Built a stock screener after getting frustrated with expensive tools

Hacker News

After years of using screeners that were missing features or were expensive, I built this free alternative available on desktop and mobile. You can filter and sort by all the essentials: Symbol, Name, Price, Market Cap, Change %, Change, Volume; P/E and Forward P/E ratios Real example that saved me hours: I was searching for (cheap & solid) stocks to sell options. I filtered with PE and Forward PE under 25, volume above 1 million, price minimum $25, and market cap over $10 billion and discovered a lot of new stocks that nobody talks about on youtube or elsewhere. Also I tried to make the design as good as I could because a lot of financial website are totally outdated in term of styling. Would love feedback from the HN community - contact us through the website if you have suggestions or need specific tools that would be useful to you Thank you for reading & I hope you get some value from it :)

Visit product View on Hacker NewsAug 23, 2025

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntUnlikely to reach the leaderboard · Strong signals: new, using · Missing: mac, agents, macos

47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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