/*
* 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.Data;
using QuantConnect.Data.UniverseSelection;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
///
/// Assert that ETF universe selection happens right away after algorithm starts
///
public class ETFConstituentUniverseImmediateSelectionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private List _constituents = new();
private Symbol _spy;
private bool _filtered;
private bool _securitiesChanged;
private bool _firstOnData = true;
///
/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
///
public override void Initialize()
{
SetStartDate(2020, 12, 1);
SetEndDate(2021, 1, 31);
SetCash(100000);
UniverseSettings.Resolution = Resolution.Hour;
_spy = AddEquity("SPY", Resolution.Hour).Symbol;
AddUniverse(Universe.ETF(_spy, universeFilterFunc: FilterETFs));
}
///
/// Filters ETFs, performing some sanity checks
///
/// Constituents of the ETF universe added above
/// Constituent Symbols to add to algorithm
/// Constituents collection was not structured as expected
private IEnumerable FilterETFs(IEnumerable constituents)
{
_filtered = true;
_constituents = constituents.Select(x => x.Symbol).Distinct().ToList();
return _constituents;
}
///
/// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
///
/// Slice object keyed by symbol containing the stock data
public override void OnData(Slice slice)
{
if (_firstOnData)
{
if (!_filtered)
{
throw new RegressionTestException("Universe selection should have been triggered right away. " +
"The first OnData call should have had happened after the universe selection");
}
_firstOnData = false;
}
}
///
/// Checks if new securities have been added to the algorithm after universe selection has occurred
///
/// Security changes
/// Expected number of stocks were not added to the algorithm
public override void OnSecuritiesChanged(SecurityChanges changes)
{
if (!_filtered)
{
throw new RegressionTestException("Universe selection should have been triggered right away");
}
if (!_securitiesChanged)
{
// Selection should be happening right on algorithm start
if (Time != StartDate)
{
throw new RegressionTestException("Universe selection should have been triggered right away");
}
// All constituents should have been added to the algorithm.
// Plus the ETF itself.
if (changes.AddedSecurities.Count != _constituents.Count + 1)
{
throw new RegressionTestException($"Expected {_constituents.Count + 1} stocks to be added to the algorithm, " +
$"instead added: {changes.AddedSecurities.Count}");
}
if (!_constituents.All(constituent => changes.AddedSecurities.Any(security => security.Symbol == constituent)))
{
throw new RegressionTestException("Not all constituents were added to the algorithm");
}
_securitiesChanged = true;
}
}
///
/// Ensures that all expected events were triggered by the end of the algorithm
///
/// An expected event didn't happen
public override void OnEndOfAlgorithm()
{
if (_firstOnData || !_filtered || !_securitiesChanged)
{
throw new RegressionTestException("Expected events didn't happen");
}
}
///
/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
///
public bool CanRunLocally { get; } = true;
///
/// This is used by the regression test system to indicate which languages this algorithm is written in.
///
public List Languages { get; } = new() { Language.CSharp };
///
/// Data Points count of all timeslices of algorithm
///
public long DataPoints => 2722;
///
/// Data Points count of the algorithm history
///
public int AlgorithmHistoryDataPoints => 0;
///
/// Final status of the algorithm
///
public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;
///
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
///
public Dictionary ExpectedStatistics => new Dictionary
{
{"Total Orders", "0"},
{"Average Win", "0%"},
{"Average Loss", "0%"},
{"Compounding Annual Return", "0%"},
{"Drawdown", "0%"},
{"Expectancy", "0"},
{"Start Equity", "100000"},
{"End Equity", "100000"},
{"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", "-0.695"},
{"Tracking Error", "0.105"},
{"Treynor Ratio", "0"},
{"Total Fees", "$0.00"},
{"Estimated Strategy Capacity", "$0"},
{"Lowest Capacity Asset", ""},
{"Portfolio Turnover", "0%"},
{"Drawdown Recovery", "0"},
{"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
};
}
}