Colton Sellers
d2d99b1f10
Regression Tests / build (push) Has been cancelled
Build & Test Lean / build (push) Has been cancelled
* Implement scheduled event sampling solution * Use UTC time, only update daily portfolio value once a day * For daily resolutions sample chart always * Cleanup * Drop resample daily all together * Force final sample * Regression updates * FIx LiveResultHandler to update portfolio and benchmark values outside of sampling event * Name the daily sampling event * Address review pt 1 * Drop force and use reference wrapper * Adjust tests * Fix warning for Benchmark Timezone Misalignment and also add test * Fix for daily resolution orders and test adjustments * Also warn on universe settings with daily resolution * Update missed regression * Fix reference wrapper use * Update regression after rebase * Add values back in for Daylight Algo * Have statistics builder skip day 1 performance * Regression adjustments * Test adjustments * Update regression unit test * Adjust some regressions starts to show performance values * Add hourly algorithm for beta comparison * Address missing Python regression changes * Remove null comment
111 lines
4.5 KiB
C#
111 lines
4.5 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 QuantConnect.Data;
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using QuantConnect.Interfaces;
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using System.Collections.Generic;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Regression algorithm using a benchmark security which should be mapped from SPWR to SPWRA during the backtest
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/// </summary>
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public class MappedBenchmarkRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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/// <summary>
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/// Initialise 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(2008, 08, 20);
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SetEndDate(2008, 10, 1);
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SetBenchmark("SPWR");
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AddEquity("SPY", Resolution.Hour);
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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 data)
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{
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if (!Portfolio.Invested)
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{
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SetHoldings("SPY", 1);
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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 Language[] Languages { get; } = { Language.CSharp };
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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", "-50.371%"},
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{"Drawdown", "12.700%"},
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{"Expectancy", "0"},
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{"Net Profit", "-7.863%"},
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{"Sharpe Ratio", "-1.149"},
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{"Probabilistic Sharpe Ratio", "15.177%"},
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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", "-0.27"},
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{"Beta", "0.262"},
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{"Annual Standard Deviation", "0.335"},
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{"Annual Variance", "0.112"},
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{"Information Ratio", "0.074"},
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{"Tracking Error", "0.721"},
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{"Treynor Ratio", "-1.465"},
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{"Total Fees", "$5.10"},
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{"Estimated Strategy Capacity", "$180000000.00"},
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{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
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{"Fitness Score", "0.003"},
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{"Kelly Criterion Estimate", "0"},
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{"Kelly Criterion Probability Value", "0"},
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{"Sortino Ratio", "-3.681"},
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{"Return Over Maximum Drawdown", "-4.174"},
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{"Portfolio Turnover", "0.034"},
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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", "d5466a4f274a65313a1f58c78d3cfd00"}
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};
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}
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}
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