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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using QuantConnect.Interfaces;
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using System.Collections.Generic;
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using QuantConnect.Data;
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using QuantConnect.Data.Consolidators;
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using QuantConnect.Data.Market;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Demonstration of how to initialize and use the RenkoConsolidator
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/// </summary>
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/// <meta name="tag" content="renko" />
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/// <meta name="tag" content="indicators" />
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/// <meta name="tag" content="using data" />
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/// <meta name="tag" content="consolidating data" />
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public class ClassicRenkoConsolidatorAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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/// <summary>
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/// Initializes the algorithm state.
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/// </summary>
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public override void Initialize()
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{
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SetStartDate(2012, 01, 01);
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SetEndDate(2013, 01, 01);
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AddEquity("SPY", Resolution.Daily);
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// this is the simple constructor that will perform the renko logic to the Value
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// property of the data it receives.
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// break SPY into $2.5 renko bricks and send that data to our 'OnRenkoBar' method
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var renkoClose = new ClassicRenkoConsolidator(2.5m);
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renkoClose.DataConsolidated += (sender, consolidated) =>
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{
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// call our event handler for renko data
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HandleRenkoClose(consolidated);
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};
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// register the consolidator for updates
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SubscriptionManager.AddConsolidator("SPY", renkoClose);
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// this is the full constructor that can accept a value selector and a volume selector
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// this allows us to perform the renko logic on values other than Close, even computed values!
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// break SPY into (2*o + h + l + 3*c)/7
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var renko7bar = new ClassicRenkoConsolidator<TradeBar>(2.5m, x => (2 * x.Open + x.High + x.Low + 3 * x.Close) / 7m, x => x.Volume);
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renko7bar.DataConsolidated += (sender, consolidated) =>
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{
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HandleRenko7Bar(consolidated);
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};
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// register the consolidator for updates
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SubscriptionManager.AddConsolidator("SPY", renko7bar);
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}
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/// <summary>
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/// We're doing our analysis in the OnRenkoBar method, but the framework verifies that this method exists, so we define it.
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/// </summary>
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public override void OnData(Slice slice)
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{
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}
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/// <summary>
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/// This function is called by our renkoClose consolidator defined in Initialize()
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/// </summary>
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/// <param name="data">The new renko bar produced by the consolidator</param>
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public void HandleRenkoClose(RenkoBar data)
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{
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if (!Portfolio.Invested)
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{
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SetHoldings(data.Symbol, 1.0);
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}
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Log($"CLOSE - {data.Time.ToIso8601Invariant()} - {data.Open} {data.Close}");
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}
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/// <summary>
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/// This function is called by our renko7bar onsolidator defined in Initialize()
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/// </summary>
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/// <param name="data">The new renko bar produced by the consolidator</param>
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public void HandleRenko7Bar(RenkoBar data)
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{
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if (Portfolio.Invested)
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{
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Liquidate(data.Symbol);
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}
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Log($"7BAR - {data.Time.ToIso8601Invariant()} - {data.Open} {data.Close}");
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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 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 long DataPoints => 2003;
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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", "29"},
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{"Average Win", "1.85%"},
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{"Average Loss", "-1.49%"},
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{"Compounding Annual Return", "7.824%"},
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{"Drawdown", "6.800%"},
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{"Expectancy", "0.281"},
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{"Start Equity", "100000"},
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{"End Equity", "107838.74"},
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{"Net Profit", "7.839%"},
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{"Sharpe Ratio", "0.692"},
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{"Sortino Ratio", "0.636"},
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{"Probabilistic Sharpe Ratio", "34.456%"},
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{"Loss Rate", "43%"},
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{"Win Rate", "57%"},
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{"Profit-Loss Ratio", "1.24"},
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{"Alpha", "0.004"},
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{"Beta", "0.411"},
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{"Annual Standard Deviation", "0.07"},
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{"Annual Variance", "0.005"},
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{"Information Ratio", "-0.704"},
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{"Tracking Error", "0.083"},
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{"Treynor Ratio", "0.118"},
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{"Total Fees", "$129.34"},
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{"Estimated Strategy Capacity", "$2500000000.00"},
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{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
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{"Portfolio Turnover", "7.91%"},
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{"Drawdown Recovery", "105"},
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{"OrderListHash", "2668157409450ab9949a71716a5dbc2e"}
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};
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}
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}
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