wehub-resource-sync
275 lines
10 KiB
C#
275 lines
10 KiB
C#
/*
|
|
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
|
|
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
|
|
*
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
*/
|
|
|
|
using System;
|
|
using System.Collections.Generic;
|
|
using System.Linq;
|
|
using QuantConnect.Algorithm.Framework.Alphas;
|
|
using QuantConnect.Algorithm.Framework.Execution;
|
|
using QuantConnect.Algorithm.Framework.Portfolio;
|
|
using QuantConnect.Data;
|
|
using QuantConnect.Data.UniverseSelection;
|
|
using QuantConnect.Interfaces;
|
|
using QuantConnect.Orders;
|
|
|
|
namespace QuantConnect.Algorithm.CSharp
|
|
{
|
|
/// <summary>
|
|
/// Tests ETF constituents universe selection with the algorithm framework models (Alpha, PortfolioConstruction, Execution)
|
|
/// </summary>
|
|
public class ETFConstituentUniverseFrameworkRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
|
{
|
|
private List<ETFConstituentUniverse> ConstituentData = new List<ETFConstituentUniverse>();
|
|
|
|
/// <summary>
|
|
/// Initializes the algorithm, setting up the framework classes and ETF constituent universe settings
|
|
/// </summary>
|
|
public override void Initialize()
|
|
{
|
|
SetStartDate(2020, 12, 1);
|
|
SetEndDate(2021, 1, 31);
|
|
SetCash(100000);
|
|
|
|
SetAlpha(new ETFConstituentAlphaModel());
|
|
SetPortfolioConstruction(new ETFConstituentPortfolioModel());
|
|
SetExecution(new ETFConstituentExecutionModel());
|
|
|
|
var spy = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
|
|
|
|
UniverseSettings.Resolution = Resolution.Hour;
|
|
AddUniverseWrapper(spy);
|
|
}
|
|
|
|
protected virtual void AddUniverseWrapper(Symbol symbol)
|
|
{
|
|
var universe = AddUniverse(Universe.ETF(symbol, UniverseSettings, FilterETFConstituents));
|
|
|
|
var historicalData = History(universe, 1).ToList();
|
|
if (historicalData.Count != 1)
|
|
{
|
|
throw new RegressionTestException($"Unexpected history count {historicalData.Count}! Expected 1");
|
|
}
|
|
foreach (var universeDataCollection in historicalData)
|
|
{
|
|
if (universeDataCollection.Data.Count < 200)
|
|
{
|
|
throw new RegressionTestException($"Unexpected universe DataCollection count {universeDataCollection.Data.Count}! Expected > 200");
|
|
}
|
|
}
|
|
}
|
|
|
|
/// <summary>
|
|
/// Filters ETF constituents
|
|
/// </summary>
|
|
/// <param name="constituents">ETF constituents</param>
|
|
/// <returns>ETF constituent Symbols that we want to include in the algorithm</returns>
|
|
public IEnumerable<Symbol> FilterETFConstituents(IEnumerable<ETFConstituentUniverse> constituents)
|
|
{
|
|
var constituentData = constituents
|
|
.Where(x => (x.Weight ?? 0m) >= 0.001m)
|
|
.ToList();
|
|
|
|
ConstituentData = constituentData;
|
|
|
|
return constituentData
|
|
.Select(x => x.Symbol)
|
|
.ToList();
|
|
}
|
|
|
|
/// <summary>
|
|
/// no-op for performance
|
|
/// </summary>
|
|
public override void OnData(Slice data)
|
|
{
|
|
}
|
|
|
|
/// <summary>
|
|
/// Alpha model for ETF constituents, where we generate insights based on the weighting
|
|
/// of the ETF constituent
|
|
/// </summary>
|
|
private class ETFConstituentAlphaModel : IAlphaModel
|
|
{
|
|
public void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
|
|
{
|
|
}
|
|
|
|
/// <summary>
|
|
/// Creates new insights based on constituent data and their weighting
|
|
/// in their respective ETF
|
|
/// </summary>
|
|
public IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice data)
|
|
{
|
|
var algo = (ETFConstituentUniverseFrameworkRegressionAlgorithm) algorithm;
|
|
|
|
foreach (var constituent in algo.ConstituentData)
|
|
{
|
|
if (!data.Bars.ContainsKey(constituent.Symbol) &&
|
|
!data.QuoteBars.ContainsKey(constituent.Symbol))
|
|
{
|
|
continue;
|
|
}
|
|
|
|
var insightDirection = constituent.Weight != null && constituent.Weight >= 0.01m
|
|
? InsightDirection.Up
|
|
: InsightDirection.Down;
|
|
|
|
yield return new Insight(
|
|
algorithm.UtcTime,
|
|
constituent.Symbol,
|
|
TimeSpan.FromDays(1),
|
|
InsightType.Price,
|
|
insightDirection,
|
|
1 * (double)insightDirection,
|
|
1.0,
|
|
weight: (double)(constituent.Weight ?? 0));
|
|
}
|
|
}
|
|
}
|
|
|
|
/// <summary>
|
|
/// Generates targets for ETF constituents, which will be set to the weighting
|
|
/// of the constituent in their respective ETF
|
|
/// </summary>
|
|
private class ETFConstituentPortfolioModel : IPortfolioConstructionModel
|
|
{
|
|
private bool _hasAdded;
|
|
|
|
/// <summary>
|
|
/// Securities changed, detects if we've got new additions to the universe
|
|
/// so that we don't try to trade every loop
|
|
/// </summary>
|
|
public void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
|
|
{
|
|
_hasAdded = changes.AddedSecurities.Count != 0;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Creates portfolio targets based on the insights provided to us by the alpha model.
