wehub-resource-sync
127 lines
5.0 KiB
C#
127 lines
5.0 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.Interfaces;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Rebalances ultra-liquid stocks monthly, testing
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/// bursts of orders centered around the start of the month at Hourly resolution
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/// </summary>
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public class MonthlyRebalanceHourly : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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public override void Initialize()
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{
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SetStartDate(2019, 12, 31);
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SetEndDate(2020, 4, 5);
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SetCash(100000);
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var spy = AddEquity("SPY", Resolution.Hour).Symbol;
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AddEquity("GE", Resolution.Hour);
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AddEquity("FB", Resolution.Hour);
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AddEquity("DIS", Resolution.Hour);
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AddEquity("CSCO", Resolution.Hour);
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AddEquity("CRM", Resolution.Hour);
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AddEquity("C", Resolution.Hour);
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AddEquity("BAC", Resolution.Hour);
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AddEquity("BABA", Resolution.Hour);
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AddEquity("AAPL", Resolution.Hour);
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Schedule.On(DateRules.MonthStart(spy), TimeRules.Noon, () =>
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{
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foreach (var symbol in Securities.Keys)
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{
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SetHoldings(symbol, 0.10);
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}
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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; } = 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 List<Language> Languages { get; } = new() { Language.CSharp };
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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 => 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 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", "35"},
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{"Average Win", "0.05%"},
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{"Average Loss", "-0.10%"},
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{"Compounding Annual Return", "-72.444%"},
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{"Drawdown", "36.500%"},
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{"Expectancy", "-0.449"},
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{"Net Profit", "-28.406%"},
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{"Sharpe Ratio", "-1.369"},
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{"Probabilistic Sharpe Ratio", "4.398%"},
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{"Loss Rate", "64%"},
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{"Win Rate", "36%"},
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{"Profit-Loss Ratio", "0.51"},
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{"Alpha", "-0.175"},
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{"Beta", "0.892"},
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{"Annual Standard Deviation", "0.503"},
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{"Annual Variance", "0.253"},
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{"Information Ratio", "-0.822"},
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{"Tracking Error", "0.138"},
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{"Treynor Ratio", "-0.772"},
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{"Total Fees", "$38.83"},
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{"Estimated Strategy Capacity", "$6000000.00"},
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{"Fitness Score", "0.004"},
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{"Kelly Criterion Estimate", "0"},
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{"Kelly Criterion Probability Value", "0"},
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{"Sortino Ratio", "-2.033"},
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{"Return Over Maximum Drawdown", "-2.079"},
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{"Portfolio Turnover", "0.018"},
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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", "1de9bcf6cda0945af6ba1f74c4dcb22c"}
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};
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}
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}
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