chore: import upstream snapshot with attribution
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wehub-resource-sync
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/*
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* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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using MathNet.Numerics.Statistics;
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using QuantConnect.Data;
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using QuantConnect.Data.UniverseSelection;
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using System;
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using System.Collections.Generic;
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using System.Linq;
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namespace QuantConnect.Algorithm.Framework.Alphas
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{
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/// <summary>
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/// This alpha model is designed to rank every pair combination by its pearson correlation
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/// and trade the pair with the hightest correlation
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/// This model generates alternating long ratio/short ratio insights emitted as a group
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/// </summary>
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public class PearsonCorrelationPairsTradingAlphaModel : BasePairsTradingAlphaModel
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{
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private readonly int _lookback;
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private readonly Resolution _resolution;
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private readonly double _minimumCorrelation;
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private Tuple<Symbol, Symbol> _bestPair;
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/// <summary>
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/// Initializes a new instance of the <see cref="PearsonCorrelationPairsTradingAlphaModel"/> class
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/// </summary>
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/// <param name="lookback">Lookback period of the analysis</param>
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/// <param name="resolution">Analysis resolution</param>
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/// <param name="threshold">The percent [0, 100] deviation of the ratio from the mean before emitting an insight</param>
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/// <param name="minimumCorrelation">The minimum correlation to consider a tradable pair</param>
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public PearsonCorrelationPairsTradingAlphaModel(int lookback = 15, Resolution resolution = Resolution.Minute, decimal threshold = 1m, double minimumCorrelation = .5)
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: base(lookback, resolution, threshold)
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{
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_lookback = lookback;
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_resolution = resolution;
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_minimumCorrelation = minimumCorrelation;
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}
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/// <summary>
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/// Event fired each time the we add/remove securities from the data feed
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/// </summary>
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/// <param name="algorithm">The algorithm instance that experienced the change in securities</param>
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/// <param name="changes">The security additions and removals from the algorithm</param>
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public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
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{
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NotifiedSecurityChanges.UpdateCollection(Securities, changes);
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var symbols = Securities.Select(x => x.Symbol).ToArray();
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var history = algorithm.History(symbols, _lookback, _resolution);
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var vectors = GetPriceVectors(history);
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if (vectors.LongLength == 0)
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{
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algorithm.Debug($"PearsonCorrelationPairsTradingAlphaModel.OnSecuritiesChanged(): The requested historical data does not have series of prices with the same date/time. Please consider increasing the looback period. Current lookback: {_lookback}");
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}
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else
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{
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var pearsonMatrix = Correlation.PearsonMatrix(vectors).UpperTriangle();
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var maxValue = pearsonMatrix.Enumerate().Where(x => Math.Abs(x) < 1).Max();
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if (maxValue >= _minimumCorrelation)
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{
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var maxTuple = pearsonMatrix.Find(x => x == maxValue);
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_bestPair = Tuple.Create(symbols[maxTuple.Item1], symbols[maxTuple.Item2]);
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}
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}
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base.OnSecuritiesChanged(algorithm, changes);
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}
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/// <summary>
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/// Check whether the assets pass a pairs trading test
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/// </summary>
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/// <param name="algorithm">The algorithm instance that experienced the change in securities</param>
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/// <param name="asset1">The first asset's symbol in the pair</param>
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/// <param name="asset2">The second asset's symbol in the pair</param>
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/// <returns>True if the statistical test for the pair is successful</returns>
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public override bool HasPassedTest(QCAlgorithm algorithm, Symbol asset1, Symbol asset2)
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{
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// Check if this method was overridden in Python
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if (TryInvokePythonOverride(nameof(HasPassedTest), out bool result, algorithm, asset1, asset2))
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{
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return result;
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}
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return _bestPair != null && asset1 == _bestPair.Item1 && asset2 == _bestPair.Item2;
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}
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private double[][] GetPriceVectors(IEnumerable<Slice> slices)
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{
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var symbols = Securities.Select(x => x.Symbol).ToArray();
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var timeZones = Securities.ToDictionary(x => x.Symbol, y => y.Exchange.TimeZone);
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// Special case: daily data and securities from different timezone
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var isDailyAndMultipleTimeZone = _resolution == Resolution.Daily && timeZones.Values.Distinct().Count() > 1;
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var bars = new List<BaseData>();
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if (isDailyAndMultipleTimeZone)
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{
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bars.AddRange(slices
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.GroupBy(x => x.Time.Date)
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.Where(x => x.Sum(k => k.Count) == symbols.Length)
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.SelectMany(x => x.SelectMany(y => y.Values)));
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}
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else
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{
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bars.AddRange(slices
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.Where(x => x.Count == symbols.Length)
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.SelectMany(x => x.Values));
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}
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return bars
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.GroupBy(x => x.Symbol)
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.Select(x =>
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{
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var array = x.Select(b => Math.Log((double)b.Price)).ToArray();
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if (array.Length > 1)
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{
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for (var i = array.Length - 1; i > 0; i--)
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{
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array[i] = array[i] - array[i - 1];
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}
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array[0] = array[1];
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return array;
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}
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else
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{
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return new double[0];
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}
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}).ToArray();
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}
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}
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}
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