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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*/
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using QuantConnect.Data;
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using System.Collections.Generic;
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using QuantConnect.Indicators;
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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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/// This example demonstrates how to add index asset types and trade index options on SPX.
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/// </summary>
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public class BasicTemplateIndexOptionsAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private Symbol _spx;
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private ExponentialMovingAverage _emaSlow;
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private ExponentialMovingAverage _emaFast;
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protected virtual Resolution Resolution => Resolution.Minute;
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protected virtual int StartDay => 4;
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/// <summary>
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/// Initialize your algorithm and add desired assets.
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/// </summary>
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public override void Initialize()
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{
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SetStartDate(2021, 1, StartDay);
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SetEndDate(2021, 2, 1);
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SetCash(1000000);
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// Use indicator for signal; but it cannot be traded.
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// We will instead trade on SPX options
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_spx = AddIndex("SPX", Resolution).Symbol;
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var spxOptions = AddIndexOption(_spx, Resolution);
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spxOptions.SetFilter(filterFunc => filterFunc.CallsOnly());
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_emaSlow = EMA(_spx, Resolution > Resolution.Minute ? 6 : 80);
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_emaFast = EMA(_spx, Resolution > Resolution.Minute ? 2 : 200);
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Settings.DailyPreciseEndTime = true;
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}
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/// <summary>
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/// Index EMA Cross trading index options of the index.
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/// </summary>
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public override void OnData(Slice slice)
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{
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if (!slice.Bars.ContainsKey(_spx))
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{
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Debug($"No SPX on {Time}");
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return;
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}
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// Warm up indicators
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if (!_emaSlow.IsReady)
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{
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Debug($"EMA slow not ready on {Time}");
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return;
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}
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foreach (var chain in slice.OptionChains.Values)
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{
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foreach (var contract in chain.Contracts.Values)
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{
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if (contract.Expiry.Month == 3 && contract.Symbol.ID.StrikePrice == 3700m && contract.Right == OptionRight.Call && slice.QuoteBars.ContainsKey(contract.Symbol))
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{
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Log($"{Time} {contract.Strike}{(contract.Right == OptionRight.Call ? 'C' : 'P')} -- {slice.QuoteBars[contract.Symbol]}");
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}
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if (Portfolio.Invested)
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{
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continue;
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}
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if (_emaFast > _emaSlow && contract.Right == OptionRight.Call)
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{
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Liquidate(InvertOption(contract.Symbol));
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MarketOrder(contract.Symbol, 1);
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}
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else if (_emaFast < _emaSlow && contract.Right == OptionRight.Put)
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{
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Liquidate(InvertOption(contract.Symbol));
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MarketOrder(contract.Symbol, 1);
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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 (Portfolio[_spx].TotalSaleVolume > 0)
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{
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throw new RegressionTestException("Index is not tradable.");
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}
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if (Portfolio.TotalSaleVolume == 0)
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{
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throw new RegressionTestException("Trade volume should be greater than zero by the end of this algorithm");
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}
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AssertIndicators();
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}
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public Symbol InvertOption(Symbol symbol)
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{
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return QuantConnect.Symbol.CreateOption(
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symbol.Underlying,
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symbol.ID.Market,
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symbol.ID.OptionStyle,
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symbol.ID.OptionRight == OptionRight.Call ? OptionRight.Put : OptionRight.Call,
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symbol.ID.StrikePrice,
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symbol.ID.Date);
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}
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/// <summary>
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/// Asserts indicators are ready
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/// </summary>
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/// <exception cref="RegressionTestException"></exception>
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protected void AssertIndicators()
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{
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if (!_emaSlow.IsReady || !_emaFast.IsReady)
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{
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throw new RegressionTestException("Indicators are not ready!");
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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 virtual bool CanRunLocally { get; } = false;
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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 virtual 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 virtual long DataPoints => 0;
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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 virtual 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 virtual Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
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{
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{"Total Orders", "8220"},
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{"Average Win", "0.00%"},
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{"Average Loss", "0.00%"},
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{"Compounding Annual Return", "-100.000%"},
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{"Drawdown", "13.500%"},
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{"Expectancy", "-0.818"},
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{"Net Profit", "-13.517%"},
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{"Sharpe Ratio", "-2.678"},
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{"Probabilistic Sharpe Ratio", "0%"},
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{"Loss Rate", "89%"},
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{"Win Rate", "11%"},
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{"Profit-Loss Ratio", "0.69"},
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{"Alpha", "4.398"},
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{"Beta", "-0.989"},
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{"Annual Standard Deviation", "0.373"},
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{"Annual Variance", "0.139"},
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{"Information Ratio", "-12.816"},
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{"Tracking Error", "0.504"},
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{"Treynor Ratio", "1.011"},
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{"Total Fees", "$15207.00"},
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{"Estimated Strategy Capacity", "$8800000.00"},
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{"Fitness Score", "0.033"},
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{"Kelly Criterion Estimate", "0"},
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{"Kelly Criterion Probability Value", "0"},
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{"Sortino Ratio", "-8.62"},
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{"Return Over Maximum Drawdown", "-7.81"},
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{"Portfolio Turnover", "302.321"},
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{"Total Insights Generated", "0"},
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{"Total Insights Closed", "0"},
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{"Total Insights Analysis Completed", "0"},
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{"Long Insight Count", "0"},
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{"Short Insight Count", "0"},
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{"Long/Short Ratio", "100%"},
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{"Estimated Monthly Alpha Value", "$0"},
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{"Total Accumulated Estimated Alpha Value", "$0"},
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{"Mean Population Estimated Insight Value", "$0"},
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{"Mean Population Direction", "0%"},
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{"Mean Population Magnitude", "0%"},
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{"Rolling Averaged Population Direction", "0%"},
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{"Rolling Averaged Population Magnitude", "0%"},
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{"OrderListHash", "35b3f4b7a225468d42ca085386a2383e"}
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};
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}
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}
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