The Hard Part of Building an AI Stock Screener Isn’t the LLM
What I learned turning vague investment ideas into structured NSE and BSE research—and why financial AI must explain its work.
TL;DR
People describe investment ideas as stories, while stock databases expect exact fields, operators, and time periods. Building a useful AI stock screener is therefore less about asking an LLM to “pick stocks” and more about translating ambiguous language into verifiable criteria, applying those criteria to structured data, and showing the user enough evidence to challenge the result.
Disclosure: I am the builder of EasyStock, the product discussed in this article. This is a product and engineering retrospective, not investment advice.
The Interface Mismatch
A person researching the Indian market might begin with a question like this:
Find profitable NSE and BSE companies with improving margins, low debt, and strong recent momentum.
The sentence feels clear to a human. To software, almost every important word is incomplete.
What counts as...
Copyright of this story solely belongs to hackernoon.com. To see the full text click HERE
