# 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 TiingoNLPDemonstrationAlgorithm(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

    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)