Options Trading Journal SaaS — $30K ARR, Broker Imports & Performance Analytics

Acquire.com

This subscription business is an options trading journal built for active traders who want a clearer way to review performance and make better decisions. Users can import broker spreadsheet exports or add trades manually, then track results by strategy, ticker, account, portfolio, premium, risk, DTE, and profit/loss. The product goes beyond basic logging by capturing thesis notes, adjustments, exit reasons, and expirations, making weekly trade reviews far more useful. With recurring revenue,...

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Actual performance

$2,500/yr

Did not reach leaderboard

Traction signals

Asking price$92,670

Annual revenue$30,000

Revenue multiple3.1x

Launch Intel predictions

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Acquire.comFits revenue-generating acquisition profile · Strong signals: arr, revenue, profit · Missing: mrr, profitable, margin

68%68% predicted probability of success on Acquire.com, 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

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: users, trading, way · Missing: mobile apps, ios, personal

64%64% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: reviews, users · Missing: plus, platform, intuitive

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

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

31%31% 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

25%25% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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