Most Reddit stock trackers just count mentions and hand you a score. Ropollo reads every mention in context — distinguishing genuine stock references from ordinary word collisions, preserving source evidence, and connecting social momentum with prices, SEC filings, and short-interest data. The goal isn't a buy signal. It's a research lead you can actually verify. Methodology is public. Limitations are documented. No black-box scores.
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Maker
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I built Ropollo because I was tired of stock trackers that just dump
mention counts into a leaderboard and call it a signal. The same
ticker letters mean completely different things depending on context
OPEN is a stock on some subreddits and an ordinary word on others.
Most tools count both. I wanted something that actually reads the
text before scoring it.
The first version surfaced a ticker with 3 mentions as a "breakout"
because the percentage math exploded off a near-zero baseline. That
was humbling. The current version gates low-mention candidates,
distinguishes familiar tickers from emerging ones, and connects
every surfaced name back to the original Reddit posts so you can
read the discussion yourself.
This isn't a prediction tool. It doesn't tell you what to buy.
It reduces the work of noticing and investigating a changing
conversation. The methodology page explains exactly what the system
measures and what it can't. If something looks off, you'll know
why and you'll have the source posts to check.
Happy to answer questions about the approach, the data sources,
or anything else.
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Love that the methodology is public and you preserve source evidence, honestly that alone sets it apart from the usual mention counters out there. One thing that would help though is a way to export the filtered mention feed with the surrounding thread context into a CSV or JSON file, so you can pipe it into your own research workflow without having to manually copy each post. Right now everything seems locked inside the dashboard which kind of defeats the verification angle.
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Maker
@aslhan345499 Thanks Aslıhan! There is currently a Download button on each ticker page, although your comment makes it clear that I need to communicate the export feature better.
I’m also interested in the surrounding-context part you mentioned. Would a CSV/JSON export containing each filtered mention, the original text, subreddit, timestamp and source link cover what you need, or would you want the full parent thread context included as well?
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The transparency angle is what sold me on this over the usual trackers. One thing that would make it even more useful for me: a way to export the raw mention data with context tags so I can plug it into my own backtests in Python. Right now most tools lock you into their dashboard, but if Ropollo is already doing the hard work of filtering real stock references from noise, letting researchers grab that cleaned dataset would be a huge differentiator.
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Maker
@sedatcanppkb Thanks Sedat that’s an interesting direction. For your backtests, what fields would you actually need in each exported record? For example: timestamp, ticker, subreddit, author, confidence classification, surrounding text and permalink? Also, would you prefer raw mentions or aggregated time buckets?
Love that the methodology is public and you preserve source evidence, honestly that alone sets it apart from the usual mention counters out there. One thing that would help though is a way to export the filtered mention feed with the surrounding thread context into a CSV or JSON file, so you can pipe it into your own research workflow without having to manually copy each post. Right now everything seems locked inside the dashboard which kind of defeats the verification angle.
@aslhan345499 Thanks Aslıhan! There is currently a Download button on each ticker page, although your comment makes it clear that I need to communicate the export feature better.
I’m also interested in the surrounding-context part you mentioned. Would a CSV/JSON export containing each filtered mention, the original text, subreddit, timestamp and source link cover what you need, or would you want the full parent thread context included as well?
The transparency angle is what sold me on this over the usual trackers. One thing that would make it even more useful for me: a way to export the raw mention data with context tags so I can plug it into my own backtests in Python. Right now most tools lock you into their dashboard, but if Ropollo is already doing the hard work of filtering real stock references from noise, letting researchers grab that cleaned dataset would be a huge differentiator.
@sedatcanppkb Thanks Sedat that’s an interesting direction. For your backtests, what fields would you actually need in each exported record? For example: timestamp, ticker, subreddit, author, confidence classification, surrounding text and permalink? Also, would you prefer raw mentions or aggregated time buckets?