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 QuantConnect.Algorithm.Framework.Alphas;
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using QuantConnect.Algorithm.Framework.Execution;
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using QuantConnect.Algorithm.Framework.Portfolio;
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using QuantConnect.Algorithm.Framework.Risk;
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using QuantConnect.Algorithm.Framework.Selection;
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using QuantConnect.Data;
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using QuantConnect.Data.UniverseSelection;
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using QuantConnect.Indicators;
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using QuantConnect.Orders.Fees;
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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.CSharp.Alphas
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{
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/// <summary>
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/// This alpha aims to capture the mean-reversion effect of ETFs during lunch-break by ranking 20 ETFs
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/// on their return between the close of the previous day to 12:00 the day after and predicting mean-reversion
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/// in price during lunch-break.
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///
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/// Source: Lunina, V. (June 2011). The Intraday Dynamics of Stock Returns and Trading Activity: Evidence from OMXS 30 (Master's Essay, Lund University).
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/// Retrieved from http://lup.lub.lu.se/luur/download?func=downloadFile&recordOId=1973850&fileOId=1973852
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///
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/// This alpha is part of the Benchmark Alpha Series created by QuantConnect which are open sourced so the community and client funds can see an example of an alpha.
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///</summary>
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public class MeanReversionLunchBreakAlpha : QCAlgorithm
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{
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public override void Initialize()
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{
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SetStartDate(2018, 1, 1);
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SetCash(100000);
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// Set zero transaction fees
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SetSecurityInitializer(security => security.FeeModel = new ConstantFeeModel(0));
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// Use Hourly Data For Simplicity
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UniverseSettings.Resolution = Resolution.Hour;
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SetUniverseSelection(new CoarseFundamentalUniverseSelectionModel(CoarseSelectionFunction));
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// Use MeanReversionLunchBreakAlphaModel to establish insights
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SetAlpha(new MeanReversionLunchBreakAlphaModel());
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// Equally weigh securities in portfolio, based on insights
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SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
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// Set Immediate Execution Model
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SetExecution(new ImmediateExecutionModel());
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// Set Null Risk Management Model
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SetRiskManagement(new NullRiskManagementModel());
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}
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/// <summary>
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/// Sort the data by daily dollar volume and take the top '20' ETFs
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/// </summary>
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private IEnumerable<Symbol> CoarseSelectionFunction(IEnumerable<CoarseFundamental> coarse)
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{
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return (from cf in coarse
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where !cf.HasFundamentalData
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orderby cf.DollarVolume descending
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select cf.Symbol).Take(20);
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}
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/// <summary>
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/// Uses the price return between the close of previous day to 12:00 the day after to
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/// predict mean-reversion of stock price during lunch break and creates direction prediction
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/// for insights accordingly.
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/// </summary>
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private class MeanReversionLunchBreakAlphaModel : AlphaModel
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{
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private const Resolution _resolution = Resolution.Hour;
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private readonly TimeSpan _predictionInterval;
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private readonly Dictionary<Symbol, SymbolData> _symbolDataBySymbol;
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public MeanReversionLunchBreakAlphaModel(int lookback = 1)
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{
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_predictionInterval = _resolution.ToTimeSpan().Multiply(lookback);
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_symbolDataBySymbol = new Dictionary<Symbol, SymbolData>();
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}
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public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice data)
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{
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foreach (var kvp in _symbolDataBySymbol)
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{
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if (data.Bars.ContainsKey(kvp.Key))
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{
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var bar = data.Bars.GetValue(kvp.Key);
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kvp.Value.Update(bar.EndTime, bar.Close);
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}
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}
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return algorithm.Time.Hour == 12
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? _symbolDataBySymbol.Select(kvp => kvp.Value.Insight)
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: Enumerable.Empty<Insight>();
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}
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public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
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{
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foreach (var security in changes.RemovedSecurities)
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{
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if (_symbolDataBySymbol.ContainsKey(security.Symbol))
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{
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_symbolDataBySymbol.Remove(security.Symbol);
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}
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}
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// Retrieve price history for all securities in the security universe
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// and update the indicators in the SymbolData object
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var symbols = changes.AddedSecurities.Select(x => x.Symbol);
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var history = algorithm.History(symbols, 1, _resolution);
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if (symbols.Count() > 0 && history.Count() == 0)
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{
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algorithm.Debug($"No data on {algorithm.Time}");
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}
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history.PushThrough(bar =>
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{
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SymbolData symbolData;
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if (!_symbolDataBySymbol.TryGetValue(bar.Symbol, out symbolData))
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{
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symbolData = new SymbolData(bar.Symbol, _predictionInterval);
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}
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symbolData.Update(bar.EndTime, bar.Price);
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_symbolDataBySymbol[bar.Symbol] = symbolData;
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});
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}
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/// <summary>
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/// Contains data specific to a symbol required by this model
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/// </summary>
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private class SymbolData
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{
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// Mean value of returns for magnitude prediction
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private readonly SimpleMovingAverage _meanOfPriceChange = new RateOfChangePercent(1).SMA(3);
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// Price change from close price the previous day
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private readonly RateOfChangePercent _priceChange = new RateOfChangePercent(3);
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private readonly Symbol _symbol;
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private readonly TimeSpan _period;
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public Insight Insight
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{
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get
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{
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// Emit "down" insight for the securities that increased in value and
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// emit "up" insight for securities that have decreased in value
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var direction = _priceChange > 0 ? InsightDirection.Down : InsightDirection.Up;
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var magnitude = Convert.ToDouble(Math.Abs(_meanOfPriceChange));
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return Insight.Price(_symbol, _period, direction, magnitude);
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}
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}
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public SymbolData(Symbol symbol, TimeSpan period)
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{
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_symbol = symbol;
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_period = period;
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}
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public bool Update(DateTime time, decimal value)
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{
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return _meanOfPriceChange.Update(time, value) &
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_priceChange.Update(time, value);
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}
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}
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}
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}
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}
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