Invested Pro – A Platform for Smarter Trading and Investing Education

Hacker News

I'm the creator of Invested Pro, an educational platform designed to help traders and investors improve their skills and strategies through expert-led courses and trade ideas. What is Invested Pro? Invested Pro is a platform that focuses primarily on trading, but also includes resources for investing. Whether you’re a trader looking to refine your strategies or an investor seeking to expand your knowledge, Invested Pro connects you with experienced instructors who share practical insights and trade ideas. Key Features: Instructor-Driven Trade Ideas: Gain access to trade ideas and strategies shared by seasoned traders, helping you to apply what you learn in real-world scenarios. Community: Through collaboration with other users and instructors, educating traders about proper risk management, where you can engage in discussions and share experiences with others. Why Invested Pro? After spending years trading and searching for solid education and guidance, I recognized a need for high-quality educational content that bridges the gap between theory and practice in both trading and investing. Invested Pro was built to address this need, focusing on learning from real traders and investors who bring practical, actionable insights. Looking for Feedback: I’m excited to share Invested Pro with the HN community and would love to hear your thoughts. Whether it’s feedback on the platform, ideas for new features, or suggestions for the Trading and Investing community.

Visit product View on Hacker NewsAug 13, 2024

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Did not reach leaderboard

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

91%91% 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.

AppSumoStrong fit for a featured deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

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

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

nativeThis product was originally launched on this platform.

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

24%24% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · 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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