targets[symbol]}, actual: {portfolioShare}");
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}
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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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return Time.Date < new DateTime(2014, 4, 4)
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// IndustryTemplateCode of AAPL and IBM is N and AIG is I
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? _targets.Keys.Take(3)
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// IndustryTemplateCode of GOOG is N and BAC is B. SPY have no fundamentals
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: _targets.Keys.Skip(3);
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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 => 52;
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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", "9"},
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{"Average Win", "0.00%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "-67.218%"},
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{"Drawdown", "0.900%"},
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{"Expectancy", "0"},
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{"Start Equity", "100000"},
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{"End Equity", "99087.50"},
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{"Net Profit", "-0.912%"},
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{"Sharpe Ratio", "-12.084"},
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{"Sortino Ratio", "-12.084"},
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{"Probabilistic Sharpe Ratio", "0%"},
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{"Loss Rate", "0%"},
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{"Win Rate", "100%"},
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{"Profit-Loss Ratio", "0"},
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{"Alpha", "-0.291"},
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{"Beta", "0.491"},
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{"Annual Standard Deviation", "0.057"},
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{"Annual Variance", "0.003"},
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{"Information Ratio", "2.114"},
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{"Tracking Error", "0.059"},
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{"Treynor Ratio", "-1.41"},
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{"Total Fees", "$14.98"},
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{"Estimated Strategy Capacity", "$150000000.00"},
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{"Lowest Capacity Asset", "IBM R735QTJ8XC9X"},
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{"Portfolio Turnover", "33.44%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "e3f762555cf5848a2e79c1e23b11ca32"}
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};
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}
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}
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