Files
quantconnect--lean/Algorithm.CSharp/BlackLittermanPortfolioOptimizationFrameworkAlgorithm.cs
T
Martin-Molinero cb326788b3
Regression Tests / build (push) Has been cancelled
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Filter out small orders based on Setting (#5776)
* Filter out small orders based on Setting

- BuyingPowerModel will filter out small orders based on algorithm
  setting, a % of PTV, instead of hard coded 1 share value. Addin unit
  and regression tests
- Updating regression algorithms to use new setting, reduce order trades

* Update regression algorithms
2021-07-19 13:17:51 -03:00

126 lines
5.7 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.Linq;
using System.Collections.Generic;
using QuantConnect.Algorithm.Framework.Alphas;
using QuantConnect.Algorithm.Framework.Execution;
using QuantConnect.Algorithm.Framework.Portfolio;
using QuantConnect.Algorithm.Framework.Risk;
using QuantConnect.Algorithm.Framework.Selection;
using QuantConnect.Interfaces;
using QuantConnect.Data.UniverseSelection;
namespace QuantConnect.Algorithm.CSharp
{
public class BlackLittermanPortfolioOptimizationFrameworkAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private IEnumerable<Symbol> _symbols = (new string[] { "AIG", "BAC", "IBM", "SPY" }).Select(s => QuantConnect.Symbol.Create(s, SecurityType.Equity, Market.USA));
/// <summary>
/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
/// </summary>
public override void Initialize()
{
// Set requested data resolution
UniverseSettings.Resolution = Resolution.Minute;
// Order margin value has to have a minimum of 0.5% of Portfolio value, allows filtering out small trades and reduce fees.
// Commented so regression algorithm is more sensitive
//Settings.MinimumOrderMarginPortfolioPercentage = 0.005m;
SetStartDate(2013, 10, 07); //Set Start Date
SetEndDate(2013, 10, 11); //Set End Date
SetCash(100000); //Set Strategy Cash
// Find more symbols here: http://quantconnect.com/data
// Forex, CFD, Equities Resolutions: Tick, Second, Minute, Hour, Daily.
// Futures Resolution: Tick, Second, Minute
// Options Resolution: Minute Only.
var optimizer = new UnconstrainedMeanVariancePortfolioOptimizer();
// set algorithm framework models
SetUniverseSelection(new CoarseFundamentalUniverseSelectionModel(CoarseSelector));
SetAlpha(new HistoricalReturnsAlphaModel(resolution: Resolution.Daily));
SetPortfolioConstruction(new BlackLittermanOptimizationPortfolioConstructionModel(optimizer: optimizer));
SetExecution(new ImmediateExecutionModel());
SetRiskManagement(new NullRiskManagementModel());
}
public IEnumerable<Symbol> CoarseSelector(IEnumerable<CoarseFundamental> coarse)
{
int last = Time.Day > 8 ? 3 : _symbols.Count();
return _symbols.Take(last);
}
public bool CanRunLocally => true;
/// <summary>
/// This is used by the regression test system to indicate which languages this algorithm is written in.
/// </summary>
public Language[] Languages { get; } = { Language.CSharp, Language.Python };
/// <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 Trades", "20"},
{"Average Win", "0%"},
{"Average Loss", "-0.13%"},
{"Compounding Annual Return", "62.435%"},
{"Drawdown", "1.100%"},
{"Expectancy", "-1"},
{"Net Profit", "0.667%"},
{"Sharpe Ratio", "3.507"},
{"Probabilistic Sharpe Ratio", "59.181%"},
{"Loss Rate", "100%"},
{"Win Rate", "0%"},
{"Profit-Loss Ratio", "0"},
{"Alpha", "-0.384"},
{"Beta", "0.564"},
{"Annual Standard Deviation", "0.116"},
{"Annual Variance", "0.013"},
{"Information Ratio", "-10.791"},
{"Tracking Error", "0.092"},
{"Treynor Ratio", "0.718"},
{"Total Fees", "$46.20"},
{"Estimated Strategy Capacity", "$2300000.00"},
{"Lowest Capacity Asset", "AIG R735QTJ8XC9X"},
{"Fitness Score", "0.645"},
{"Kelly Criterion Estimate", "13.787"},
{"Kelly Criterion Probability Value", "0.231"},
{"Sortino Ratio", "79228162514264337593543950335"},
{"Return Over Maximum Drawdown", "65.642"},
{"Portfolio Turnover", "0.645"},
{"Total Insights Generated", "13"},
{"Total Insights Closed", "10"},
{"Total Insights Analysis Completed", "10"},
{"Long Insight Count", "6"},
{"Short Insight Count", "7"},
{"Long/Short Ratio", "85.71%"},
{"Estimated Monthly Alpha Value", "$52003.0716"},
{"Total Accumulated Estimated Alpha Value", "$8956.0846"},
{"Mean Population Estimated Insight Value", "$895.6085"},
{"Mean Population Direction", "70%"},
{"Mean Population Magnitude", "70%"},
{"Rolling Averaged Population Direction", "94.5154%"},
{"Rolling Averaged Population Magnitude", "94.5154%"},
{"OrderListHash", "0945ff7a39bb8f8a07b3dcc817c070aa"}
};
}
}