d2d99b1f10
Regression Tests / build (push) Has been cancelled
Build & Test Lean / build (push) Has been cancelled
* 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
118 lines
4.9 KiB
C#
118 lines
4.9 KiB
C#
/*
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* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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using System;
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using System.Collections.Generic;
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using QuantConnect.Data.UniverseSelection;
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using QuantConnect.Data.Custom.AlphaStreams;
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using QuantConnect.Algorithm.Framework.Execution;
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using QuantConnect.Algorithm.Framework.Portfolio;
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using QuantConnect.Algorithm.Framework.Selection;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Example algorithm consuming an alpha streams portfolio state and trading based on it
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/// </summary>
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public class AlphaStreamsUniverseSelectionTemplateAlgorithm : AlphaStreamsBasicTemplateAlgorithm
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{
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/// <summary>
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/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
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/// </summary>
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public override void Initialize()
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{
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SetStartDate(2018, 04, 04);
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SetEndDate(2018, 04, 06);
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SetExecution(new ImmediateExecutionModel());
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Settings.MinimumOrderMarginPortfolioPercentage = 0.01m;
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SetPortfolioConstruction(new EqualWeightingAlphaStreamsPortfolioConstructionModel());
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SetUniverseSelection(new ScheduledUniverseSelectionModel(
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DateRules.EveryDay(),
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TimeRules.Midnight,
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SelectAlphas,
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new UniverseSettings(UniverseSettings)
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{
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SubscriptionDataTypes = new List<Tuple<Type, TickType>>
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{new(typeof(AlphaStreamsPortfolioState), TickType.Trade)},
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FillForward = false,
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}
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));
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}
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private IEnumerable<Symbol> SelectAlphas(DateTime dateTime)
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{
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Log($"SelectAlphas() {Time}");
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foreach (var alphaId in new[] {"623b06b231eb1cc1aa3643a46", "9fc8ef73792331b11dbd5429a"})
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{
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var alphaSymbol = new Symbol(SecurityIdentifier.GenerateBase(typeof(AlphaStreamsPortfolioState), alphaId, Market.USA),
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alphaId);
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yield return alphaSymbol;
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}
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}
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/// <summary>
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/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
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/// </summary>
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public override Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
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{
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{"Total Trades", "2"},
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{"Average Win", "0%"},
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{"Average Loss", "-0.12%"},
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{"Compounding Annual Return", "-13.200%"},
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{"Drawdown", "0.200%"},
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{"Expectancy", "-1"},
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{"Net Profit", "-0.116%"},
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{"Sharpe Ratio", "0"},
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{"Probabilistic Sharpe Ratio", "0%"},
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{"Loss Rate", "100%"},
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{"Win Rate", "0%"},
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{"Profit-Loss Ratio", "0"},
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{"Alpha", "0"},
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{"Beta", "0"},
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{"Annual Standard Deviation", "0"},
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{"Annual Variance", "0"},
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{"Information Ratio", "2.474"},
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{"Tracking Error", "0.339"},
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{"Treynor Ratio", "0"},
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{"Total Fees", "$0.00"},
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{"Estimated Strategy Capacity", "$83000.00"},
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{"Lowest Capacity Asset", "BTCUSD XJ"},
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{"Fitness Score", "0.011"},
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{"Kelly Criterion Estimate", "0"},
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{"Kelly Criterion Probability Value", "0"},
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{"Sortino Ratio", "79228162514264337593543950335"},
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{"Return Over Maximum Drawdown", "-113.513"},
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{"Portfolio Turnover", "0.023"},
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{"Total Insights Generated", "0"},
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{"Total Insights Closed", "0"},
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{"Total Insights Analysis Completed", "0"},
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{"Long Insight Count", "0"},
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{"Short Insight Count", "0"},
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{"Long/Short Ratio", "100%"},
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{"Estimated Monthly Alpha Value", "$0"},
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{"Total Accumulated Estimated Alpha Value", "$0"},
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{"Mean Population Estimated Insight Value", "$0"},
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{"Mean Population Direction", "0%"},
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{"Mean Population Magnitude", "0%"},
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{"Rolling Averaged Population Direction", "0%"},
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{"Rolling Averaged Population Magnitude", "0%"},
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{"OrderListHash", "2b94bc50a74caebe06c075cdab1bc6da"}
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};
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}
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}
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