I made an interactive stock watchlist maker

Hacker News

Hi HN, I’m the maker behind Dollarsign. Over the pandemic I've done a lot of financial research and I wanted a tool to help collect it and automatically generate watchlists. This site is a side-project for that. It uses 15-min delayed data from all markets along with live news. Since there are other people probably thinking similar things as your own ideas, there's also the ability to fork, watch and star watchlists in a github-like fashion. Let me know what you think. You can jump directly to making watchlists here: https://app.dollarsign.ai/w/new Cheers! [edit] fixed terms/privacy/disclaimer links on www page.

Visit product View on Hacker NewsOct 23, 2022

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

32%32% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

11%11% 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.

Incorrect prediction on native model

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