Files
quantconnect--lean/Algorithm.CSharp/OptionAssignmentRegressionAlgorithm.cs
T
Martin-Molinero f3c386663b Feature .net 5 (#5505)
* Update projects to use .NET 5.0, the successor to .NET Core

* Fix ambiguous errors. Add IBAutomator net5

* Remove FXCM

* Upgrade IBAutomater to v1.0.51

ignored, and an empty message aborts the commit.

* Fix rebase

- Fix ambiguous Index
- Remove StrategyCapacity.cs
- Update System.Threading.Tasks.Extensionsy

* Remove unrequired references

* Fixes

- Travis will use dotnet, not nunit nor mono
- Remove mono from foundation image
- Fix python setup in research
- Fix unit tests

* Don't call ReadKey when input is redirected

* Fix ConsoleLeanOptimizer

* Research fixes

* Update comment

* Add vsdbg to Dockerfile

* Fixes

- Revert dockerfile FROM custom changes
- Adjust and fix regression algorithms
   - Option assignment will be deterministic in the order
   - 'Rolling Averaged Population' is calculated using doubles, updating
     expected values.
- Update readme, removing references to mono
- Add missing Py.Gil lock

* Replace ICSharp with .NET Interactive

* Fixes after rebase

* CSharp research fixes

- Adding new Initialize.csx that pre loads all assemblies
- Adjusting template research file
- Moving steps in dockerfilejupyter
- Fix unit tests and regression tests after rebase

Co-authored-by: Gerardo Salazar <gsalaz9800@gmail.com>
Co-authored-by: Stefano Raggi <stefano.raggi67@gmail.com>
Co-authored-by: Jasper van Merle <jaspervmerle@gmail.com>
2021-05-06 17:23:51 -03:00

124 lines
4.8 KiB
C#

/*
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*/
using System.Collections.Generic;
using System.Linq;
using QuantConnect.Data;
using QuantConnect.Interfaces;
using QuantConnect.Securities;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// This regression algorithm verifies automatic option contract assignment behavior.
/// </summary>
/// <meta name="tag" content="regression test" />
/// <meta name="tag" content="options" />
/// <meta name="tag" content="using data" />
/// <meta name="tag" content="filter selection" />
public class OptionAssignmentRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private Security Stock;
private Security CallOption;
private Symbol CallOptionSymbol;
private Security PutOption;
private Symbol PutOptionSymbol;
public override void Initialize()
{
SetStartDate(2015, 12, 23);
SetEndDate(2015, 12, 24);
SetCash(100000);
Stock = AddEquity("GOOG", Resolution.Minute);
var contracts = OptionChainProvider.GetOptionContractList(Stock.Symbol, UtcTime).ToList();
PutOptionSymbol = contracts
.Where(c => c.ID.OptionRight == OptionRight.Put)
.OrderBy(c => c.ID.Date)
.First(c => c.ID.StrikePrice == 800m);
CallOptionSymbol = contracts
.Where(c => c.ID.OptionRight == OptionRight.Call)
.OrderBy(c => c.ID.Date)
.First(c => c.ID.StrikePrice == 600m);
PutOption = AddOptionContract(PutOptionSymbol);
CallOption = AddOptionContract(CallOptionSymbol);
}
public override void OnData(Slice data)
{
if (!Portfolio.Invested && Stock.Price != 0 && PutOption.Price != 0 && CallOption.Price != 0)
{
// this gets executed on start and after each auto-assignment, finally ending with expiration assignment
MarketOrder(PutOptionSymbol, -1);
MarketOrder(CallOptionSymbol, -1);
}
}
public bool CanRunLocally { get; } = true;
public Language[] Languages { get; } = {Language.CSharp};
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
{
{"Total Trades", "24"},
{"Average Win", "0%"},
{"Average Loss", "0%"},
{"Compounding Annual Return", "0%"},
{"Drawdown", "0%"},
{"Expectancy", "0"},
{"Net Profit", "0%"},
{"Sharpe Ratio", "0"},
{"Probabilistic Sharpe Ratio", "0%"},
{"Loss Rate", "0%"},
{"Win Rate", "0%"},
{"Profit-Loss Ratio", "0"},
{"Alpha", "0"},
{"Beta", "0"},
{"Annual Standard Deviation", "0"},
{"Annual Variance", "0"},
{"Information Ratio", "0"},
{"Tracking Error", "0"},
{"Treynor Ratio", "0"},
{"Total Fees", "$12.00"},
{"Estimated Strategy Capacity", "$310000.00"},
{"Fitness Score", "0.5"},
{"Kelly Criterion Estimate", "0"},
{"Kelly Criterion Probability Value", "0"},
{"Sortino Ratio", "79228162514264337593543950335"},
{"Return Over Maximum Drawdown", "-50.725"},
{"Portfolio Turnover", "8.14"},
{"Total Insights Generated", "0"},
{"Total Insights Closed", "0"},
{"Total Insights Analysis Completed", "0"},
{"Long Insight Count", "0"},
{"Short Insight Count", "0"},
{"Long/Short Ratio", "100%"},
{"Estimated Monthly Alpha Value", "$0"},
{"Total Accumulated Estimated Alpha Value", "$0"},
{"Mean Population Estimated Insight Value", "$0"},
{"Mean Population Direction", "0%"},
{"Mean Population Magnitude", "0%"},
{"Rolling Averaged Population Direction", "0%"},
{"Rolling Averaged Population Magnitude", "0%"},
{"OrderListHash", "58557574cf0489dd38fb37768f509ca1"}
};
}
}