|
|
/// Emits portfolio targets setting the quantity to the weight of the constituent
|
|
/// in its respective ETF.
|
|
/// </summary>
|
|
public IEnumerable<IPortfolioTarget> CreateTargets(QCAlgorithm algorithm, Insight[] insights)
|
|
{
|
|
if (!_hasAdded)
|
|
{
|
|
yield break;
|
|
}
|
|
|
|
foreach (var insight in insights)
|
|
{
|
|
yield return new PortfolioTarget(insight.Symbol, (decimal) (insight.Weight ?? 0));
|
|
_hasAdded = false;
|
|
}
|
|
}
|
|
}
|
|
|
|
/// <summary>
|
|
/// Executes based on ETF constituent weighting
|
|
/// </summary>
|
|
private class ETFConstituentExecutionModel : IExecutionModel
|
|
{
|
|
/// <summary>
|
|
/// Liquidates if constituents have been removed from the universe
|
|
/// </summary>
|
|
public void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
|
|
{
|
|
foreach (var change in changes.RemovedSecurities)
|
|
{
|
|
algorithm.Liquidate(change.Symbol);
|
|
}
|
|
}
|
|
|
|
/// <summary>
|
|
/// Creates orders for constituents that attempts to add
|
|
/// the weighting of the constituent in our portfolio. The
|
|
/// resulting algorithm portfolio weight might not be equal
|
|
/// to the leverage of the ETF (1x, 2x, 3x, etc.)
|
|
/// </summary>
|
|
public void Execute(QCAlgorithm algorithm, IPortfolioTarget[] targets)
|
|
{
|
|
foreach (var target in targets)
|
|
{
|
|
algorithm.SetHoldings(target.Symbol, target.Quantity);
|
|
}
|
|
}
|
|
|
|
public void OnOrderEvent(QCAlgorithm algorithm, OrderEvent orderEvent)
|
|
{
|
|
|
|
}
|
|
}
|
|
|
|
/// <summary>
|
|
/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
|
|
/// </summary>
|
|
public bool CanRunLocally { get; } = true;
|
|
|
|
/// <summary>
|
|
/// This is used by the regression test system to indicate which languages this algorithm is written in.
|
|
/// </summary>
|
|
public virtual List<Language> Languages { get; } = new() { Language.CSharp, Language.Python };
|
|
|
|
/// <summary>
|
|
/// Data Points count of all timeslices of algorithm
|
|
/// </summary>
|
|
public long DataPoints => 2436;
|
|
|
|
/// <summary>
|
|
/// Data Points count of the algorithm history
|
|
/// </summary>
|
|
public virtual int AlgorithmHistoryDataPoints => 1;
|
|
|
|
/// <summary>
|
|
/// Final status of the algorithm
|
|
/// </summary>
|
|
public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;
|
|
|
|
/// <summary>
|
|
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
|
|
/// </summary>
|
|
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
|
{
|
|
{"Total Orders", "3"},
|
|
{"Average Win", "0%"},
|
|
{"Average Loss", "0%"},
|
|
{"Compounding Annual Return", "3.006%"},
|
|
{"Drawdown", "0.700%"},
|
|
{"Expectancy", "0"},
|
|
{"Start Equity", "100000"},
|
|
{"End Equity", "100485.34"},
|
|
{"Net Profit", "0.485%"},
|
|
{"Sharpe Ratio", "1.055"},
|
|
{"Sortino Ratio", "1.53"},
|
|
{"Probabilistic Sharpe Ratio", "50.834%"},
|
|
{"Loss Rate", "0%"},
|
|
{"Win Rate", "0%"},
|
|
{"Profit-Loss Ratio", "0"},
|
|
{"Alpha", "0.012"},
|
|
{"Beta", "0.096"},
|
|
{"Annual Standard Deviation", "0.017"},
|
|
{"Annual Variance", "0"},
|
|
{"Information Ratio", "-0.544"},
|
|
{"Tracking Error", "0.096"},
|
|
{"Treynor Ratio", "0.191"},
|
|
{"Total Fees", "$3.00"},
|
|
{"Estimated Strategy Capacity", "$1400000000.00"},
|
|
{"Lowest Capacity Asset", "IBM R735QTJ8XC9X"},
|
|
{"Portfolio Turnover", "0.12%"},
|
|
{"Drawdown Recovery", "22"},
|
|
{"OrderListHash", "5d1e80a607d65ba4c7329f6f0b86999f"}
|
|
};
|
|
}
|
|
}
|