A plain-English walkthrough for first-time users: what the model output means, how confidence should be interpreted, and how to use it as part of a better research workflow.
The daily model classification is designed to help you organise your attention. It gives you a structured view of whether a ticker currently looks stronger, weaker, or more neutral based on the signals the model tracks.
It is not a prediction of guaranteed price movement. Think of it as a research prompt: it tells you where to look more closely, not what to do blindly.
The confidence percentage reflects how strongly the current data pattern matches what the model has learned from similar conditions. A higher percentage means the model sees a clearer pattern. A lower percentage means the picture is more mixed.
Confidence is about pattern strength, not certainty. A 78% confidence score does not mean there is a 78% chance the stock will rise. It means the model has stronger conviction in its classification based on the information available.
Do not treat a strong classification as a buy signal on its own. Do not assume a weak classification means a company is permanently poor quality. And do not ignore context such as macro news, liquidity, or sharp event-driven moves.
The model helps you prioritise research. Your judgment still matters — very much so.
No. Confidence score reflects how strongly the pattern matches historical similar conditions, not the probability of a price move. A 75% confidence means the model has strong conviction based on the data, but the stock can still move in any direction based on news, sentiment, or unexpected events.
No. Treat the classification as a research prompt, not a trading signal. Always cross-check with market context, company news, sector trends, and your own risk rules before making any decision. The model helps you prioritize where to look more closely, not what to buy or sell blindly.
Yes. As new price data, volume patterns, and technical indicators update, the model's classification can shift. This is normal and reflects changing market conditions. Watch for sustained directional trends rather than reacting to single-day changes.
That's valuable information. Divergence between your view and the model often reveals blind spots. Either the model is highlighting a pattern you missed, or your qualitative research has captured something the quantitative model doesn't. Investigate both perspectives before deciding.
Learn what these classification terms mean and how to use them in your research.
A step-by-step guide that includes how to use AI classifications in your research workflow.
Understand the limits of AI analysis and where human judgment still matters most.
Follow a structured process that pairs market context with model classifications and ticker analysis.
Pair the daily classification with your watchlist and ticker detail review so the signal supports your thinking instead of replacing it.