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ContinuousFutureRegressionAlgorithm.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.Linq;
using QuantConnect.Data;
using QuantConnect.Orders;
using QuantConnect.Interfaces;
using QuantConnect.Securities;
using System.Collections.Generic;
using QuantConnect.Securities.Future;
using QuantConnect.Data.UniverseSelection;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Continuous Futures Regression algorithm. Asserting and showcasing the behavior of adding a continuous future
/// </summary>
public class ContinuousFutureRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private List<Symbol> _previousMappedContractSymbols = new();
private Symbol _currentMappedSymbol;
private Future _continuousContract;
private DateTime _lastMonth;
/// <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(2013, 7, 1);
SetEndDate(2014, 1, 1);
_continuousContract = AddFuture(Futures.Indices.SP500EMini,
dataNormalizationMode: DataNormalizationMode.BackwardsRatio,
dataMappingMode: DataMappingMode.LastTradingDay,
contractDepthOffset: 0
);
}
/// <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)
{
// we subtract a minute cause we can get data on the market close, from the previous minute
if (!_continuousContract.Exchange.DateTimeIsOpen(Time.AddMinutes(-1)))
{
if (slice.Bars.Count > 0 || slice.QuoteBars.Count > 0)
{
throw new RegressionTestException($"We are getting data during closed market!");
}
}
var currentlyMappedSecurity = Securities[_continuousContract.Mapped];
if (slice.Keys.Count != 1)
{
throw new RegressionTestException($"We are getting data for more than one symbols! {string.Join(",", slice.Keys.Select(symbol => symbol))}");
}
foreach (var changedEvent in slice.SymbolChangedEvents.Values)
{
if (changedEvent.Symbol == _continuousContract.Symbol)
{
_previousMappedContractSymbols.Add(Symbol(changedEvent.OldSymbol));
Log($"{Time} - SymbolChanged event: {changedEvent}");
if (_currentMappedSymbol == _continuousContract.Mapped)
{
throw new RegressionTestException($"Continuous contract current symbol did not change! {_continuousContract.Mapped}");
}
var currentExpiration = changedEvent.Symbol.Underlying.ID.Date;
var frontMonthExpiration = FuturesExpiryFunctions.FuturesExpiryFunction(_continuousContract.Symbol)(Time.AddMonths(1));
if (currentExpiration != frontMonthExpiration.Date)
{
throw new RegressionTestException($"Unexpected current mapped contract expiration {currentExpiration}" +
$" @ {Time} it should be AT front month expiration {frontMonthExpiration}");
}
}
}
if (_lastMonth.Month != Time.Month && currentlyMappedSecurity.HasData)
{
_lastMonth = Time;
Log($"{Time}- {currentlyMappedSecurity.GetLastData()}");
if (Portfolio.Invested)
{
Liquidate();
}
else
{
// This works because we set this contract as tradable, even if it's a canonical security
Buy(currentlyMappedSecurity.Symbol, 1);
}
if(Time.Month == 1 && Time.Year == 2013)
{
var response = History(new[] { _continuousContract.Symbol }, 60 * 24 * 90);
if (!response.Any())
{
throw new RegressionTestException("Unexpected empty history response");
}
}
}
_currentMappedSymbol = _continuousContract.Mapped;
}
public override void OnOrderEvent(OrderEvent orderEvent)
{
if (orderEvent.Status == OrderStatus.Filled)
{
Log($"{orderEvent}");
}
}
public override void OnSecuritiesChanged(SecurityChanges changes)
{
Debug($"{Time}-{changes}");
if (changes.AddedSecurities.Any(security => security.Symbol != _continuousContract.Symbol)
|| changes.RemovedSecurities.Any(security => security.Symbol != _continuousContract.Symbol))
{
throw new RegressionTestException($"We got an unexpected security changes {changes}");
}
}
public override void OnEndOfAlgorithm()
{
var expectedMappingCounts = 2;
if (_previousMappedContractSymbols.Count != expectedMappingCounts)
{
throw new RegressionTestException($"Unexpected symbol changed events: {_previousMappedContractSymbols.Count}, was expecting {expectedMappingCounts}");
}
var delistedSecurities = _previousMappedContractSymbols
.Select(x => Securities.Total.Single(sec => sec.Symbol == x))
.Where(x => x.Symbol.ID.Date < Time)
.ToList();
var markedDelistedSecurities = delistedSecurities.Where(x => x.IsDelisted && !x.IsTradable).ToList();
if (markedDelistedSecurities.Count != delistedSecurities.Count)
{
throw new RegressionTestException($"Not all delisted contracts are properly market as delisted and non-tradable: " +
$"only {markedDelistedSecurities.Count} are marked, was expecting {delistedSecurities.Count}");
}
}
/// <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 => 162575;
/// <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", "0.84%"},
{"Average Loss", "0%"},
{"Compounding Annual Return", "3.380%"},
{"Drawdown", "1.900%"},
{"Expectancy", "0"},
{"Start Equity", "100000"},
{"End Equity", "101687.3"},
{"Net Profit", "1.687%"},
{"Sharpe Ratio", "0.605"},
{"Sortino Ratio", "0.202"},
{"Probabilistic Sharpe Ratio", "45.198%"},
{"Loss Rate", "0%"},
{"Win Rate", "100%"},
{"Profit-Loss Ratio", "0"},
{"Alpha", "-0.013"},
{"Beta", "0.134"},
{"Annual Standard Deviation", "0.027"},
{"Annual Variance", "0.001"},
{"Information Ratio", "-2.687"},
{"Tracking Error", "0.075"},
{"Treynor Ratio", "0.121"},
{"Total Fees", "$6.45"},
{"Estimated Strategy Capacity", "$2600000000.00"},
{"Lowest Capacity Asset", "ES VMKLFZIH2MTD"},
{"Portfolio Turnover", "1.88%"},
{"Drawdown Recovery", "16"},
{"OrderListHash", "1973b0beb9bc5e618e0387d960553d7a"}
};
}
}