LightGBM

The same idea as Gradient Boosting — trees correcting each other in a chain — but built to train much faster with many data columns at once.

Why it matters

Here, "columns" means every indicator on this site evaluated together. Recent literature (arXiv, not just vendor sources) finds this kind of model competitive with — sometimes better than — LSTM-style neural networks for daily series with a few thousand rows, like this one, without needing the data volume or training cost a neural network does.

Build it yourself

This model doesn't have its own interactive widget here: it combines the same sequential correction as Gradient Boosting with a speed optimization that doesn't lend itself to an honest numeric example without inventing unverified performance figures. The explanation above already covers what makes it different.

How it works, step by step

  1. It uses the same sequential-correction mechanism as Gradient Boosting (see the previous model): each new tree learns to correct the error of the ones before it.
  2. Instead of testing every possible split point of every indicator one by one (the classic method), it buckets values into a small number of "bins" before searching for the best split — much faster, with little loss of accuracy.
  3. It grows each tree by adding leaves wherever they reduce error the most, rather than level by level — it tends to reach good results with fewer trees overall.
Honest limitation. It isn't evaluated here against more recent architectures like Transformers for time series; the project's comparison is limited to this family of classic tabular machine-learning models.

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.

← All models