Concept
Online/incremental Learning
Online/incremental Learning is a Machine Learning concept. The Library holds 2 implementations, each one a working definition you can pull into Quant.
Top Online/incremental Learning indicators
2 total
What is Online/incremental Learning?
Online, or incremental, learning updates a model continuously as new data arrives instead of refitting it on a stored batch. Each bar runs the same loop: the model predicts, the realized value reveals an error, and the parameters take a small corrective step. Algorithms built for this regime include least mean squares (LMS) and its normalized variant (NLMS), recursive least squares (RLS), and stochastic gradient descent. They need constant memory and constant work per bar, so they run comfortably inside an indicator.
The appeal for markets is drift: relationships between features and returns change, and an incremental model tracks the current relationship rather than an average over years. The cost is a tuning knob, the learning rate or forgetting factor, that trades stability for responsiveness. Update too fast and the model chases noise; too slow and it lags regime change, the same dilemma every adaptive parameterization scheme faces. Nothing about the update rule guarantees the tracked relationship persists.
How traders use it
- As adaptive smoothing and forecasting: filter coefficients update each bar so the fitted line or one-step-ahead forecast keeps tracking current conditions, in the same family as the Kalman filter.
- As drift handling for signal models: a classifier or regression updated incrementally weights recent behavior more, avoiding the stale fit of a frozen training window.
- As a lightweight alternative to periodic refits: constant per-bar cost replaces scheduled batch retraining, which matters inside charting environments with tight compute limits.
Related concepts · Features & pipeline
Concept family
Machine Learning
32 concepts mapped · 21 in the Library
Online/incremental Learning FAQ
What is the difference between online learning and batch retraining?
Batch retraining refits the model from scratch on a stored window at intervals, so it adapts in steps and needs the full dataset each time. Online learning folds each new observation into the parameters immediately and can then discard it, adapting continuously with constant memory. Batch fits are usually more stable; online fits react faster but are more exposed to noisy updates.
Why use a forgetting factor in recursive least squares?
The forgetting factor down-weights old observations geometrically so the fit reflects recent conditions rather than the entire history. Values near 1 change the model slowly and smooth out noise; smaller values adapt quickly but can overreact to a few unusual bars. It is a stability-versus-responsiveness dial, no setting suits every regime, and sensitivity checking is worth the effort.
Build Online/incremental Learning your way.
Quant writes, tests, and refines it with you — then it runs on LuxAlgo charting or ports to TradingView.