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 System.Linq;
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using QuantConnect.Data;
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using QuantConnect.Indicators;
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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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/// Basic algorithm demonstrating how to place stop limit orders.
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/// </summary>
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/// <meta name="tag" content="trading and orders" />
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/// <meta name="tag" content="placing orders" />
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/// <meta name="tag" content="stop limit order"/>
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public class StopLimitOrderRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private Symbol _symbol;
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private OrderTicket _buyOrderTicket;
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private OrderTicket _sellOrderTicket;
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private const decimal _tolerance = 0.001m;
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private const int _fastPeriod = 30;
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private const int _slowPeriod = 60;
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private ExponentialMovingAverage _fast;
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private ExponentialMovingAverage _slow;
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protected virtual bool AsynchronousOrders => false;
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public bool IsReady { get { return _fast.IsReady && _slow.IsReady; } }
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public bool TrendIsUp { get { return IsReady && _fast > _slow * (1 + _tolerance); } }
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public bool TrendIsDown { get { return IsReady && _fast < _slow * (1 + _tolerance); } }
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/// <summary>
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/// Initialize 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, 01, 01);
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SetEndDate(2017, 01, 01);
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SetCash(100000);
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_symbol = AddEquity("SPY", Resolution.Daily).Symbol;
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_fast = EMA(_symbol, _fastPeriod, Resolution.Daily);
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_slow = EMA(_symbol, _slowPeriod, Resolution.Daily);
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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="data">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 (!IsReady)
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{
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return;
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}
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var security = Securities[_symbol];
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if (_buyOrderTicket == null && TrendIsUp)
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{
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_buyOrderTicket = StopLimitOrder(_symbol, 100, stopPrice: security.High * 1.10m, limitPrice: security.High * 1.11m, asynchronous: AsynchronousOrders);
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}
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else if (_buyOrderTicket.Status == OrderStatus.Filled && _sellOrderTicket == null && TrendIsDown)
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{
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_sellOrderTicket = StopLimitOrder(_symbol, -100, stopPrice: security.Low * 0.99m, limitPrice: security.Low * 0.98m, asynchronous: AsynchronousOrders);
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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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var order = Transactions.GetOrderById(orderEvent.OrderId);
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if (!((StopLimitOrder)order).StopTriggered)
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{
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throw new RegressionTestException("StopLimitOrder StopTriggered should haven been set if the order filled.");
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}
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if (orderEvent.Direction == OrderDirection.Buy)
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{
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var limitPrice = _buyOrderTicket.Get(OrderField.LimitPrice);
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if (orderEvent.FillPrice > limitPrice)
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{
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throw new RegressionTestException($@"Buy stop limit order should have filled with price less than or equal to the limit price {
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limitPrice}. Fill price: {orderEvent.FillPrice}");
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}
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}
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else
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{
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var limitPrice = _sellOrderTicket.Get(OrderField.LimitPrice);
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if (orderEvent.FillPrice < limitPrice)
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{
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throw new RegressionTestException($@"Sell stop limit order should have filled with price greater than or equal to the limit price {
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limitPrice}. Fill price: {orderEvent.FillPrice}");
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}
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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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if (_buyOrderTicket == null || _sellOrderTicket == null)
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{
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throw new RegressionTestException("Expected two orders (buy and sell) to have been filled at the end of the algorithm.");
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}
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if (_buyOrderTicket.Status != OrderStatus.Filled || _sellOrderTicket.Status != OrderStatus.Filled)
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{
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throw new RegressionTestException("Expected the two orders (buy and sell) to have been filled at the end of the algorithm.");
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}
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foreach (var ticket in Transactions.GetOrderTickets())
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{
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if (ticket.SubmitRequest.Asynchronous != AsynchronousOrders)
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{
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throw new RegressionTestException("Expected all orders to have the same asynchronous flag as the algorithm.");
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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, 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 => 8061;
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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", "2"},
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{"Average Win", "1.28%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "0.318%"},
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{"Drawdown", "1.500%"},
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{"Expectancy", "0"},
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{"Start Equity", "100000"},
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{"End Equity", "101277.61"},
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{"Net Profit", "1.278%"},
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{"Sharpe Ratio", "-0.791"},
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{"Sortino Ratio", "-0.433"},
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{"Probabilistic Sharpe Ratio", "0.000%"},
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{"Loss Rate", "0%"},
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{"Win Rate", "100%"},
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{"Profit-Loss Ratio", "0"},
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{"Alpha", "-0.009"},
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{"Beta", "0.03"},
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{"Annual Standard Deviation", "0.008"},
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{"Annual Variance", "0"},
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{"Information Ratio", "-0.963"},
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{"Tracking Error", "0.104"},
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{"Treynor Ratio", "-0.199"},
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{"Total Fees", "$2.00"},
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{"Estimated Strategy Capacity", "$6100000000.00"},
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{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
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{"Portfolio Turnover", "0.02%"},
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{"Drawdown Recovery", "39"},
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{"OrderListHash", "f315858f3f9e6a983cfcf887237f70fd"}
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};
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}
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}
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