wehub-resource-sync
121 lines
4.8 KiB
C#
121 lines
4.8 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 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.Selection;
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using QuantConnect.Interfaces;
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using QuantConnect.Orders;
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using System;
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using System.Collections.Generic;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Regression algorithm to test ImmediateExecutionModel places orders with the
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/// correct quantity (taking into account the fee's) so that the fill quantity
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/// is the expected one.
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/// </summary>
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public class ImmediateExecutionModelWorksWithBinanceFeeModel: QCAlgorithm, IRegressionAlgorithmDefinition
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{
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public override void Initialize()
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{
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SetStartDate(2022, 12, 13);
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SetEndDate(2022, 12, 14);
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SetAccountCurrency("BUSD");
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SetCash("BUSD", 100000, 1);
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UniverseSettings.Resolution = Resolution.Minute;
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var symbols = new List<Symbol>() { QuantConnect.Symbol.Create("BTCBUSD", SecurityType.Crypto, Market.Binance) };
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SetUniverseSelection(new ManualUniverseSelectionModel(symbols));
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SetAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromMinutes(20), 0.025, null));
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SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel(Resolution.Minute));
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SetExecution(new ImmediateExecutionModel());
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SetBrokerageModel(Brokerages.BrokerageName.Binance, AccountType.Margin);
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}
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public override void OnOrderEvent(OrderEvent orderEvent)
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{
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if (orderEvent.Status == OrderStatus.Filled)
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{
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if (Math.Abs(orderEvent.Quantity - 5.8m) > 0.01m)
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{
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throw new RegressionTestException($"The expected quantity was {5.8m} but the quantity from the order was {orderEvent.Quantity}");
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}
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}
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}
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public bool CanRunLocally => 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 => 2882;
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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 => 5;
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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", "103411.39"},
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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", "BUSD99.75"},
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{"Estimated Strategy Capacity", "BUSD600000.00"},
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{"Lowest Capacity Asset", "BTCBUSD 18N"},
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{"Portfolio Turnover", "48.18%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "2ad07f12d7c80fd4a904269d62794e9e"}
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};
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}
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}
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