I

I re-built 9 options trading SaaS in 3 months and made it free

Hacker News

I re-built 9 options trading SaaS in 3 months and made it free

Hi HN! I’m a solo founder who trades options, and I got tired of paying $100–$300/month for a bunch of different tools: one for GEX, one for backtesting, one for scanning, another for trade ideas, another for volatility… it felt like death by a thousand subscriptions. So I built a single website that replaces all of them and made it completely free. For anyone who trades options, it’s basically all your favorite money making tools in one place, with a super generous free tier so that you never need to pay unless you’re a crazy power user. What it currently includes: - Options backtester - GEX (Gamma Exposure) visualizations - Trade ideas & flow - Unusual options activity scanner - Probability calculators & DTE analytics - Strategy models & edge tools - …and a bunch more that I used to pay for across 10+ different sites Why I built it Over the last few years I found myself juggling way too many expensive subscriptions just to trade effectively. Some tools were $150/mo, others $80/mo, others locked basic features behind paywalls. It created a huge financial barrier for new traders — the exact people who need the tools the most. So I spent the last 3 months obsessing over every detail and building a single, clean, fast toolbox that gives traders everything in one place without the painful subscription stacking. Data Transparency The Toolbox uses ORATS institutional data, which normally costs me $600/mo. No shady data scraping. No questionable aggregators. Just a clean interface using high-quality options data. Who it’s for - Options sellers - Directional traders - Income traders - People learning options - And anyone tired of 5–10 subscriptions to do basic research Try it here https://optionstradingtoolbox.com Ask me anything! I’ll be active in the comments! Thanks for reading Merry Christmas, Austin (solo founder)

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
89%89% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month, trading, way · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: model, user, new · Missing: mac, agents, macos
38%38% 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: arr, saas, subscription · Missing: mrr, revenue, profit
29%29% 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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