See more than you can watch manually.
The model checks US and Italian equities with the same daily checklist, so opportunities are not limited to the charts already on your screen.
A quant model · Made accessible
From a vast equity universe to a clear shortlist.
nanoHedgeLABS ranks long and short setups across a vast equity universe and shows the reason, market context and risk behind every result. It brings the research workflow of a small quant fund into one public terminal — without managing your money or placing trades.
BEFORE Hundreds of charts → NOW One ranked shortlist
BEFORE Signals without context → NOW Signal, context and risk
BEFORE Different judgments → NOW The same rules every day
01 · What you get
The useful parts of a quant desk, in one place.
Know what deserves attention first.
Every setup is compared on one scale. The result is a priority list, not a promise about what happens next.
See where the idea stops making sense.
Each visible setup keeps its market context, invalidation and first objective beside the score.
02 · In plain English
How the model turns a market into a shortlist.
Scan the universe
The system checks the same group of stocks every day. Nothing gets attention just because it is popular.
Ask the same questions
Is the trend clear? Is a meaningful pattern present? Is there enough room for the idea to work?
Define the boundary
The model marks the condition that would make the original setup no longer valid.
Sort the shortlist
The strongest combinations rise to the top, with market and sector context still visible.
03 · Understand the model
You do not need to be a quant to follow the logic.
The research notes explain one part of the process at a time, using the same simple question: what is the model measuring, and why does it matter?
What is a market trigger?
A simple yes-or-no check that tells the model whether one useful condition is present.
Read the simple guide →What would prove the setup wrong?
Why every serious process needs a clear line in the sand before the outcome is known.
Read the simple guide →How does the model choose what comes first?
How many different setups become one practical shortlist without pretending to predict the future.
Read the simple guide →