Logistic regression
Combines every indicator into a single score and turns it into a probability that price will be higher within N days.
Why it matters
It's the baseline every other model has to beat: if a more complex model doesn't clearly outperform it, that extra complexity isn't paying for itself. It's also the easiest to audit — every indicator has a fixed, visible weight, so it's possible to see exactly how much each one contributes to the final prediction, something the tree-based models below can't offer as simply.
Build it yourself
Drag the indicators' combined score and watch the logistic function turn it into a probability.
Illustrative example numbers for practice — not real data.
How it works, step by step
- Each indicator (MVRV, SOPR, NVT Signal...) is multiplied by its own weight, learned from historical data during training: a positive weight means "when this indicator rises, the probability of a rise increases"; a negative one, the opposite.
- All of those products are added into a single combined score, which can be any number — positive, negative, large, or small.
- That score passes through the logistic ("sigmoid") function, which smoothly squashes it into a range between 0 and 1 — no combination of indicators can ever produce a probability of exactly 0% or 100%.
Predictions
These are the real predictions stored day by day, each one produced by walk-forward validation: every day's was produced by a model trained only on prior cycles. They're published for transparency, not because there's an edge behind them — for price direction, the project's research found none that is defensible.
Probability of a rise within 7 days
Probability of a rise within 30 days
Probability of a rise within 90 days
Data last updated on 2026-09-09.