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Universes immediate selection (#7785)
* Make sure continuous futures universe selection is performed right away on the start of the algorithm. This ensures the Future.Mapped is not null * Minor changes * Minor fixes * Update regression algorithms stats * Add DC futures map file * Update DC futures map file * Update regression algorithms stats * Include ETFs for immediate universe selection * Include Coarse Fundamentals for immediate universe selection * Include Futures Chain Universe for immediate selection * Add Option Chain Universe test * Include custom universes for immediate selection * Minor updates * Include constituents universes for immediate selection * Minor update * Minor changes, docs and stats updates * Minor changes * Minor changes and clean up * Minor regression algorithm stat update
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153 changes: 153 additions & 0 deletions
153
Algorithm.CSharp/CoarseFundamentalImmediateSelectionRegressionAlgorithm.cs
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/* | ||
* 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. | ||
*/ | ||
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using System; | ||
using System.Linq; | ||
using QuantConnect.Interfaces; | ||
using System.Collections.Generic; | ||
using QuantConnect.Data.UniverseSelection; | ||
using QuantConnect.Data; | ||
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namespace QuantConnect.Algorithm.CSharp | ||
{ | ||
/// <summary> | ||
/// Assert that CoarseFundamentals universe selection happens right away after algorithm starts | ||
/// </summary> | ||
public class CoarseFundamentalImmediateSelectionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition | ||
{ | ||
private const int NumberOfSymbols = 3; | ||
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private bool _initialSelectionDone; | ||
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public override void Initialize() | ||
{ | ||
UniverseSettings.Resolution = Resolution.Daily; | ||
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SetStartDate(2014, 03, 25); | ||
SetEndDate(2014, 03, 30); | ||
SetCash(100000); | ||
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AddUniverse(CoarseSelectionFunction); | ||
} | ||
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// sort the data by daily dollar volume and take the top 'NumberOfSymbols' | ||
public IEnumerable<Symbol> CoarseSelectionFunction(IEnumerable<CoarseFundamental> coarse) | ||
{ | ||
if (!_initialSelectionDone) | ||
{ | ||
if (Time != StartDate) | ||
{ | ||
throw new Exception($"CoarseSelectionFunction called at unexpected time. " + | ||
$"Expected it to be called on {StartDate} but was called on {Time}"); | ||
} | ||
} | ||
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// sort descending by daily dollar volume | ||
var sortedByDollarVolume = coarse.OrderByDescending(x => x.DollarVolume); | ||
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// take the top entries from our sorted collection | ||
var top = sortedByDollarVolume.Take(NumberOfSymbols); | ||
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// we need to return only the symbol objects | ||
return top.Select(x => x.Symbol); | ||
} | ||
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public void OnData(Slice data) | ||
{ | ||
Log($"OnData({UtcTime:o}): Keys: {string.Join(", ", data.Keys.OrderBy(x => x))}"); | ||
} | ||
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public override void OnSecuritiesChanged(SecurityChanges changes) | ||
{ | ||
Log($"OnSecuritiesChanged({UtcTime:o}):: {changes}"); | ||
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// This should also happen right away | ||
if (!_initialSelectionDone) | ||
{ | ||
_initialSelectionDone = true; | ||
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if (Time != StartDate) | ||
{ | ||
throw new Exception($"OnSecuritiesChanged called at unexpected time. " + | ||
$"Expected it to be called on {StartDate} but was called on {Time}"); | ||
} | ||
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if (changes.AddedSecurities.Count != NumberOfSymbols) | ||
{ | ||
throw new Exception($"Unexpected number of added securities. " + | ||
$"Expected {NumberOfSymbols} but was {changes.AddedSecurities.Count}"); | ||
} | ||
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if (changes.RemovedSecurities.Count != 0) | ||
{ | ||
throw new Exception($"Unexpected number of removed securities. " + | ||
$"Expected 0 but was {changes.RemovedSecurities.Count}"); | ||
} | ||
} | ||
} | ||
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/// <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; | ||
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/// <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 }; | ||
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/// <summary> | ||
/// Data Points count of all timeslices of algorithm | ||
/// </summary> | ||
public long DataPoints => 35405; | ||
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/// <summary> | ||
/// Data Points count of the algorithm history | ||
/// </summary> | ||
public int AlgorithmHistoryDataPoints => 0; | ||
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/// <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", "0"}, | ||
{"Average Win", "0%"}, | ||
{"Average Loss", "0%"}, | ||
{"Compounding Annual Return", "0%"}, | ||
{"Drawdown", "0%"}, | ||
{"Expectancy", "0"}, | ||
{"Net Profit", "0%"}, | ||
{"Sharpe Ratio", "0"}, | ||
{"Sortino Ratio", "0"}, | ||
{"Probabilistic Sharpe Ratio", "0%"}, | ||
{"Loss Rate", "0%"}, | ||
{"Win Rate", "0%"}, | ||
{"Profit-Loss Ratio", "0"}, | ||
{"Alpha", "0"}, | ||
{"Beta", "0"}, | ||
{"Annual Standard Deviation", "0"}, | ||
{"Annual Variance", "0"}, | ||
{"Information Ratio", "3.134"}, | ||
{"Tracking Error", "0.097"}, | ||
{"Treynor Ratio", "0"}, | ||
{"Total Fees", "$0.00"}, | ||
{"Estimated Strategy Capacity", "$0"}, | ||
{"Lowest Capacity Asset", ""}, | ||
{"Portfolio Turnover", "0%"}, | ||
{"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"} | ||
}; | ||
} | ||
} |
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