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.Collections.Generic;
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using System.Linq;
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using QuantConnect.Brokerages;
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using QuantConnect.Data;
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using QuantConnect.Data.UniverseSelection;
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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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/// Regression algorithm asserting that a currency added at runtime (here BTCEUR, from a scheduled event) has its
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/// conversion rate seeded right away, so using it immediately no longer throws because the rate is still 0.
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/// </summary>
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public class RuntimeCurrencyConversionSeedingRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private Symbol _ltcusd;
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private bool _addedAtRuntime;
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private bool _assertedSeeded;
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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(2018, 4, 5);
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SetEndDate(2018, 4, 5);
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SetBrokerageModel(BrokerageName.GDAX, AccountType.Cash);
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SetCash(100000);
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// Account currency asset that funds the loop
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_ltcusd = AddCrypto("LTCUSD", Resolution.Minute).Symbol;
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// Add a non-account-currency asset at runtime, mirroring users that add assets from a scheduled event
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Schedule.On(DateRules.EveryDay(), TimeRules.At(10, 0), () =>
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{
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if (_addedAtRuntime)
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{
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return;
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}
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_addedAtRuntime = true;
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AddCrypto("BTCEUR", Resolution.Minute);
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});
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}
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/// <summary>
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/// Runs right after the runtime-added security is wired up, the earliest point it can be used
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/// </summary>
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public override void OnSecuritiesChanged(SecurityChanges changes)
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{
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if (!changes.AddedSecurities.Any(security => security.Symbol.Value == "BTCEUR"))
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{
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return;
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}
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_assertedSeeded = true;
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// With the fix these are already seeded here. Without it they would still be 0 and the conversion below would throw.
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var eur = Portfolio.CashBook["EUR"];
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var btc = Portfolio.CashBook["BTC"];
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if (eur.ConversionRate == 0 || btc.ConversionRate == 0)
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{
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throw new RegressionTestException(
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$"Runtime-added currency conversion rates were not seeded (EUR={eur.ConversionRate}, BTC={btc.ConversionRate})");
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}
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if (Portfolio.CashBook.ConvertToAccountCurrency(100m, "EUR") <= 0)
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{
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throw new RegressionTestException("Expected a positive EUR -> account currency conversion");
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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 (!_addedAtRuntime || Portfolio.Invested)
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{
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return;
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}
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if (Securities[_ltcusd].Price != 0)
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{
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SetHoldings(_ltcusd, 0.5);
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}
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}
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/// <summary>
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/// Makes sure the seeding path was actually exercised so the test can't silently pass
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/// </summary>
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public override void OnEndOfAlgorithm()
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{
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if (!_assertedSeeded)
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{
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throw new RegressionTestException("BTCEUR was never added at runtime, the seeding path was not exercised");
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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 => 6005;
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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 => 591;
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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", "1"},
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{"Average Win", "0%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "0%"},
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{"Drawdown", "0%"},
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{"Expectancy", "0"},
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{"Start Equity", "100000.00"},
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{"End Equity", "99064.52"},
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{"Net Profit", "0%"},
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{"Sharpe Ratio", "0"},
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{"Sortino Ratio", "0"},
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{"Probabilistic Sharpe Ratio", "0%"},
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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"},
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{"Beta", "0"},
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{"Annual Standard Deviation", "0"},
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{"Annual Variance", "0"},
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{"Information Ratio", "0"},
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{"Tracking Error", "0"},
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{"Treynor Ratio", "0"},
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{"Total Fees", "$149.18"},
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{"Estimated Strategy Capacity", "$160000.00"},
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{"Lowest Capacity Asset", "LTCUSD 2XR"},
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{"Portfolio Turnover", "50.20%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "69d27a394cffbd938ec23fbb451f37ae"}
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};
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}
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}
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