MOON AI - Stop drowning in tabs. Ask Moon AI about any stock.
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Chat with Moon AI, your intelligent stock market assistant. Get instant answers about stocks, market analysis, portfolio insights, and investment education.
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Maker
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Hey everyone! Maker here.
Moon AI started from a personal frustration: I was drowning in 47 open tabs — SEC filings, analyst reports, Reddit threads, Discord alpha — just to make one investment decision. The information wasn't the problem. The noise was.
So I built what I wished existed: an AI that doesn't just summarize headlines, but actually pulls live financial data, runs the numbers, and explains what matters in plain English.
What makes Moon AI different:
It's not a chatbot wrapper. Moon AI has 9 autonomous data tools that fetch real-time quotes, DCF valuations, insider trades, congressional holdings, and technical indicators on demand.
It shows, not just tells. Answers come with interactive charts, stock cards, and prediction visualizations — we call it Generative UI.
It thinks through 6 legendary investment frameworks (inspired by Dalio, Munger, Simons, and others) so you see every stock from multiple angles.
It scans your entire portfolio in seconds — just paste your holdings or upload a screenshot — and gives you a health score with specific risk warnings.
What it won't do: Tell you to buy or sell. Moon AI is a research and education tool. We organize the data, you make the decisions.
It covers 5,500+ US stocks, and it's completely free to start.
I'd love to hear how you approach stock research today — and what's broken about it. Your feedback will directly shape what we build next.
Report
Maker
Headline: I fired my $50k/year financial advisor and built a 174k-line AI engine solo. Stop reading 100-page 10-Ks and start listening to the debate.
The Problem: Financial Analysis is Broken
Let’s be real: Retail investors are stuck between a rock and a hard place. You either read 100-page 10-K filings written by lawyers for lawyers, watch "analysts" on YouTube who discovered RSI last week, or pay $50k/year for an advisor who tells you to "diversify and hold." These aren’t solutions; they are homework assignments.
The Solution: The Legends Council
I built StockExpertAI to be the "War Room" you never had. It’s a multi-agent orchestration layer where legendary investors actually debate your stock picks in real-time.
Buffett Agent: Moat analysis, intrinsic value, and owner earnings.
Dalio Agent: Macro regime detection and debt cycle positioning.
Simons Agent: Pure quant logic, mean reversion, and momentum factor decomposition.
These agents don't just talk; they challenge each other. When Buffett ignores a macro headwind that Dalio flags, the friction becomes your biggest insight.
The Architecture: Under the Hood
I didn't want to build "Product Theater"—features that look good in a demo but solve zero problems. I built a real engine.
1. The Dual-Brain Prediction Engine
We don’t rely on a single price target. We run two independent engines and measure their divergence:
Technical Ensemble: 5 models (Linear Regression, Holt’s Method, WMA, Momentum, and a Kalman Filter) with Bayesian dynamic weighting. Models must "earn" their influence based on recent historical accuracy ($reliability = 1 / (avgErrorVariance + ALPHA)$).
Facebook Prophet: Handles seasonality, changepoint analysis, and 2-year trend decomposition via a Python worker process.
When the engines disagree, we don't average them out. We show you the honest uncertainty instead of fake precision.
2. Deterministic Scoring (No LLM Guesswork)
Before the LLM even sees your ticker, our engine calculates the MoonShot Score (0-100). This is pure math based on ROE, D/E, FCF Yield, and technical momentum. The AI agents are strictly forbidden from overriding the math—their job is narrative and synthesis, not guessing numbers.
3. The Solo Founder Stack
Frontend: React/TypeScript with SSR for 6,000+ stock pages (Googlebot loves this).
Backend: Express with streaming Server-Sent Events (SSE). An audit takes 12s, but you see the "War Room" start debating in under 2s.
Kill the spectacle, keep the substance: The first version was a single AI. The debate mechanic (The Council) came in v3 once the core data loop was solid.
Regrets? I have a few: Spawning Python child processes from Node.js for Prophet workers was a mistake. Cold starts are a nightmare—I should have gone with a persistent worker pool.
Unfair Advantage: As a solo founder, my advantage is taste. A team of 20 would have built a dashboard with 50 tabs. I built a chat interface where you ask a question and get a high-conviction answer.
Replies
Headline: I fired my $50k/year financial advisor and built a 174k-line AI engine solo. Stop reading 100-page 10-Ks and start listening to the debate.
The Problem: Financial Analysis is Broken
Let’s be real: Retail investors are stuck between a rock and a hard place. You either read 100-page 10-K filings written by lawyers for lawyers, watch "analysts" on YouTube who discovered RSI last week, or pay $50k/year for an advisor who tells you to "diversify and hold." These aren’t solutions; they are homework assignments.
The Solution: The Legends Council
I built StockExpertAI to be the "War Room" you never had. It’s a multi-agent orchestration layer where legendary investors actually debate your stock picks in real-time.
Buffett Agent: Moat analysis, intrinsic value, and owner earnings.
Dalio Agent: Macro regime detection and debt cycle positioning.
Simons Agent: Pure quant logic, mean reversion, and momentum factor decomposition.
These agents don't just talk; they challenge each other. When Buffett ignores a macro headwind that Dalio flags, the friction becomes your biggest insight.
The Architecture: Under the Hood
I didn't want to build "Product Theater"—features that look good in a demo but solve zero problems. I built a real engine.
1. The Dual-Brain Prediction Engine
We don’t rely on a single price target. We run two independent engines and measure their divergence:
Technical Ensemble: 5 models (Linear Regression, Holt’s Method, WMA, Momentum, and a Kalman Filter) with Bayesian dynamic weighting. Models must "earn" their influence based on recent historical accuracy ($reliability = 1 / (avgErrorVariance + ALPHA)$).
Facebook Prophet: Handles seasonality, changepoint analysis, and 2-year trend decomposition via a Python worker process.
When the engines disagree, we don't average them out. We show you the honest uncertainty instead of fake precision.
2. Deterministic Scoring (No LLM Guesswork)
Before the LLM even sees your ticker, our engine calculates the MoonShot Score (0-100). This is pure math based on ROE, D/E, FCF Yield, and technical momentum. The AI agents are strictly forbidden from overriding the math—their job is narrative and synthesis, not guessing numbers.
3. The Solo Founder Stack
Frontend: React/TypeScript with SSR for 6,000+ stock pages (Googlebot loves this).
Backend: Express with streaming Server-Sent Events (SSE). An audit takes 12s, but you see the "War Room" start debating in under 2s.
Scale: 174,127 lines of code, 743 files, 1 developer, $0 funding.
Brutal Lessons from a Solo Build
Kill the spectacle, keep the substance: The first version was a single AI. The debate mechanic (The Council) came in v3 once the core data loop was solid.
Regrets? I have a few: Spawning Python child processes from Node.js for Prophet workers was a mistake. Cold starts are a nightmare—I should have gone with a persistent worker pool.
Unfair Advantage: As a solo founder, my advantage is taste. A team of 20 would have built a dashboard with 50 tabs. I built a chat interface where you ask a question and get a high-conviction answer.
The Beta is Live at StockExpertAI.com
I’m building the thing that makes Bloomberg Terminals unnecessary for 99% of investors. Join the beta today—no credit card, no catch.
Roast my architecture. I can take it.