Gerardo Salazar
106 lines
4.1 KiB
C#
106 lines
4.1 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.Data.Custom.PsychSignal;
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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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/// This example algorithm shows how to import and use psychsignal sentiment data.
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/// </summary>
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/// <meta name="tag" content="strategy example" />
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/// <meta name="tag" content="using data" />
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/// <meta name="tag" content="custom data" />
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/// <meta name="tag" content="psychsignal" />
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/// <meta name="tag" content="sentiment" />
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public class PsychSignalSentimentRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private const string Ticker = "AAPL";
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private Symbol _symbol;
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/// <summary>
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/// Initialize the algorithm with our custom data
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/// </summary>
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public override void Initialize()
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{
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SetStartDate(2019, 6, 3);
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SetEndDate(2019, 6, 9);
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SetCash(100000);
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_symbol = AddEquity(Ticker).Symbol;
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AddData<PsychSignalSentiment>(Ticker);
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}
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/// <summary>
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/// Loads each new data point into the algorithm. On sentiment data, we place orders depending on the sentiment
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/// </summary>
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/// <param name="slice">Slice object containing the sentiment data</param>
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public override void OnData(Slice slice)
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{
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foreach (var message in slice.Get<PsychSignalSentiment>().Values)
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{
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if (!Portfolio.Invested && Transactions.GetOpenOrders().Count == 0 && slice.ContainsKey(_symbol) &&
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message.BullIntensity > 1.5m && message.BullScoredMessages > 3.0m)
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{
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SetHoldings(_symbol, 0.25);
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}
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else if (Portfolio.Invested && message.BearIntensity > 1.5m && message.BearScoredMessages > 3.0m)
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{
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Liquidate(_symbol);
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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 Language[] Languages { get; } = { Language.CSharp, 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", "13"},
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{"Average Win", "0.36%"},
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{"Average Loss", "-0.25%"},
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{"Compounding Annual Return", "160.597%"},
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{"Drawdown", "0.700%"},
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{"Expectancy", "0.612"},
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{"Net Profit", "1.276%"},
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{"Sharpe Ratio", "11.154"},
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{"Loss Rate", "33%"},
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{"Win Rate", "67%"},
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{"Profit-Loss Ratio", "1.42"},
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{"Alpha", "0"},
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{"Beta", "0"},
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{"Annual Standard Deviation", "0.057"},
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{"Annual Variance", "0.003"},
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{"Information Ratio", "0"},
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{"Tracking Error", "0"},
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{"Treynor Ratio", "0"},
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{"Total Fees", "$13.00"}
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};
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}
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}
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