wehub-resource-sync
273 lines
11 KiB
C#
273 lines
11 KiB
C#
/*
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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 System;
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using System.Linq;
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using QuantConnect.Data;
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using QuantConnect.Securities;
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using QuantConnect.Interfaces;
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using System.Collections.Generic;
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using QuantConnect.Data.Fundamental;
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using QuantConnect.Data.UniverseSelection;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Demonstration of how to define a universe using the fundamental data
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/// </summary>
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public class FundamentalRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private const int NumberOfSymbolsFundamental = 2;
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private SecurityChanges _changes = SecurityChanges.None;
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private Universe _universe;
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public override void Initialize()
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{
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UniverseSettings.Resolution = Resolution.Daily;
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SetStartDate(2014, 03, 26);
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SetEndDate(2014, 04, 07);
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_universe = AddUniverse(FundamentalSelectionFunction);
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// before we add any symbol
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AssertFundamentalUniverseData();
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AddEquity("SPY");
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AddEquity("AAPL");
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// Request fundamental data for symbols at current algorithm time
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var ibm = QuantConnect.Symbol.Create("IBM", SecurityType.Equity, Market.USA);
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var ibmFundamental = Fundamentals(ibm);
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if (Time != StartDate || Time != ibmFundamental.EndTime)
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{
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throw new RegressionTestException($"Unexpected {nameof(Fundamental)} time {ibmFundamental.EndTime}");
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}
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if (ibmFundamental.Price == 0)
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{
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throw new RegressionTestException($"Unexpected {nameof(Fundamental)} IBM price!");
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}
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var nb = QuantConnect.Symbol.Create("NB", SecurityType.Equity, Market.USA);
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var fundamentals = Fundamentals(new List<Symbol>{ nb, ibm }).ToList();
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if (fundamentals.Count != 2)
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{
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throw new RegressionTestException($"Unexpected {nameof(Fundamental)} count {fundamentals.Count}! Expected 2");
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}
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// Request historical fundamental data for symbols
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var history = History<Fundamental>(Securities.Keys, new TimeSpan(2, 0, 0, 0)).ToList();
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if(history.Count != 2)
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{
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throw new RegressionTestException($"Unexpected {nameof(Fundamental)} history count {history.Count}! Expected 2");
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}
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if (history[0].Values.Count != 2)
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{
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throw new RegressionTestException($"Unexpected {nameof(Fundamental)} data count {history[0].Values.Count}, expected 2!");
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}
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// assert all fundamental API data match
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foreach (var ticker in new[] {"AAPL", "SPY"})
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{
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var fundamentalThroughSecurity = Securities[ticker].Fundamentals;
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var fundamentalThroughAlgo = Fundamentals(ticker);
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if (!history[1].TryGetValue(ticker, out var fundamental) || fundamental.Price == 0
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|| fundamentalThroughSecurity.Price != fundamental.Price
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|| fundamentalThroughSecurity.EndTime != fundamental.EndTime
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|| fundamentalThroughAlgo.Price != fundamental.Price
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|| fundamentalThroughAlgo.EndTime != fundamental.EndTime)
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{
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throw new RegressionTestException($"Unexpected {ticker} fundamental data");
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}
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}
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AssertFundamentalUniverseData();
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}
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private void AssertFundamentalUniverseData()
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{
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// we run it twice just to match the history request data point count with the python version which has 1 extra different api test/assert
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for (var i = 0; i < 2; i++)
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{
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// Request historical fundamental data for all symbols, passing the universe instance
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var universeDataPerTime = History(_universe, new TimeSpan(2, 0, 0, 0)).ToList();
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if (universeDataPerTime.Count != 2)
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{
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throw new RegressionTestException($"Unexpected {nameof(Fundamentals)} history count {universeDataPerTime.Count}! Expected 1");
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}
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foreach (var universeDataCollection in universeDataPerTime)
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{
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AssertFundamentalEnumerator(universeDataCollection, "1");
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}
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}
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// Passing through the unvierse type and symbol
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var enumerableOfDataDictionary = History<FundamentalUniverse>(new[] { _universe.Symbol }, 100);
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foreach (var selectionCollectionForADay in enumerableOfDataDictionary)
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{
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AssertFundamentalEnumerator(selectionCollectionForADay[_universe.Symbol], "2");
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}
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}
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private void AssertFundamentalEnumerator(IEnumerable<BaseData> enumerable, string caseName)
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{
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var dataPointCount = 0;
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// note we need to cast to Fundamental type
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foreach (Fundamental fundamental in enumerable)
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{
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dataPointCount++;
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}
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if (dataPointCount < 7000)
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{
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throw new RegressionTestException($"Unexpected historical {nameof(Fundamentals)} data count {dataPointCount} case {caseName}! Expected > 7000");
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}
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}
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// sort the data by daily dollar volume and take the top 'NumberOfSymbolsCoarse'
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public IEnumerable<Symbol> FundamentalSelectionFunction(IEnumerable<Fundamental> fundamental)
