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# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from clr import AddReference
AddReference("System")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Common")
from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Data.Custom.Tiingo import *
### <summary>
### Look for positive and negative words in the news article description
### and trade based on the sum of the sentiment
### </summary>
class TiingoNewsAlgorithm(QCAlgorithm):
def Initialize(self):
# Predefine a dictionary of words with scores to scan for in the description
# of the Tiingo news article
self.words = {
"bad": -0.5, "good": 0.5,
"negative": -0.5, "great": 0.5,
"growth": 0.5, "fail": -0.5,
"failed": -0.5, "success": 0.5, "nailed": 0.5,
"beat": 0.5, "missed": -0.5,
}
self.SetStartDate(2019, 6, 10)
self.SetEndDate(2019, 10, 3)
self.SetCash(100000)
aapl = self.AddEquity("AAPL", Resolution.Hour).Symbol
self.aaplCustom = self.AddData(TiingoNews, aapl).Symbol
# Request underlying equity data.
ibm = self.AddEquity("IBM", Resolution.Minute).Symbol
# Add news data for the underlying IBM asset
news = self.AddData(TiingoNews, ibm).Symbol
# Request 60 days of history with the TiingoNews IBM Custom Data Symbol
history = self.History(TiingoNews, news, 60, Resolution.Daily)
# Count the number of items we get from our history request
self.Debug(f"We got {len(history)} items from our history request")
def OnData(self, data):
# Confirm that the data is in the collection
if not data.ContainsKey(self.aaplCustom):
return
# Gets the data from the slice
article = data[self.aaplCustom]
# Article descriptions come in all caps. Lower and split by word
descriptionWords = article.Description.lower().split(" ")
# Take the intersection of predefined words and the words in the
# description to get a list of matching words
intersection = set(self.words.keys()).intersection(descriptionWords)
# Get the sum of the article's sentiment, and go long or short
# depending if it's a positive or negative description
sentiment = sum([self.words[i] for i in intersection])
self.SetHoldings(article.Symbol.Underlying, sentiment)