from AlgorithmImports import *
from QuantConnect.DataSource import *

class BenzingaNewsDataAlgorithm(QCAlgorithm):

    current_holdings = 0
    target_holdings = 0
    word_scores = {
        'good': 1, 'great': 1, 'best': 1, 'growth': 1,
        'bad': -1, 'terrible': -1, 'worst': -1, 'loss': -1}

    def initialize(self) -> None:
        self.set_start_date(2021, 1, 1)
        self.set_end_date(2021, 6, 1)
        self.set_cash(100000)
        
        # Requesting data
        self.aapl = self.add_equity("AAPL", Resolution.MINUTE).symbol
        self.benzinga_symbol = self.add_data(BenzingaNews, self.aapl).symbol
        
        # Historical data
        history = self.history(self.benzinga_symbol, 14, Resolution.DAILY)
        self.debug(f"We got {len(history)} items from our history request")
        
    def on_data(self, slice: Slice) -> None:
        if slice.contains_key(self.benzinga_symbol):
            # Assign a sentiment score to the news article
            content_words = slice[self.benzinga_symbol].contents.lower()
            score = 0
            for word, word_score in self.word_scores.items():
                score += (content_words.count(word) * word_score)
            self.target_holdings = int(score > 0)
        
        # Ensure we have AAPL data in the current Slice
        if not (slice.contains_key(self.aapl) and slice[self.aapl] is not None and not slice[self.aapl].is_fill_forward):
            return
        
        # Buy or sell if the sentiment has changed from our current holdings
        if self.current_holdings != self.target_holdings:
            self.set_holdings(self.aapl, self.target_holdings)
            self.current_holdings = self.target_holdings