Files
quantconnect--lean/Algorithm.CSharp/MeanVarianceOptimizationFrameworkAlgorithm.cs
T
Colton Sellers 5bf72d2432
Build & Test Lean / build (push) Has been cancelled
Adjust Volume for Splits (#5525)
* Adjust TradeBar volume for splits

* Refactor solution to centralized function `Scale`

* Expand BaseData scale/adjust/normalize tests to assert volume behavior

* Add regression algorithm

* Update regression statistics

* Address review

* NOP to trigger cloud build

* adjust QuoteBar ask and bid size

* adjust broken regression

* Update tests to include QuoteBar adjustments

* nit cleanup

* Adjust Quote Tick bid and ask sizes

* Round volume and size values to nearest int in scale()

* nit

* Adjust regressions
2021-05-17 16:47:44 -03:00

131 lines
5.6 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.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 System.Linq;
using QuantConnect.Data.UniverseSelection;
using QuantConnect.Orders;
namespace QuantConnect.Algorithm.CSharp
{
public class MeanVarianceOptimizationFrameworkAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private IEnumerable<Symbol> _symbols = (new[] { "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;
Settings.RebalancePortfolioOnInsightChanges = false;
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.
// set algorithm framework models
SetUniverseSelection(new CoarseFundamentalUniverseSelectionModel(CoarseSelector));
SetAlpha(new HistoricalReturnsAlphaModel(resolution: Resolution.Daily));
SetPortfolioConstruction(new MeanVarianceOptimizationPortfolioConstructionModel());
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 override void OnOrderEvent(OrderEvent orderEvent)
{
if (orderEvent.Status == OrderStatus.Filled)
{
Log($"{orderEvent}");
}
}
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", "12"},
{"Average Win", "0.30%"},
{"Average Loss", "-0.48%"},
{"Compounding Annual Return", "531.295%"},
{"Drawdown", "1.200%"},
{"Expectancy", "-0.595"},
{"Net Profit", "2.556%"},
{"Sharpe Ratio", "14.163"},
{"Probabilistic Sharpe Ratio", "74.688%"},
{"Loss Rate", "75%"},
{"Win Rate", "25%"},
{"Profit-Loss Ratio", "0.62"},
{"Alpha", "1.486"},
{"Beta", "0.809"},
{"Annual Standard Deviation", "0.185"},
{"Annual Variance", "0.034"},
{"Information Ratio", "11.831"},
{"Tracking Error", "0.103"},
{"Treynor Ratio", "3.236"},
{"Total Fees", "$26.11"},
{"Estimated Strategy Capacity", "$26000000.00"},
{"Lowest Capacity Asset", "AIG R735QTJ8XC9X"},
{"Fitness Score", "0.691"},
{"Kelly Criterion Estimate", "13.28"},
{"Kelly Criterion Probability Value", "0.237"},
{"Sortino Ratio", "79228162514264337593543950335"},
{"Return Over Maximum Drawdown", "438.018"},
{"Portfolio Turnover", "0.691"},
{"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", "$50520.5824"},
{"Total Accumulated Estimated Alpha Value", "$8700.7670"},
{"Mean Population Estimated Insight Value", "$870.0767"},
{"Mean Population Direction", "70%"},
{"Mean Population Magnitude", "70%"},
{"Rolling Averaged Population Direction", "94.5154%"},
{"Rolling Averaged Population Magnitude", "94.5154%"},
{"OrderListHash", "4e765434075ebc5bf51887a95b5cc43d"}
};
}
}