Gerardo Salazar
75 lines
2.8 KiB
C#
75 lines
2.8 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 System.Linq;
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using QuantConnect.Data;
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using QuantConnect.Data.Custom.Tiingo;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Look for positive and negative words in the news article description
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/// and trade based on the sum of the sentiment
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/// </summary>
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public class TiingoNewsAlgorithm : QCAlgorithm
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{
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private Symbol _tiingoSymbol;
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// Predefine a dictionary of words with scores to scan for in the description
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// of the Tiingo news article
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private readonly Dictionary<string, double> _words = new Dictionary<string, double>()
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{
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{"bad", -0.5}, {"good", 0.5},
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{ "negative", -0.5}, {"great", 0.5},
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{"growth", 0.5}, {"fail", -0.5},
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{"failed", -0.5}, {"success", 0.5},
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{"nailed", 0.5}, {"beat", 0.5},
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{"missed", -0.5}
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};
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public override void Initialize()
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{
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SetStartDate(2019, 6, 10);
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SetEndDate(2019, 10, 3);
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SetCash(100000);
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var aapl = AddEquity("AAPL", Resolution.Hour).Symbol;
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_tiingoSymbol = AddData<TiingoNews>(aapl).Symbol;
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}
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public override void OnData(Slice data)
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{
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//Confirm that the data is in the collection
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if (!data.ContainsKey(_tiingoSymbol)) return;
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// Gets the first piece of data from the Slice
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var article = data.Get<TiingoNews>(_tiingoSymbol);
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// Article descriptions come in all caps. Lower and split by word
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var descriptionWords = article.Description.ToLowerInvariant().Split(' ');
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// Take the intersection of predefined words and the words in the
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// description to get a list of matching words
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var intersection = _words.Keys.Intersect(descriptionWords);
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// Get the sum of the article's sentiment, and go long or short
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// depending if it's a positive or negative description
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var sentiment = intersection.Select(x => _words[x]).Sum();
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SetHoldings(article.Symbol.Underlying, sentiment);
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}
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}
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} |