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 System;
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using System.Collections.Generic;
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using QuantConnect.Data;
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using QuantConnect.Interfaces;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// This regression algorithm aims to test the TotalPortfolioValue,
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/// verifying its correctly updated (GH issue 3272)
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/// </summary>
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public class TotalPortfolioValueRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private List<Symbol> _symbols = new List<Symbol>();
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/// <summary>
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/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
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/// </summary>
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public override void Initialize()
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{
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SetStartDate(2016, 1, 1);
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SetEndDate(2017, 1, 1);
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SetCash(100000);
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var securitiesToAdd = new List<string>
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{
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"SPY", "AAPL", "AAA", "GOOG", "GOOGL", "IBM", "QQQ", "FB", "WM", "WMI", "BAC", "USO", "IWM", "EEM", "BNO", "AIG"
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};
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foreach (var symbolStr in securitiesToAdd)
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{
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var security = AddEquity(symbolStr, Resolution.Daily);
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security.SetLeverage(100);
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_symbols.Add(security.Symbol);
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}
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}
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/// <summary>
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/// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
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/// </summary>
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/// <param name="slice">Slice object keyed by symbol containing the stock data</param>
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public override void OnData(Slice slice)
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{
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if (Portfolio.Invested)
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{
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Liquidate();
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}
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else
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{
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foreach (var symbol in _symbols)
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{
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SetHoldings(symbol, 10m / _symbols.Count);
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}
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// We will add some cash just for testing, users should not do this
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var totalPortfolioValueSnapshot = Portfolio.TotalPortfolioValue;
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var accountCurrencyCash = Portfolio.CashBook[AccountCurrency];
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var existingAmount = accountCurrencyCash.Amount;
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// increase cash amount
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Portfolio.CashBook.Add(AccountCurrency, existingAmount * 1.1m, 1);
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if (totalPortfolioValueSnapshot * 1.1m != Portfolio.TotalPortfolioValue)
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{
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throw new RegressionTestException($"Unexpected TotalPortfolioValue {Portfolio.TotalPortfolioValue}." +
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$" Expected: {totalPortfolioValueSnapshot * 1.1m}");
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}
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// lets remove part of what we added
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Portfolio.CashBook[AccountCurrency].AddAmount(-existingAmount * 0.05m);
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if (totalPortfolioValueSnapshot * 1.05m != Portfolio.TotalPortfolioValue)
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{
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throw new RegressionTestException($"Unexpected TotalPortfolioValue {Portfolio.TotalPortfolioValue}." +
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$" Expected: {totalPortfolioValueSnapshot * 1.05m}");
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}
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// lets set amount back to original value
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Portfolio.CashBook[AccountCurrency].SetAmount(existingAmount);
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if (totalPortfolioValueSnapshot != Portfolio.TotalPortfolioValue)
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{
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throw new RegressionTestException($"Unexpected TotalPortfolioValue {Portfolio.TotalPortfolioValue}." +
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$" Expected: {totalPortfolioValueSnapshot}");
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}
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}
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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 };
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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 => 5331;
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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 int AlgorithmHistoryDataPoints => 0;
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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", "3752"},
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{"Average Win", "0.63%"},
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{"Average Loss", "-0.73%"},
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{"Compounding Annual Return", "-5.190%"},
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{"Drawdown", "58.600%"},
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{"Expectancy", "0.007"},
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{"Start Equity", "100000"},
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{"End Equity", "94814.26"},
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{"Net Profit", "-5.186%"},
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{"Sharpe Ratio", "0.439"},
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{"Sortino Ratio", "0.44"},
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{"Probabilistic Sharpe Ratio", "22.919%"},
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{"Loss Rate", "46%"},
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{"Win Rate", "54%"},
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{"Profit-Loss Ratio", "0.86"},
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{"Alpha", "-0.014"},
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{"Beta", "4.75"},
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{"Annual Standard Deviation", "0.811"},
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{"Annual Variance", "0.658"},
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{"Information Ratio", "0.372"},
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{"Tracking Error", "0.746"},
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{"Treynor Ratio", "0.075"},
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{"Total Fees", "$18907.56"},
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{"Estimated Strategy Capacity", "$410000.00"},
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{"Lowest Capacity Asset", "BNO UN3IMQ2JU1YD"},
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{"Portfolio Turnover", "601.23%"},
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{"Drawdown Recovery", "237"},
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{"OrderListHash", "7e35def0ca91b89579b42cf23ef941e2"}
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};
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}
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}
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