Files
quantconnect--lean/Algorithm.CSharp/PortfolioRebalanceOnCustomFuncRegressionAlgorithm.cs
2020-04-06 10:32:59 -03:00

167 lines
7.4 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.Algorithm.Framework.Selection;
using QuantConnect.Orders;
using QuantConnect.Interfaces;
using QuantConnect.Securities;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Regression algorithm testing portfolio construction model control over rebalancing,
/// specifying a custom rebalance function that returns null in some cases, see GH 4075.
/// </summary>
public class PortfolioRebalanceOnCustomFuncRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private DateTime _lastRebalanceTime;
/// <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;
SetStartDate(2015, 1, 1);
SetEndDate(2018, 1, 1);
Settings.RebalancePortfolioOnInsightChanges = false;
Settings.RebalancePortfolioOnSecurityChanges = false;
SetUniverseSelection(new CustomUniverseSelectionModel("CustomUniverseSelectionModel",
time => new List<string> { "AAPL", "IBM", "FB", "SPY", "AIG", "BAC", "BNO" }
));
SetAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromMinutes(20), 0.025, null));
SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel(
time =>
{
// for performance only run rebalance logic once a week
if (time.DayOfWeek != DayOfWeek.Monday)
{
return null;
}
if (_lastRebalanceTime == default(DateTime))
{
// initial rebalance
_lastRebalanceTime = time;
return time;
}
var deviation = 0m;
var count = Securities.Values.Count(security => security.Invested);
if (count > 0)
{
_lastRebalanceTime = time;
var portfolioValuePerSecurity = Portfolio.TotalPortfolioValue / count;
foreach (var security in Securities.Values.Where(security => security.Invested))
{
var reservedBuyingPowerForCurrentPosition = security.BuyingPowerModel.GetReservedBuyingPowerForPosition(
new ReservedBuyingPowerForPositionParameters(security)).AbsoluteUsedBuyingPower
// see GH issue 4107
* security.BuyingPowerModel.GetLeverage(security);
// we sum up deviation for each security
deviation += (portfolioValuePerSecurity - reservedBuyingPowerForCurrentPosition) / portfolioValuePerSecurity;
}
// if securities are deviated 2% from their theoretical share of TotalPortfolioValue we rebalance
if (deviation >= 0.02m)
{
return time;
}
}
return null;
}));
SetExecution(new ImmediateExecutionModel());
}
public override void OnOrderEvent(OrderEvent orderEvent)
{
Debug($"{orderEvent}");
if (orderEvent.Status == OrderStatus.Submitted)
{
if (UtcTime - _lastRebalanceTime > TimeSpan.Zero || UtcTime.DayOfWeek != DayOfWeek.Monday)
{
throw new Exception($"{UtcTime} {orderEvent.Symbol} {UtcTime - _lastRebalanceTime}");
}
}
}
/// <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", "28"},
{"Average Win", "0.78%"},
{"Average Loss", "0%"},
{"Compounding Annual Return", "18.526%"},
{"Drawdown", "9.200%"},
{"Expectancy", "0"},
{"Net Profit", "66.431%"},
{"Sharpe Ratio", "1.752"},
{"Probabilistic Sharpe Ratio", "87.909%"},
{"Loss Rate", "0%"},
{"Win Rate", "100%"},
{"Profit-Loss Ratio", "0"},
{"Alpha", "0.16"},
{"Beta", "-0.055"},
{"Annual Standard Deviation", "0.088"},
{"Annual Variance", "0.008"},
{"Information Ratio", "0.376"},
{"Tracking Error", "0.147"},
{"Treynor Ratio", "-2.817"},
{"Total Fees", "$39.42"},
{"Fitness Score", "0.001"},
{"Kelly Criterion Estimate", "0"},
{"Kelly Criterion Probability Value", "1"},
{"Sortino Ratio", "2.129"},
{"Return Over Maximum Drawdown", "2.01"},
{"Portfolio Turnover", "0.001"},
{"Total Insights Generated", "5327"},
{"Total Insights Closed", "5320"},
{"Total Insights Analysis Completed", "5320"},
{"Long Insight Count", "5327"},
{"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", "994000364"}
};
}
}