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{
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// select only symbols with fundamental data and sort descending by daily dollar volume
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var sortedByDollarVolume = fundamental
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.Where(x => x.Price > 1)
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.OrderByDescending(x => x.DollarVolume);
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// sort descending by P/E ratio
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var sortedByPeRatio = sortedByDollarVolume.OrderByDescending(x => x.ValuationRatios.PERatio);
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// take the top entries from our sorted collection
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var topFine = sortedByPeRatio.Take(NumberOfSymbolsFundamental).ToArray();
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// selection fundamental data should match all other APIs
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foreach (var fundamentalPoint in topFine)
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{
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var symbol = fundamentalPoint.Symbol;
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if (fundamentalPoint.Price == 0)
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{
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throw new RegressionTestException($"Unexpected {symbol} fundamental data in selection");
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}
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if (UniverseSettings.Asynchronous.HasValue && UniverseSettings.Asynchronous.Value)
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{
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continue;
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}
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var fundamentalThroughSecurity = Securities.ContainsKey(symbol) ? Securities[symbol].Fundamentals : null;
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var fundamentalThroughAlgo = Fundamentals(symbol);
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if (fundamentalThroughSecurity != null && (fundamentalThroughSecurity.Price != fundamentalPoint.Price
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|| fundamentalThroughSecurity.EndTime != fundamentalPoint.EndTime)
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|| fundamentalThroughAlgo != null && (fundamentalThroughAlgo.Price != fundamentalPoint.Price
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|| fundamentalThroughAlgo.EndTime != fundamentalPoint.EndTime))
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{
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throw new RegressionTestException($"Unexpected {symbol} fundamental data in selection");
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}
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}
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// we need to return only the symbol objects
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return topFine.Select(x => x.Symbol);
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}
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public override void OnData(Slice slice)
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{
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// if we have no changes, do nothing
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if (_changes == SecurityChanges.None) return;
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// liquidate removed securities
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foreach (var security in _changes.RemovedSecurities)
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{
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if (security.Invested)
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{
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Liquidate(security.Symbol);
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}
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}
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// we want allocation in each security in our universe
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foreach (var security in _changes.AddedSecurities)
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{
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SetHoldings(security.Symbol, 0.02m);
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}
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_changes = SecurityChanges.None;
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}
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// this event fires whenever we have changes to our universe
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public override void OnSecuritiesChanged(SecurityChanges changes)
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{
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_changes = changes;
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}
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/// <summary>
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/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
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/// </summary>
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public bool CanRunLocally { get; } = true;
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/// <summary>
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/// This is used by the regression test system to indicate which languages this algorithm is written in.
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/// </summary>
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public List<Language> Languages { get; } = new() { Language.CSharp, Language.Python };
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/// <summary>
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/// Data Points count of all timeslices of algorithm
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/// </summary>
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public long DataPoints => 70954;
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/// <summary>
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/// Data Points count of the algorithm history
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/// </summary>
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public virtual int AlgorithmHistoryDataPoints => 16;
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/// <summary>
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/// Final status of the algorithm
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/// </summary>
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public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;
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/// <summary>
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/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
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/// </summary>
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public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
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{
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{"Total Orders", "3"},
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{"Average Win", "0%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "-1.169%"},
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{"Drawdown", "0.100%"},
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{"Expectancy", "0"},
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{"Start Equity", "100000"},
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{"End Equity", "99958.14"},
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{"Net Profit", "-0.042%"},
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{"Sharpe Ratio", "-3.451"},
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{"Sortino Ratio", "-4.933"},
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{"Probabilistic Sharpe Ratio", "17.672%"},
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{"Loss Rate", "0%"},
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{"Win Rate", "0%"},
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{"Profit-Loss Ratio", "0"},
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{"Alpha", "-0.013"},
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{"Beta", "0.043"},
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{"Annual Standard Deviation", "0.005"},
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{"Annual Variance", "0"},
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{"Information Ratio", "0.607"},
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{"Tracking Error", "0.093"},
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{"Treynor Ratio", "-0.381"},
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{"Total Fees", "$3.00"},
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{"Estimated Strategy Capacity", "$1900000000.00"},
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{"Lowest Capacity Asset", "IBM R735QTJ8XC9X"},
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{"Portfolio Turnover", "0.45%"},
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{"Drawdown Recovery", "4"},
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{"OrderListHash", "6870238e07de15cc14e0116c5094e535"}
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};
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}
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}
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