About Benzinga News Feed

The Benzinga News Feed dataset by Benzinga tracks US Equity news releases. The data covers about 1,250 articles per day across 8,000 Equities, starts in January 2016, and is delivered on a second frequency. This dataset is created by structuring the content produced by Benzinga's editorial team.

This dataset depends on the US Equity Security Master dataset because the US Equity Security Master dataset contains information on splits, dividends, and symbol changes.


About Benzinga

Benzinga was founded by Jason Raznick in 2010 with goal of connecting the world with news, data, and education that makes the path to financial prosperity easier for everyone, everyday. Benzinga provides access to real-time news for individual investors.


About QuantConnect

QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.


Algorithm Example

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.SetStartDate(2021, 1, 1)
        self.SetEndDate(2021, 6, 1)
        self.SetCash(100000)
        
        # Requesting data
        self.aapl = self.AddEquity("AAPL", Resolution.Minute).Symbol
        self.benzinga_symbol = self.AddData(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 OnData(self, slice: Slice) -> None:
        if slice.ContainsKey(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.ContainsKey(self.aapl) and slice[self.aapl] is not None and not slice[self.aapl].IsFillForward):
            return
        
        # Buy or sell if the sentiment has changed from our current holdings
        if self.current_holdings != self.target_holdings:
            self.SetHoldings(self.aapl, self.target_holdings)
            self.current_holdings = self.target_holdings

Example Applications

The Benzinga News Feed enables you to accurately design strategies harnessing real-time news releases. Examples include the following strategies: