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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.Algorithm.Framework")
AddReference("QuantConnect.Common")
from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Data.Custom.Benzinga import *
from datetime import datetime, timedelta
### <summary>
### Benzinga is a provider of news data. Their news is made in-house
### and covers stock related news such as corporate events.
### </summary>
class BenzingaNewsAlgorithm(QCAlgorithm):
def Initialize(self):
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.lastTrade = datetime(1, 1, 1)
self.SetStartDate(2018, 6, 5)
self.SetEndDate(2018, 8, 4)
self.SetCash(100000)
aapl = self.AddEquity("AAPL", Resolution.Hour).Symbol
ibm = self.AddEquity("IBM", Resolution.Hour).Symbol
self.AddData(BenzingaNews, aapl)
self.AddData(BenzingaNews, ibm)
def OnData(self, data):
if (self.Time - self.lastTrade) < timedelta(days=5):
return
# Get rid of our holdings after 5 days, and start fresh
self.Liquidate()
# Get all Benzinga data and loop over it
for article in data.Get(BenzingaNews).Values:
selectedSymbol = None
# Use loop instead of list comprehension for clarity purposes
# Select the same Symbol we're getting a data point for
# from the articles list so that we can get the sentiment of the article
# We use the underlying Symbol because the Symbols included in the `Symbols` property
# are equity Symbols.
for symbol in article.Symbols:
if symbol == article.Symbol.Underlying:
selectedSymbol = symbol
break
if selectedSymbol is None:
raise Exception(f"Could not find current Symbol {article.Symbol.Underlying} even though it should exist")
# The intersection of the article contents and the pre-defined words are the words that are included in both collections
intersection = set(article.Contents.lower().split(" ")).intersection(list(self.words.keys()))
# Get the words, then get the aggregate sentiment
sentimentSum = sum([self.words[i] for i in intersection])
if sentimentSum >= 0.5:
self.Log(f"Longing {article.Symbol.Underlying} with sentiment score of {sentimentSum}")
self.SetHoldings(article.Symbol.Underlying, sentimentSum / 5)
self.lastTrade = self.Time
if sentimentSum <= -0.5:
self.Log(f"Shorting {article.Symbol.Underlying} with sentiment score of {sentimentSum}")
self.SetHoldings(article.Symbol.Underlying, sentimentSum / 5)
self.lastTrade = self.Time
def OnSecuritiesChanged(self, changes):
for r in changes.RemovedSecurities:
# If removed from the universe, liquidate and remove the custom data from the algorithm
self.Liquidate(r.Symbol)
self.RemoveSecurity(Symbol.CreateBase(BenzingaNews, r.Symbol, Market.USA))