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CustomDataUniverseScheduledRegressionAlgorithm.cs
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/
CustomDataUniverseScheduledRegressionAlgorithm.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.Interfaces;
using System.Collections.Generic;
using QuantConnect.Data.UniverseSelection;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Custom data universe selection regression algorithm asserting it's behavior. Similar to CustomDataUniverseRegressionAlgorithm but with a custom schedule
/// </summary>
public class CustomDataUniverseScheduledRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private readonly Queue<DateTime> _selectionTime = new(new[] {
new DateTime(2014, 03, 25, 0, 0, 0),
new DateTime(2014, 03, 27, 0, 0, 0),
new DateTime(2014, 03, 29, 0, 0, 0)
});
/// <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(2014, 03, 24);
SetEndDate(2014, 03, 31);
UniverseSettings.Resolution = Resolution.Daily;
UniverseSettings.Schedule.On(DateRules.On(_selectionTime.ToArray()));
AddUniverse<CoarseFundamental>("custom-data-universe", UniverseSettings, (coarse) =>
{
Debug($"Universe selection called: {Time} Count: {coarse.Count()}");
var expectedTime = _selectionTime.Dequeue();
if (expectedTime != Time)
{
throw new Exception($"Unexpected selection time {Time} expected {expectedTime}");
}
return coarse.OfType<CoarseFundamental>().OrderByDescending(x => x.DollarVolume)
.SelectMany(x => new[] {
x.Symbol,
QuantConnect.Symbol.CreateBase(typeof(CustomData), x.Symbol)})
.Take(20);
});
// This use case is also valid/same because it will use the algorithm settings by default
// AddUniverse<CoarseFundamental>("custom-data-universe", (coarse) => {});
}
/// <summary>
/// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
/// </summary>
/// <param name="data">Slice object keyed by symbol containing the stock data</param>
public override void OnData(Slice data)
{
if (!Portfolio.Invested)
{
var customData = data.Get<CustomData>();
var symbols = data.Keys.Where(symbol => symbol.SecurityType != SecurityType.Base).ToList();
foreach (var symbol in symbols)
{
SetHoldings(symbol, 1m / symbols.Count);
if (!customData.Any(custom => custom.Key.Underlying == symbol))
{
throw new Exception($"Custom data was not found for underlying symbol {symbol}");
}
}
}
}
public override void OnEndOfAlgorithm()
{
if (_selectionTime.Count != 0)
{
throw new Exception($"Unexpected selection times, missing {_selectionTime.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 Language[] Languages { get; } = { Language.CSharp, Language.Python };
/// <summary>
/// Data Points count of all timeslices of algorithm
/// </summary>
public long DataPoints => 21382;
/// <summary>
/// Data Points count of the algorithm history
/// </summary>
public int AlgorithmHistoryDataPoints => 0;
/// <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", "7"},
{"Average Win", "0%"},
{"Average Loss", "0%"},
{"Compounding Annual Return", "-65.130%"},
{"Drawdown", "2.900%"},
{"Expectancy", "0"},
{"Start Equity", "100000"},
{"End Equity", "97717.31"},
{"Net Profit", "-2.283%"},
{"Sharpe Ratio", "-4.298"},
{"Sortino Ratio", "-4.067"},
{"Probabilistic Sharpe Ratio", "5.388%"},
{"Loss Rate", "0%"},
{"Win Rate", "0%"},
{"Profit-Loss Ratio", "0"},
{"Alpha", "-1.062"},
{"Beta", "1.336"},
{"Annual Standard Deviation", "0.132"},
{"Annual Variance", "0.018"},
{"Information Ratio", "-12.03"},
{"Tracking Error", "0.078"},
{"Treynor Ratio", "-0.426"},
{"Total Fees", "$13.87"},
{"Estimated Strategy Capacity", "$430000000.00"},
{"Lowest Capacity Asset", "NB R735QTJ8XC9X"},
{"Portfolio Turnover", "12.54%"},
{"OrderListHash", "fae1a7c34d640dfa020330f24378bcf7"}
};
}
}