Colton Sellers
20910ca2dc
* Remove regression references to non-existant Python versions * Adjust regressions estimated capacity not adjusted by #5389 * Adjusts regression algorithms so that they pass (Index/Index Options) * Changes start/end date on BasicTemplateIndexAlgorithm * Changes option pricing model to BlackScholes in IndexOptionCallITMGreeksExpiryRegressionAlgorithm - The root cause of why there are no greeks at times for these options was identified. It is most likely due to the underlying's VolatilityModel not having had enough data to be "warmed up", which means it will return a standard deviation of zero to the option pricing model, rendering most metrics as NaN. * Adds missing index/index options regression algorithms - Regression algorithms are now 1-1 between C# and Python for Indexes/Index options. All regression tests are now passing * Fixes broken BasicTemplateIndex regression algorithm * Previously traded SPY, but because we have no SPY data in Lean master, I instead opted for index options, since data for those dates is already included * Deal with weekend for breaking test case * Adjust DefaultEndDate test to always pass * Check todays date for open Co-authored-by: Gerardo Salazar <gsalaz9800@gmail.com>
88 lines
3.7 KiB
C#
88 lines
3.7 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.Collections.Generic;
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using QuantConnect.Data;
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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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/// Regression Definition for Python NamedArgumentsRegression
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/// Used to test PythonNet kwargs
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/// </summary>
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/// <meta name="tag" content="using data" />
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public class NamedArgumentsRegression : IRegressionAlgorithmDefinition
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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 Language[] Languages { get; } = { Language.Python };
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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 Trades", "1"},
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{"Average Win", "0%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "246.000%"},
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{"Drawdown", "1.100%"},
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{"Expectancy", "0"},
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{"Net Profit", "3.459%"},
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{"Sharpe Ratio", "10.11"},
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{"Probabilistic Sharpe Ratio", "83.150%"},
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{"Loss Rate", "0%"},
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{"Win Rate", "0%"},
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{"Profit-Loss Ratio", "0"},
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{"Alpha", "1.935"},
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{"Beta", "-0.119"},
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{"Annual Standard Deviation", "0.16"},
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{"Annual Variance", "0.026"},
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{"Information Ratio", "-4.556"},
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{"Tracking Error", "0.221"},
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{"Treynor Ratio", "-13.568"},
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{"Total Fees", "$3.26"},
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{"Estimated Strategy Capacity", "$890000000.00"},
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{"Fitness Score", "0.111"},
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{"Kelly Criterion Estimate", "0"},
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{"Kelly Criterion Probability Value", "0"},
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{"Sortino Ratio", "52.533"},
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{"Return Over Maximum Drawdown", "214.75"},
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{"Portfolio Turnover", "0.111"},
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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", "82fee25cd17100c53bb173834ab5f0b2"}
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};
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}
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}
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