Our Story
Built after years of late alerts and paid opinions — education, receipts, and weekly notes first. Research stack under rebuild, built in public.
The Origin Story
Paul Strong on why late watchlists and opaque “guru” picks pushed him to build a platform that leads with education, timestamps, and receipts — not hype.
Strong Stock Trading provides educational content and AI-powered market analysis tools. Past performance does not guarantee future results. Not financial advice.
The Trading Education Industry Is Broken
Retail traders were sold certainty. What arrived was late lists, crowded chat rooms, and excuses when the tape moved first.
I spent years doing what every retail trader does. I subscribed to popular alert services. I paid for watchlists. I sat through webinars where well-known trading educators told me their system was different, their picks were special, and their track record was unbeatable. Month after month, the subscription fees added up.
Here is what I actually got: watchlists that showed up after the move already happened. Alerts that came in when a stock was already up 30%. Chat rooms full of people chasing the same crowded trades. And when a pick went south, the excuses were always the same — the market was irrational, the setup was right but the timing was off, or maybe you should have taken profits earlier. It was never the system's fault.
The final straw was watching a service charge $300 per month for opinions disguised as signals. No backtesting. No data. No accountability. Just a guy on a livestream drawing lines on a chart and telling people to buy. I realized the whole model was backwards — and I decided to build something better.
What If AI Could Do It Better?
Static rules age. The bet was a research scoring system that can be rebuilt in public — education and receipts first, not a finished product promise.
I started building an automated scanning system from scratch. The first versions were simple — basic screeners, technical filters, the usual stuff you can find anywhere. They worked okay but they had the same problem as every other tool: static rules that break when the market shifts.
The next step was a weighted research scoring stack — multiple factors and risk gates that review candidates before anything is presented as a study idea. That stack is under active rebuild. Details stay proprietary; the public promise is education and transparency, not a live self-learning product.
That is how the research genome started: a scoring framework for study and review, not a guarantee of edge. We publish weekly notes and course material while the scoring system is rebuilt in public.
* Educational and research framing only. Any historical backtest or paper figures mentioned elsewhere are not live results. Past performance does not guarantee future results.
A research stack that argues with itself
Not one bot with a hot take — a research committee of roles that challenge each other before an idea clears review.
This is not marketed as one autonomous profit engine. It is a research review stack with distinct roles that challenge each other. Think of it as a study committee: scout, analyze, validate, debunk, monitor, and coordinate. Live fills today are still manual on Webull when Paul chooses to act on a research idea.
Scout (research)
Scans markets across multiple scan types and surfaces candidates that match the current research criteria for further review.
Analyst (research)
Deep-dives into each ticker’s technicals — price action, momentum, relative strength, and patterns — as study material, not advice.
Researcher
Validates each candidate with volume analysis, float data, catalyst checks, and sector context. Asks whether the story matches the numbers.
Debunker
Tries to disprove the thesis. Looks for hidden risks, dilution history, earnings traps, and reasons NOT to study the idea further.
Monitor (research)
Tracks active study positions and risk markers — targets, stops, time-based exits, and changing conditions — for education and review.
Coordinator
Orchestrates the research roles, resolves conflicts, and prioritizes review work so nothing important falls through the cracks.
Day One: AI vs Guru
March 23, 2026 — one early session on a small account. Same names, different entry times. The table below is a historical receipt, not a track record or a guarantee of edge.
Logged from one early live session. No cherry-picking claim here — and no claim that day one equals ongoing edge. Here is what was recorded that day.
| Ticker | SST Entry | Alert Service Entry | SST Result | Alert Service Result |
|---|---|---|---|---|
| BIAF | $3.75 | $4.20 | +$6.69 | Loss |
| SATL | Entered & scaled out | — | All tiers profitable | — |
| UGRO | Found before any alert | Late / never alerted | Early entry | — |
We do not hide losses. Trades are logged — winners and losers — for education and accountability. Transparency is the point; marketing “AI wins” is not. Check the AI vs Guru breakdown for timestamped entries when you want the full session notes.
* Past performance does not guarantee future results. These are actual results from one trading day on a small account. Individual results will vary.
Where We Are Going
A living stack — education, watchlists, and scanners that keep improving — not a finished product on a shelf.
Here is what we have built so far, and where it is heading.
10 Courses
95 modules and 250 quizzes covering stocks, options, futures, forex, ETFs, crypto, AI trading, and more.
7 Daily Watchlists
AI-generated watchlists from pre-market through weekend prep. Every day, fresh ideas backed by data.
Extended scanning
Research scans cover liquid names and ETFs across sessions. Tools can run when the market is open; people still decide.
Options pipeline
Options analysis tools in development — calls, puts, spreads, and Greeks as educational research material.
Scoring system under rebuild
Weighted research scoring is being rebuilt in public. Soft factor updates for study — not a live self-learning product promise.
Education access for everyone
The goal: make clear research tools and courses accessible to retail learners without the hype-alert model.
Built by a trader who needed the tool
Paul Strong
Background in trading and technology. Built the entire SST platform — every line of code, every agent, every algorithm, every pixel on this page. No VC money, no development team, no outsourced code. Just one trader who got fed up with paying for someone else's guesses.
Every feature you see on this platform exists because I needed it for my own trading first. This was not built by a marketing team trying to sell you something. It was built by a trader trying to solve his own problem — and then realizing other people have the same one.
Milestones — without the highlight-reel wash
See It For Yourself
Start free. Read the weekly notes. Learn at your pace — education first, not advice.