Files
quantconnect--lean/Algorithm.CSharp/AlphaStreamsUniverseSelectionTemplateAlgorithm.cs
T
Colton Sellers d2d99b1f10
Regression Tests / build (push) Has been cancelled
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Algorithm Sampling and Statistics Fixes (#5936)
* Implement scheduled event sampling solution

* Use UTC time, only update daily portfolio value once a day

* For daily resolutions sample chart always

* Cleanup

* Drop resample daily all together

* Force final sample

* Regression updates

* FIx LiveResultHandler to update portfolio and benchmark values outside of sampling event

* Name the daily sampling event

* Address review pt 1

* Drop force and use reference wrapper

* Adjust tests

* Fix warning for Benchmark Timezone Misalignment and also add test

* Fix for daily resolution orders and test adjustments

* Also warn on universe settings with daily resolution

* Update missed regression

* Fix reference wrapper use

* Update regression after rebase

* Add values back in for Daylight Algo

* Have statistics builder skip day 1 performance

* Regression adjustments

* Test adjustments

* Update regression unit test

* Adjust some regressions starts to show performance values

* Add hourly algorithm for beta comparison

* Address missing Python regression changes

* Remove null comment
2021-10-05 19:31:25 -03:00

118 lines
4.9 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.Data.UniverseSelection;
using QuantConnect.Data.Custom.AlphaStreams;
using QuantConnect.Algorithm.Framework.Execution;
using QuantConnect.Algorithm.Framework.Portfolio;
using QuantConnect.Algorithm.Framework.Selection;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Example algorithm consuming an alpha streams portfolio state and trading based on it
/// </summary>
public class AlphaStreamsUniverseSelectionTemplateAlgorithm : AlphaStreamsBasicTemplateAlgorithm
{
/// <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()
{
SetStartDate(2018, 04, 04);
SetEndDate(2018, 04, 06);
SetExecution(new ImmediateExecutionModel());
Settings.MinimumOrderMarginPortfolioPercentage = 0.01m;
SetPortfolioConstruction(new EqualWeightingAlphaStreamsPortfolioConstructionModel());
SetUniverseSelection(new ScheduledUniverseSelectionModel(
DateRules.EveryDay(),
TimeRules.Midnight,
SelectAlphas,
new UniverseSettings(UniverseSettings)
{
SubscriptionDataTypes = new List<Tuple<Type, TickType>>
{new(typeof(AlphaStreamsPortfolioState), TickType.Trade)},
FillForward = false,
}
));
}
private IEnumerable<Symbol> SelectAlphas(DateTime dateTime)
{
Log($"SelectAlphas() {Time}");
foreach (var alphaId in new[] {"623b06b231eb1cc1aa3643a46", "9fc8ef73792331b11dbd5429a"})
{
var alphaSymbol = new Symbol(SecurityIdentifier.GenerateBase(typeof(AlphaStreamsPortfolioState), alphaId, Market.USA),
alphaId);
yield return alphaSymbol;
}
}
/// <summary>
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
/// </summary>
public override Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
{
{"Total Trades", "2"},
{"Average Win", "0%"},
{"Average Loss", "-0.12%"},
{"Compounding Annual Return", "-13.200%"},
{"Drawdown", "0.200%"},
{"Expectancy", "-1"},
{"Net Profit", "-0.116%"},
{"Sharpe Ratio", "0"},
{"Probabilistic Sharpe Ratio", "0%"},
{"Loss Rate", "100%"},
{"Win Rate", "0%"},
{"Profit-Loss Ratio", "0"},
{"Alpha", "0"},
{"Beta", "0"},
{"Annual Standard Deviation", "0"},
{"Annual Variance", "0"},
{"Information Ratio", "2.474"},
{"Tracking Error", "0.339"},
{"Treynor Ratio", "0"},
{"Total Fees", "$0.00"},
{"Estimated Strategy Capacity", "$83000.00"},
{"Lowest Capacity Asset", "BTCUSD XJ"},
{"Fitness Score", "0.011"},
{"Kelly Criterion Estimate", "0"},
{"Kelly Criterion Probability Value", "0"},
{"Sortino Ratio", "79228162514264337593543950335"},
{"Return Over Maximum Drawdown", "-113.513"},
{"Portfolio Turnover", "0.023"},
{"Total Insights Generated", "0"},
{"Total Insights Closed", "0"},
{"Total Insights Analysis Completed", "0"},
{"Long Insight Count", "0"},
{"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", "2b94bc50a74caebe06c075cdab1bc6da"}
};
}
}