Gerardo Salazar
76 lines
3.1 KiB
Python
76 lines
3.1 KiB
Python
# 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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from clr import AddReference
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AddReference("System")
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AddReference("QuantConnect.Algorithm")
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AddReference("QuantConnect.Algorithm.Framework")
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AddReference("QuantConnect.Common")
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from System import *
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from QuantConnect import *
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from QuantConnect.Algorithm import *
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from QuantConnect.Algorithm.Framework.Selection import *
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from QuantConnect.Data.Custom.Benzinga import *
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from QuantConnect.Data.UniverseSelection import *
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class BenzingaNewsAlgorithm(QCAlgorithm):
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def Initialize(self):
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self.words = {
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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, "slipped": -0.5,
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"outperforming": 0.5, "underperforming": -0.5,
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"outperform": 0.5, "underperform": -0.5
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}
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self.SetStartDate(2018, 10, 12)
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self.SetEndDate(2018, 11, 25)
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self.SetCash(100000)
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self.UniverseSettings.Resolution = Resolution.Daily
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self.AddUniverseSelection(CoarseFundamentalUniverseSelectionModel(self.CoarseSelector))
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def CoarseSelector(self, coarse):
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# Add Benzinga news data from the filtered coarse selection
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symbols = [i.Symbol for i in coarse if i.HasFundamentalData and i.DollarVolume > 50000000][:10]
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for symbol in symbols:
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self.AddData(BenzingaNews, symbol)
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return symbols
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def OnData(self, data):
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for article in data.Get(BenzingaNews).Values:
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# Split the article into words in all lowercase
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articleWords = article.Contents.lower().split(" ")
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# Get the list of matching words we have sentiment definitions for
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intersection = set(self.words.keys()).intersection(articleWords)
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# Get the sentiment score
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sentimentScore = sum([self.words[i] for i in intersection])
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# Set holdings equal to 1/10th of the sentiment score we get
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self.SetHoldings(article.Symbol.Underlying, sentimentScore / 10.0)
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def OnSecuritiesChanged(self, changes):
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for r in [i for i in changes.RemovedSecurities if i.Symbol.SecurityType == SecurityType.Equity]:
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# If removed from the universe, liquidate and remove the custom data from the algorithm
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self.Liquidate(r.Symbol)
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self.RemoveSecurity(Symbol.CreateBase(BenzingaNews, r.Symbol, Market.USA))
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