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.Indicators;
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using QuantConnect.Interfaces;
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using QuantConnect.Orders;
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using QuantConnect.Orders.Fees;
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using QuantConnect.Securities;
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using QuantConnect.Securities.Equity;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Demonstration of how to use custom security properties.
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/// In this algorithm we trade a security based on the values of a slow and fast EMAs which are stored in the security itself.
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/// </summary>
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public class SecurityCustomPropertiesAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private Equity _spy;
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private dynamic _dynamicSpy;
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public override void Initialize()
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{
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SetStartDate(2013, 10, 07);
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SetEndDate(2013, 10, 11);
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SetCash(100000);
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_spy = AddEquity("SPY", Resolution.Minute);
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// Using the dynamic interface to store our indicator as a custom property
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_dynamicSpy = _spy;
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_dynamicSpy.SlowEma = EMA(_spy.Symbol, 30, Resolution.Minute);
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// Using the generic interface to store our indicator as a custom property
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_spy.Add("FastEma", EMA(_spy.Symbol, 60, Resolution.Minute));
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// Using the indexer to store our indicator as a custom property
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_spy["BB"] = BB(_spy.Symbol, 20, 1, MovingAverageType.Simple, Resolution.Minute);
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// Fee factor to be used by the custom fee model
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_dynamicSpy.FeeFactor = 0.00002m;
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_spy.SetFeeModel(new CustomFeeModel());
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// This property will be used to store the prices used to calculate the fees in order to assert the correct fee factor is used.
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_dynamicSpy.OrdersFeesPrices = new Dictionary<int, decimal>();
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}
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public override void OnData(Slice slice)
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{
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if (!_dynamicSpy.FastEma.IsReady)
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{
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return;
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}
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if (!Portfolio.Invested)
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{
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// Using the dynamic interface to access the custom properties
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if (_dynamicSpy.SlowEma > _dynamicSpy.FastEma)
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{
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SetHoldings(_spy.Symbol, 1);
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}
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}
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// Using the generic interface to access the custom properties
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else if (_spy.Get<ExponentialMovingAverage>("SlowEma") < _spy.Get<ExponentialMovingAverage>("FastEma"))
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{
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Liquidate(_spy.Symbol);
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}
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// Using the indexer to access the custom properties
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var bb = _spy["BB"] as BollingerBands;
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Plot("BB", bb.UpperBand, bb.MiddleBand, bb.LowerBand);
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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 fee = orderEvent.OrderFee;
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var expectedFee = _dynamicSpy.OrdersFeesPrices[orderEvent.OrderId] * orderEvent.AbsoluteFillQuantity * _dynamicSpy.FeeFactor;
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if (fee.Value.Amount != expectedFee)
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{
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throw new RegressionTestException($"Custom fee model failed to set the correct fee. Expected: {expectedFee}. Actual: {fee.Value.Amount}");
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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 (Transactions.OrdersCount == 0)
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{
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throw new RegressionTestException("No orders executed");
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}
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}
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/// <summary>
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/// This custom fee is implemented for demonstration purposes only.
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/// </summary>
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private class CustomFeeModel : FeeModel
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{
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public CustomFeeModel()
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{
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}
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public override OrderFee GetOrderFee(OrderFeeParameters parameters)
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{
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var security = parameters.Security;
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// custom fee math using the fee factor stored in security instance
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var hasFeeFactor = security.TryGet<decimal>("FeeFactor", out var feeFactor);
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if (!hasFeeFactor)
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{
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feeFactor = 0.00001m;
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}
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// Store the price used to calculate the fee for this order
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((dynamic)security).OrdersFeesPrices[parameters.Order.Id] = security.Price;
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var fee = Math.Max(1m, security.Price * parameters.Order.AbsoluteQuantity * feeFactor);
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return new OrderFee(new CashAmount(fee, "USD"));
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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 => 3943;
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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", "31"},
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{"Average Win", "0.43%"},
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{"Average Loss", "-0.08%"},
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{"Compounding Annual Return", "84.608%"},
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{"Drawdown", "0.800%"},
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{"Expectancy", "0.628"},
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{"Start Equity", "100000"},
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{"End Equity", "100786.91"},
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{"Net Profit", "0.787%"},
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{"Sharpe Ratio", "12.062"},
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{"Sortino Ratio", "0"},
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{"Probabilistic Sharpe Ratio", "88.482%"},
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{"Loss Rate", "73%"},
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{"Win Rate", "27%"},
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{"Profit-Loss Ratio", "5.11"},
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{"Alpha", "0.258"},
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{"Beta", "0.342"},
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{"Annual Standard Deviation", "0.077"},
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{"Annual Variance", "0.006"},
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{"Information Ratio", "-7.082"},
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{"Tracking Error", "0.147"},
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{"Treynor Ratio", "2.73"},
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{"Total Fees", "$59.78"},
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{"Estimated Strategy Capacity", "$7300000.00"},
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{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
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{"Portfolio Turnover", "597.29%"},
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{"Drawdown Recovery", "2"},
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{"OrderListHash", "947ae7fbc63fb8cc499f96ac92ee3394"}
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};
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}
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}
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