# 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.

## 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.

- [Kernel Regression](https://www.luxalgo.com/library/concept/kernel-regression.md): 4 implementations
- [Gaussian Process Regression](https://www.luxalgo.com/library/concept/gaussian-process-regression.md): 2 implementations
- [Kernel Density Estimation](https://www.luxalgo.com/library/concept/kernel-density-estimation.md): 2 implementations
- [Support Vector Machines](https://www.luxalgo.com/library/concept/support-vector-machines.md): 1 implementations
- [Decision Trees](https://www.luxalgo.com/library/concept/decision-trees.md): 1 implementations
- [Gradient Boosting](https://www.luxalgo.com/library/concept/gradient-boosting.md): 1 implementations
- [Random Forest](https://www.luxalgo.com/library/concept/random-forest.md): build pending
- [Neural Networks](https://www.luxalgo.com/library/concept/neural-networks.md): build pending
- [LSTM / Recurrent Networks](https://www.luxalgo.com/library/concept/lstm-recurrent-networks.md): reference entry
- [Bayesian Classifiers](https://www.luxalgo.com/library/concept/bayesian-classifiers.md): build pending
- [Self-organizing Maps](https://www.luxalgo.com/library/concept/self-organizing-maps.md): build pending
- [Reinforcement-learning Policies](https://www.luxalgo.com/library/concept/reinforcement-learning-policies.md): build pending
- [LLM/transformer-derived Signals](https://www.luxalgo.com/library/concept/llm-transformer-derived-signals.md): reference entry

## 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.

- [Feature Engineering](https://www.luxalgo.com/library/concept/feature-engineering.md): 1 implementations
- [Feature Selection](https://www.luxalgo.com/library/concept/feature-selection.md): 1 implementations
- [Train/validation Discipline](https://www.luxalgo.com/library/concept/train-validation-discipline.md): 1 implementations
- [Online/incremental Learning](https://www.luxalgo.com/library/concept/online-incremental-learning.md): 1 implementations
- [Logistic Signal Calibration](https://www.luxalgo.com/library/concept/logistic-signal-calibration.md): 1 implementations
- [PCA](https://www.luxalgo.com/library/concept/pca.md): reference entry
- [Label Definition & Prediction Horizon](https://www.luxalgo.com/library/concept/label-definition-and-prediction-horizon.md): build pending
- [Model Overfitting](https://www.luxalgo.com/library/concept/model-overfitting.md): reference entry
- [Probability Calibration Curves](https://www.luxalgo.com/library/concept/probability-calibration-curves.md): reference entry

## 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.

- [K-means Regime Clustering](https://www.luxalgo.com/library/concept/k-means-regime-clustering.md): 6 implementations
- [Gaussian Mixture Models](https://www.luxalgo.com/library/concept/gaussian-mixture-models.md): 1 implementations
- [Hidden Markov / Markov-switching Regimes](https://www.luxalgo.com/library/concept/hidden-markov-markov-switching-regimes.md): reference entry
- [Spectral Clustering](https://www.luxalgo.com/library/concept/spectral-clustering.md): build pending

## Similarity & memory

Forecasting by analogy: find the historical stretches most similar to the present and study what followed them.

- [kNN Analog Forecasting](https://www.luxalgo.com/library/concept/knn-analog-forecasting.md): 7 implementations
- [Dynamic Time Warping Similarity](https://www.luxalgo.com/library/concept/dynamic-time-warping-similarity.md): 1 implementations
- [Matrix Profile](https://www.luxalgo.com/library/concept/matrix-profile.md): 1 implementations

## Ensembles

Several imperfect signals combined by vote or weight, on the reasoning that independent errors partly cancel.

- [Ensemble Voting of Signals](https://www.luxalgo.com/library/concept/ensemble-voting-of-signals.md): 1 implementations
- [Model Stacking](https://www.luxalgo.com/library/concept/model-stacking.md): 1 implementations
- [Bagging](https://www.luxalgo.com/library/concept/bagging.md): 1 implementations

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Source: https://www.luxalgo.com/library/family/machine-learning/ (LuxAlgo Library). Free to use with attribution: https://www.luxalgo.com/library/license/