Files
quantconnect--lean/Algorithm.CSharp/PortfolioRebalanceOnDateRulesRegressionAlgorithm.cs
T
Martin-Molinero cb326788b3
Regression Tests / build (push) Has been cancelled
Build & Test Lean / build (push) Has been cancelled
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

131 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;
using System.Collections.Generic;
using QuantConnect.Algorithm.Framework.Alphas;
using QuantConnect.Algorithm.Framework.Execution;
using QuantConnect.Algorithm.Framework.Portfolio;
using QuantConnect.Algorithm.Framework.Selection;
using QuantConnect.Orders;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Regression algorithm testing portfolio construction model control over rebalancing,
/// specifying a date rules, see GH 4075.
/// </summary>
public class PortfolioRebalanceOnDateRulesRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
/// <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()
{
UniverseSettings.Resolution = Resolution.Daily;
// 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(2015, 1, 1);
SetEndDate(2017, 1, 1);
Settings.RebalancePortfolioOnInsightChanges = false;
Settings.RebalancePortfolioOnSecurityChanges = false;
SetUniverseSelection(new CustomUniverseSelectionModel(
"CustomUniverseSelectionModel",
time => new List<string> { "AAPL", "IBM", "FB", "SPY" }
));
SetAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromMinutes(20), 0.025, null));
SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel(DateRules.Every(DayOfWeek.Wednesday)));
SetExecution(new ImmediateExecutionModel());
}
public override void OnOrderEvent(OrderEvent orderEvent)
{
if (orderEvent.Status == OrderStatus.Submitted)
{
Debug($"{orderEvent}");
if (UtcTime.DayOfWeek != DayOfWeek.Wednesday)
{
throw new Exception($"{UtcTime} {orderEvent.Symbol} {UtcTime.DayOfWeek}");
}
}
}
/// <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 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", "362"},
{"Average Win", "0.06%"},
{"Average Loss", "-0.03%"},
{"Compounding Annual Return", "11.407%"},
{"Drawdown", "18.200%"},
{"Expectancy", "1.300"},
{"Net Profit", "24.116%"},
{"Sharpe Ratio", "0.689"},
{"Probabilistic Sharpe Ratio", "29.629%"},
{"Loss Rate", "24%"},
{"Win Rate", "76%"},
{"Profit-Loss Ratio", "2.01"},
{"Alpha", "0.106"},
{"Beta", "0.006"},
{"Annual Standard Deviation", "0.154"},
{"Annual Variance", "0.024"},
{"Information Ratio", "0.219"},
{"Tracking Error", "0.201"},
{"Treynor Ratio", "16.79"},
{"Total Fees", "$366.83"},
{"Estimated Strategy Capacity", "$40000000.00"},
{"Lowest Capacity Asset", "IBM R735QTJ8XC9X"},
{"Fitness Score", "0.002"},
{"Kelly Criterion Estimate", "0"},
{"Kelly Criterion Probability Value", "1"},
{"Sortino Ratio", "0.949"},
{"Return Over Maximum Drawdown", "0.625"},
{"Portfolio Turnover", "0.003"},
{"Total Insights Generated", "2028"},
{"Total Insights Closed", "2024"},
{"Total Insights Analysis Completed", "2024"},
{"Long Insight Count", "2028"},
{"Short Insight Count", "0"},
{"Long/Short Ratio", "100%"},
{"Estimated Monthly Alpha Value", "$0"},
{"Total Accumulated Estimated Alpha Value", "$0"},
{"Mean Population Estimated Insight Value", "$0"},
{"Mean Population Direction", "0%"},
{"Mean Population Magnitude", "0%"},
{"Rolling Averaged Population Direction", "0%"},
{"Rolling Averaged Population Magnitude", "0%"},
{"OrderListHash", "20d5c49aff16826f5a7fba8f3b9c23f2"}
};
}
}