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Zenly – Investment research platform built around a quality/value score

Hacker News

Zenly – Investment research platform built around a quality/value score

I've spent the past year building what I believe to be a comprehensive investment research and tracking platform. As an avid investor who incorporates a few strategies for investing (index investing, factor tilts, some quality stocks at good valuations, a bit of dividends), I was continuously jumping between different websites and tools for different needs, most of which were missing the exact thing I needed. I wanted one place which had visual representation of stock fundamentals without overwhelming me with data, a portfolio tracker that integrates with my brokerage, a stock screener which filters for PEG ratio the correct way (trailing P/E vs long term eps growth rate), to see a full exposure analysis and AI insights on my portfolio, back-testing capabilities, and a quick way to evaluate how good an investment is (since the process I was doing for evaluating stocks was the same one every time - PEG ratio, expected growth, debt, revenue growth, ROIC). I wanted something simple yet powerful that gives me exactly the information I need, and nothing more. Zenly is a result of many months of work of re-iteration, improvement, and research. It's a passion project that eventually became my number one used tool for all my investing needs. What sets Zenly apart: - The zen score (Methodology: https://zenlyfinancial.com/zen-score-explained ). This is a score I created based on the quality factor premium literature along with hefty work I personally did on running regression analysis on stock behavior based on fundamentals for the past 20 years. The score takes into account fundamental metrics that have been found to be differentiators in stock returns - 5y revenue growth, estimated eps growth, debt levels, ROIC, and forward looking valuation. Grading every publicly US-listed equity with this score, basically creating a desirability score for each. - Intuitive visualizations of stock fundamentals, along with builtin AI analysis for the thesis, bear case, bull case, and earnings sustainability. - In-depth exposure analysis, income tracking, performance tracking, and aggregate quality data on the users portfolio, all automatically connected to your brokerage. - Stock screening, back testing, aggregate fundamental simulation on pre-built portfolios. It's priced at 5usd/month to cover my hosting, financial apis and AI usage cost, with a 14 day free trial no credit card required. I would love any feedback, and I welcome any questions regarding the Zen Score, my stack, or anything else that comes to mind.

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, ios · Missing: supports, reddit linkedin, podcasting
95%95% 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.
TrustMRRFits verified-revenue profile · Strong signals: ios, personal, month · Missing: mobile apps, entrepreneurs, apps
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, intuitive, host · Missing: plus, reviews, exclusive
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: user, visual, apis · Missing: mac, agents, macos
43%43% 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: lua, ide, io · Missing: https docs, excited, just released
40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: revenue, growth · Missing: arr, mrr, profit
19%19% 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.

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