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StokeStox
AI analyzes your stock portfolio in 3 seconds.
Most retail traders rely on emotion and noisy headlines. Engineered by a mathematics professor, StokeStox levels the playing field. Our dual-engine platform combines custom machine learning (tested with strict out-of-sample data) with Gemini 3.0 to generate clear 0-100 stock scores. Stop guessing and chat directly with institutional-grade data. Features include multi-portfolio sync, AI explanations, and walk-forward backtested signals accounting for realistic slippage.
Hi Product Hunt! 👋
I’m Michael. I’m a mathematics professor, a retail trader of 20+ years, and the solo developer behind StokeStox.
For two decades, I traded my own accounts and kept hitting the same wall. Despite my math background, I was frustrated by retail tools that felt like black boxes—either noisy sentiment trackers chasing social media hype or technical analysis that didn't hold up out-of-sample.
I knew institutional-grade math existed—I teach it—but there was no clean way to apply strict quantitative machine learning to my own daily trading. I actually lost followers on X when I shifted from "entertainment" to this deep math approach. But that's okay—I'm looking for the investors who care about Alpha, not hype.
🧠 Under the Hood:
I built custom ensemble ML models that calculate a strict 0-100 score for thousands of stocks based on years of financial data, backtested with realistic slippage. But because data without context is just noise, I’ve integrated Google’s latest Gemini AI directly into the engine. This means you aren’t just staring at a chart; you can actually chat with your portfolio to understand the "why" behind the movement through Explainable AI (xAI).
🛠️ The Stack:
Built solo using React/Node.js and Firebase for a snappy, real-time frontend, with Python (Pandas/Scikit-learn) handling the heavy ML pipelines and institutional feeds from FMP.
🎯 Why I’m here:
I didn't build this to stay on my local machine. I want to see if it holds up in the wild. I’m looking for data-driven traders and developers to battle-test this engine. Go score your own portfolios, run the AI Analyst, and honestly... try to break the system.
🎁 PH Special:
The scanner and basic scoring are free. If you want to push the system to its limits unlocking 250,000 AI tokens/month (upto 350 quantitative assistant queries) and full factor breakdowns, use the code FOUNDER40 at checkout for 40% off lifetime Pro access as an early tester.
I’ll be here all day to geek out over quant math, trading, and Node.js architecture. Let me know what you think!
About StokeStox on Product Hunt
“AI analyzes your stock portfolio in 3 seconds.”
StokeStox was submitted on Product Hunt and earned 1 upvotes and 1 comments, placing #148 on the daily leaderboard. Most retail traders rely on emotion and noisy headlines. Engineered by a mathematics professor, StokeStox levels the playing field. Our dual-engine platform combines custom machine learning (tested with strict out-of-sample data) with Gemini 3.0 to generate clear 0-100 stock scores. Stop guessing and chat directly with institutional-grade data. Features include multi-portfolio sync, AI explanations, and walk-forward backtested signals accounting for realistic slippage.
On the analytics side, StokeStox competes within Fintech, Investing and Artificial Intelligence — topics that collectively have 539.6k followers on Product Hunt. The dashboard above tracks how StokeStox performed against the three products that launched closest to it on the same day.
Who hunted StokeStox?
StokeStox was hunted by Michael La Barbera. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of StokeStox including community comment highlights and product details, visit the product overview.
Michael La Barbera