wehub-resource-sync
213 lines
8.6 KiB
C#
213 lines
8.6 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.Collections.Generic;
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using System.Linq;
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using QuantConnect.Data;
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using QuantConnect.Data.Market;
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using QuantConnect.Data.UniverseSelection;
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using QuantConnect.Interfaces;
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using QuantConnect.Orders;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Regression algorithm that test if the fill prices are the correct quote side.
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/// </summary>
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/// <meta name="tag" content="using data" />
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/// <meta name="tag" content="using quantconnect" />
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/// <meta name="tag" content="trading and orders" />
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public class EquityTradeAndQuotesRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private Symbol _symbol;
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private bool _canTrade;
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private int _quoteCounter;
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private int _tradeCounter;
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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(2013, 10, 07); //Set Start Date
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SetEndDate(2013, 10, 11); //Set End Date
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SetCash(100000); //Set Strategy Cash
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SetSecurityInitializer(x => x.SetDataNormalizationMode(DataNormalizationMode.Raw));
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_symbol = AddEquity("IBM", Resolution.Minute).Symbol;
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AddEquity("AAPL", Resolution.Daily);
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// 2013-10-07 was Monday, that's why we ask 3 days history to get data from previous Friday.
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var history = History(new[] { _symbol }, TimeSpan.FromDays(3), Resolution.Minute).ToList();
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Log($"{Time} - history.Count: {history.Count}");
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const int expectedSliceCount = 390;
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if (history.Count != expectedSliceCount)
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{
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throw new RegressionTestException($"History slices - expected: {expectedSliceCount}, actual: {history.Count}");
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}
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if (history.Any(s => s.Bars.Count != 1 && s.QuoteBars.Count != 1))
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{
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throw new RegressionTestException($"History not all slices have trades and quotes.");
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}
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Schedule.On(DateRules.EveryDay(_symbol), TimeRules.AfterMarketOpen(_symbol, 0), () => { _canTrade = true; });
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Schedule.On(DateRules.EveryDay(_symbol), TimeRules.BeforeMarketClose(_symbol, 16), () => { _canTrade = false; });
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}
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/// <summary>
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/// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
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/// </summary>
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/// <param name="slice">Slice object keyed by symbol containing the stock data</param>
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public override void OnData(Slice slice)
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{
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_quoteCounter += slice.QuoteBars.Count;
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_tradeCounter += slice.Bars.Count;
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if (!Portfolio.Invested && _canTrade)
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{
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SetHoldings(_symbol, 1);
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Log($"Purchased Security {_symbol.ID}");
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}
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if (Time.Minute % 15 == 0)
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{
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Liquidate();
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}
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}
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public override void OnSecuritiesChanged(SecurityChanges changes)
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{
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foreach (var addedSecurity in changes.AddedSecurities)
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{
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var subscriptions = SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(addedSecurity.Symbol);
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if (addedSecurity.Symbol == _symbol)
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{
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if (!(subscriptions.Count == 2 &&
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subscriptions.Any(s => s.TickType == TickType.Trade) &&
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subscriptions.Any(s => s.TickType == TickType.Quote)))
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{
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throw new RegressionTestException($"Subscriptions were not correctly added for high resolution.");
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}
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}
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else
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{
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if (subscriptions.Single().TickType != TickType.Trade)
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{
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throw new RegressionTestException($"Subscriptions were not correctly added for low resolution.");
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}
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}
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}
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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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Log($"{Time:s} {orderEvent.Direction}");
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var expectedFillPrice = orderEvent.Direction == OrderDirection.Buy ? Securities[_symbol].AskPrice : Securities[_symbol].BidPrice;
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if (orderEvent.FillPrice != expectedFillPrice)
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{
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throw new RegressionTestException($"Fill price is not the expected for OrderId {orderEvent.OrderId} at Algorithm Time {Time:s}." +
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$"\n\tExpected fill price: {expectedFillPrice}, Actual fill price: {orderEvent.FillPrice}");
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}
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}
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}
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public override void OnEndOfAlgorithm()
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{
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// We expect at least 390 * 5 = 1950 minute bar
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// + 5 daily bars, but those are pumped into OnData every minute
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if (_tradeCounter <= 1955)
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{
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throw new RegressionTestException($"Fail at trade bars count expected >= 1955, actual: {_tradeCounter}.");
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}
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// We expect 390 * 5 = 1950 quote bars.
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if (_quoteCounter != 1950)
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{
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throw new RegressionTestException($"Fail at trade bars count expected: 1950, actual: {_quoteCounter}.");
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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 => 5504;
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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 => 780;
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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", "250"},
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{"Average Win", "0.12%"},
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{"Average Loss", "-0.10%"},
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{"Compounding Annual Return", "-88.292%"},
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{"Drawdown", "3.300%"},
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{"Expectancy", "-0.225"},
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{"Start Equity", "100000"},
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{"End Equity", "97294.97"},
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{"Net Profit", "-2.705%"},
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{"Sharpe Ratio", "-5.072"},
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{"Sortino Ratio", "-5.033"},
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{"Probabilistic Sharpe Ratio", "1.489%"},
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{"Loss Rate", "65%"},
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{"Win Rate", "35%"},
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{"Profit-Loss Ratio", "1.20"},
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{"Alpha", "-1.882"},
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{"Beta", "0.571"},
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{"Annual Standard Deviation", "0.149"},
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{"Annual Variance", "0.022"},
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{"Information Ratio", "-22.183"},
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{"Tracking Error", "0.123"},
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{"Treynor Ratio", "-1.323"},
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{"Total Fees", "$670.74"},
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{"Estimated Strategy Capacity", "$170000.00"},
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{"Lowest Capacity Asset", "IBM R735QTJ8XC9X"},
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{"Portfolio Turnover", "4996.13%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "c65a9aa12b55e53a49a29cd28a358fcd"}
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};
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}
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}
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