Describe the kind of investor you are. The analyst checks the live market, prepares trade ideas within your risk limits, and waits for your approval before placing anything in the paper account.
AI-datacenter momentum; fits your “cautious tech” tilt without breaching the 15% position cap.
- Within position cap
- Cash floor kept
- Listed, liquid, above $3
Simulated — paper trading only.
The student’s finished workspace, running on live market data.
What your analyst handles
Start with your investing style
You might say, “cautious tech investor, prefers dividends, avoids meme stocks.” The analyst turns that into a profile with clear preferences and updates it whenever your views change.
Research on live markets
It screens market movers, reads news and indicators such as SMA, RSI, volatility, and drawdown, then prepares a target allocation and up to five proposed orders with an explanation for each one.
Rules run in code, not in the model.
Risk limits checked in code
Position caps, order limits, cash floors, and penny-stock filters are regular software rules. Any proposal that falls outside your limits is stopped before it reaches the order screen.
Fits your tech tilt; stays under the position cap.
You decide on every order
Each idea arrives as an order ticket. You can approve it, change the size, or reject it and explain why. The analyst uses that feedback during the next round of research.
Reports wait in your workspace when a run finishes.
Research that runs on schedule
Set a morning scan, a weekly portfolio review, or a sector deep dive. The report will be waiting in your workspace when the scheduled run finishes.
A market you can explore
The Discover tab brings together top movers, charts, holdings, and your watchlist. Open any US ticker to see the detail or send it to the analyst for a closer look.
Rules run in code, not in the model.
Fits your tech tilt; stays under the position cap.
Reports wait in your workspace when a run finishes.
Start with your investing style
You might say, “cautious tech investor, prefers dividends, avoids meme stocks.” The analyst turns that into a profile with clear preferences and updates it whenever your views change.
Research on live markets
It screens market movers, reads news and indicators such as SMA, RSI, volatility, and drawdown, then prepares a target allocation and up to five proposed orders with an explanation for each one.
Risk limits checked in code
Position caps, order limits, cash floors, and penny-stock filters are regular software rules. Any proposal that falls outside your limits is stopped before it reaches the order screen.
You decide on every order
Each idea arrives as an order ticket. You can approve it, change the size, or reject it and explain why. The analyst uses that feedback during the next round of research.
Research that runs on schedule
Set a morning scan, a weekly portfolio review, or a sector deep dive. The report will be waiting in your workspace when the scheduled run finishes.
A market you can explore
The Discover tab brings together top movers, charts, holdings, and your watchlist. Open any US ticker to see the detail or send it to the analyst for a closer look.
Set your preferences, then review each idea.
Open your private demo
A private $100k paper portfolio opens instantly, with sample holdings ready to explore.
Describe how you invest
Share your goals and limits in chat. The analyst drafts an investor profile for you to review before it becomes active.
It researches and proposes
Run research whenever you like or put it on a schedule. Every trade idea passes the same risk checks.
Review the order and leave feedback
Approved orders go to the paper account. If you reject one, your reason becomes useful context for the next report.
The product grew one feature at a time.
A past Lantr student began with a simple portfolio screen and released an early version. Live market tools, AI research, risk checks, user accounts, and scheduled reports came next. Lantr now hosts the finished project for visitors to explore.
Read the source on GitHub →Built from one student’s interest in finance and AI.
The student wanted to understand how market research, software, and investor judgment could work together. Building the product meant making each part work in a real interface.
Finance & Economics
Portfolio construction, risk limits, and market microstructure become working parts of the product.
Computer Science & AI
The AI uses market tools, remembers feedback, and works inside a separate rules-based risk layer.
Data Science & Math
Indicators, screeners, evaluation, and a live data pipeline carry market information into each report.
Try the full workflow with $100k in paper funds.
No signup. Your changes stay private and clear automatically after 24 hours.