BERTEUR
BERT / Euro
crypto

Hey everyone,
I've been messing around lately with the idea of pulling sentiment from finance and business news to see if it gives any edge on whether a stock might pop or drop the next day or two. It is now basically just basic positive/negative classification on recent articles.
I ended up using FinBERT (that finance-tuned BERT model) because it seems to handle words like "earnings beat" or "supply chain issues" way better than generic sentiment tools. It spits out a score from 0-1 on how positive or negative the headline feels, and then I naively mapped anything strongly positive = "BULLISH" signal and negative = "BEARISH". Super simple, probably too simple.
For tickers, I tried to spot mentioned companies (using some basic regex + a ticker list) and tag rough impact based on the sentiment direction. Sometimes it catches obvious ones like if "Rivian" is in a headline about upgrades, but it definitely misses context or sarcasm a lot.
I set it up to refresh every few hours automatically so it's always got the freshest batch of \~20-40 headlines from major sources. The output is just a plain JSON file with the title, sentiment label, confidence score, the crude bullish/bearish tag, and any detected tickers with a quick positive/neutral/negative flag. You could hit it with a normal GET request if you wanted the latest update.
Honestly, I'm not even sure how reliable this kind of thing is in practice. From what I've read in older threads here and in algotrading, raw headline/description sentiment can be super noisy, and markets front-run news anyway, or the real move comes from analyst notes / whispers that aren't public yet.
So I'm genuinely curious what you experienced traders think:
\- Do you guys bother with news sentiment at all for trading decisions, or is it better to stick to price/volume/technicals/fundamentals?
\- If you do use it, how do you weight it? Like, do you aggregate over multiple articles, look at change in sentiment day-over-day, filter by source credibility, or focus only on high-impact events
I'm not selling anything or building a product. If anyone's done similar experiments, I'd love to hear about it, or even contribute to my repo!
Thanks for any thoughts!

Hey everyone,
I've been messing around lately with the idea of pulling sentiment from finance and business news to see if it gives any edge on whether a stock might pop or drop the next day or two. It is now basically just basic positive/negative classification on recent articles.
I ended up using FinBERT (that finance-tuned BERT model) because it seems to handle words like "earnings beat" or "supply chain issues" way better than generic sentiment tools. It spits out a score from 0-1 on how positive or negative the headline feels, and then I naively mapped anything strongly positive = "BULLISH" signal and negative = "BEARISH". Super simple, probably too simple.
For tickers, I tried to spot mentioned companies (using some basic regex + a ticker list) and tag rough impact based on the sentiment direction. Sometimes it catches obvious ones like if "Rivian" is in a headline about upgrades, but it definitely misses context or sarcasm a lot.
I set it up to refresh every few hours automatically so it's always got the freshest batch of \~20-40 headlines from major sources. The output is just a plain JSON file with the title, sentiment label, confidence score, the crude bullish/bearish tag, and any detected tickers with a quick positive/neutral/negative flag. You could hit it with a normal GET request if you wanted the latest update.
Honestly, I'm not even sure how reliable this kind of thing is in practice. From what I've read in older threads here and in algotrading, raw headline/description sentiment can be super noisy, and markets front-run news anyway, or the real move comes from analyst notes / whispers that aren't public yet.
So I'm genuinely curious what you experienced traders think:
\- Do you guys bother with news sentiment at all for trading decisions, or is it better to stick to price/volume/technicals/fundamentals?
\- If you do use it, how do you weight it? Like, do you aggregate over multiple articles, look at change in sentiment day-over-day, filter by source credibility, or focus only on high-impact events
I'm not selling anything or building a product. If anyone's done similar experiments, I'd love to hear about it, or even contribute to my repo!
Thanks for any thoughts!