Concept family
Machine Learning
Learned models on market data — kernels, clustering, neural networks, and ensembles.
Machine learning enters technical analysis as an estimation toolkit rather than a black box: methods that fit themselves to the data instead of relying on fixed formulas. On charts these tend to be compact, fitted on the fly to the available history. Nearest-neighbor lookups, kernel smoothers, and k-means state detection are the most common approaches in the Library, with neural networks and probabilistic models in smaller numbers.
The pieces fit together as a pipeline. Learned models map engineered inputs to forecasts; feature, label, and calibration choices decide whether those forecasts mean anything; regime detection tells other tools what kind of market they are in; similarity search treats history as memory; and voting blends imperfect signals into one. The caveat throughout: markets are nonstationary, so anything fitted to the past can degrade out of sample.
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kNN Analog Forecasting
The most common approach in this family: match recent price action to its nearest historical neighbors and project what tended to follow.
13 indicators
K-means Regime Clustering
Learns a handful of market states directly from the data and tags each bar with the closest match.
6 indicators
Kernel Regression
Estimates the underlying trend with distance-weighted averaging, smoothing price without assuming any fixed functional form.
6 indicators
Learned models
The models themselves, from kernel smoothers and Gaussian processes to support vector machines, neural networks, and reinforcement learning, each fitted to market data to produce an estimate, a classification, or a decision.
Features & pipeline
Everything around the model itself: engineering inputs, reducing dimensions, defining labels and horizons, updating the fit as new bars arrive, and calibrating raw outputs into usable probabilities.
Regimes & clustering
Unsupervised methods that sort market conditions into a small set of states, such as trending, ranging, or volatile, so other tools can adapt to the current environment.
Similarity & memory
Forecasting by analogy: find the historical stretches most similar to the present and study what followed them.
Ensembles
Several imperfect signals combined by vote or weight, on the reasoning that independent errors partly cancel.