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Insiderviz – Insider trading data and visualizations

Hacker News

Insiderviz – Insider trading data and visualizations

Hey HN, We built a much nicer way to view insider trading data. Most of the site is free to use, with a subscription for some additional features. It is most likely not bug free at this point so please give us feedback. The data is pulled from Form 4s in the SEC EDGAR system and then aggregated in our own backend. The site allows you to view a page dedicated to each company and insider, with information about their trades plotted with the stock data. There are also currently two screeners on the site, for companies and forms. The company screener allows you to filter for specific types of companies based on a set of criteria. The form screener is the same idea but for forms (trades). The cluster buys and top companies pages are saved permutations of these filters. In the future we plan to extend the functionality of the app significantly to include things like institutional holdings reports (Form 13f), annual reports (Form 10k) and more.

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Product HuntOn track for Day 1 leaderboard · Strong signals: visual · Missing: mac, agents, macos
68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
64%64% 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
58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: trading, way · Missing: mobile apps, ios, personal
51%51% 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
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
17%17% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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