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chore: import upstream snapshot with attribution

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2026-07-13 13:02:50 +08:00
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Algorithm.CSharp/UniverseSelectionRegressionAlgorithm.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.
*/
using System;
using System.Collections.Generic;
using System.Linq;
using QuantConnect.Data;
using QuantConnect.Data.UniverseSelection;
using QuantConnect.Orders;
using QuantConnect.Securities;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Universe Selection regression algorithm simulates an edge case. In one week, Google listed two new symbols, delisted one of them and changed tickers.
/// </summary>
/// <meta name="tag" content="regression test" />
public class UniverseSelectionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private HashSet<Symbol> _delistedSymbols = new HashSet<Symbol>();
private SecurityChanges _changes;
/// <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(2014, 03, 22); //Set Start Date
SetEndDate(2014, 04, 07); //Set End Date
SetCash(100000); //Set Strategy Cash
// Find more symbols here: http://quantconnect.com/data
// security that exists with no mappings
AddSecurity(SecurityType.Equity, "SPY", Resolution.Daily);
// security that doesn't exist until half way in backtest (comes in as GOOCV)
AddSecurity(SecurityType.Equity, "GOOG", Resolution.Daily);
AddUniverse(coarse =>
{
// select the various google symbols over the period
return from c in coarse
let sym = c.Symbol.Value
where sym == "GOOG" || sym == "GOOCV" || sym == "GOOAV" || sym == "GOOGL"
select c.Symbol;
// Before March 28th 2014:
// - Only GOOG T1AZ164W5VTX existed
// On March 28th 2014
// - GOOAV VP83T1ZUHROL and GOOCV VP83T1ZUHROL are listed
// On April 02nd 2014
// - GOOAV VP83T1ZUHROL is delisted
// - GOOG T1AZ164W5VTX becomes GOOGL
// - GOOCV VP83T1ZUHROL becomes GOOG
});
}
/// <summary>
/// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
/// </summary>
/// <param name="slice">Slice object keyed by symbol containing the stock data</param>
public override void OnData(Slice slice)
{
// can access the current set of active securitie through UniverseManager.ActiveSecurities
Log(Time + ": Active Securities: " + string.Join(", ", UniverseManager.ActiveSecurities.Keys));
// verify we don't receive data for inactive securities
var inactiveSymbols = slice.Keys
.Where(sym => !UniverseManager.ActiveSecurities.ContainsKey(sym))
// on daily data we'll get the last data point and the delisting at the same time
.Where(sym => !slice.Delistings.ContainsKey(sym) || slice.Delistings[sym].Type != DelistingType.Delisted)
.ToList();
if (inactiveSymbols.Any())
{
var symbols = string.Join(", ", inactiveSymbols);
throw new RegressionTestException($"Received data for non-active security: {symbols}.");
}
if (Transactions.OrdersCount == 0)
{
MarketOrder("SPY", 100);
}
foreach (var kvp in slice.Delistings)
{
_delistedSymbols.Add(kvp.Key);
}
if (_changes != null && _changes.AddedSecurities.All(x => slice.Bars.ContainsKey(x.Symbol)))
{
foreach (var security in _changes.AddedSecurities)
{
Log(Time + ": Added Security: " + security.Symbol.ID);
MarketOnOpenOrder(security.Symbol, 100);
}
foreach (var security in _changes.RemovedSecurities)
{
Log(Time + ": Removed Security: " + security.Symbol.ID);
if (!_delistedSymbols.Contains(security.Symbol))
{
MarketOnOpenOrder(security.Symbol, -100);
}
}
_changes = null;
}
}
public override void OnSecuritiesChanged(SecurityChanges changes)
{
_changes = changes;
}
public override void OnOrderEvent(OrderEvent orderEvent)
{
if (orderEvent.Status == OrderStatus.Submitted)
{
Log(Time + ": Submitted: " + Transactions.GetOrderById(orderEvent.OrderId));
}
if (orderEvent.Status.IsFill())
{
Log(Time + ": Filled: " + Transactions.GetOrderById(orderEvent.OrderId));
}
}
public override void OnEndOfAlgorithm()
{
foreach (var security in Portfolio.Securities.Values.Where(x => x.Invested))
{
// At the end, we should hold 100 shares of:
// - SPY (bought on March, 25th 2014),
// - GOOG T1AZ164W5VTX (bought on March, 26th 2014),
// - GOOCV VP83T1ZUHROL (bought on March, 28th 2014).
AssertQuantity(security, 100);
}
}
private void AssertQuantity(Security security, int expected)
{
var actual = security.Holdings.Quantity;
if (actual != expected)
{
var symbol = security.Symbol;
throw new RegressionTestException($"{symbol}({symbol.ID}) expected {expected.ToStringInvariant()}, but received {actual.ToStringInvariant()}.");
}
}
/// <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 List<Language> Languages { get; } = new() { Language.CSharp, Language.Python };
/// <summary>
/// Data Points count of all timeslices of algorithm
/// </summary>
public long DataPoints => 78092;
/// <summary>
/// Data Points count of the algorithm history
/// </summary>
public int AlgorithmHistoryDataPoints => 0;
/// <summary>
/// Final status of the algorithm
/// </summary>
public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;
/// <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 Orders", "4"},
{"Average Win", "0.14%"},
{"Average Loss", "0%"},
{"Compounding Annual Return", "-59.145%"},
{"Drawdown", "4.100%"},
{"Expectancy", "0"},
{"Start Equity", "100000"},
{"End Equity", "95916.61"},
{"Net Profit", "-4.083%"},
{"Sharpe Ratio", "-2.753"},
{"Sortino Ratio", "-3.15"},
{"Probabilistic Sharpe Ratio", "12.292%"},
{"Loss Rate", "0%"},
{"Win Rate", "100%"},
{"Profit-Loss Ratio", "0"},
{"Alpha", "-0.306"},
{"Beta", "1.175"},
{"Annual Standard Deviation", "0.175"},
{"Annual Variance", "0.031"},
{"Information Ratio", "-2.391"},
{"Tracking Error", "0.139"},
{"Treynor Ratio", "-0.41"},
{"Total Fees", "$3.00"},
{"Estimated Strategy Capacity", "$120000000.00"},
{"Lowest Capacity Asset", "GOOAV VP83T1ZUHROL"},
{"Portfolio Turnover", "11.26%"},
{"Drawdown Recovery", "0"},
{"OrderListHash", "b9c45830fc218afd9de9ce729afc6200"}
};
}
}