Concept
Support Vector Machines
Support Vector Machines are Machine Learning concepts. The Library holds 1 implementations, each one a working definition you can pull into Quant.
SVM signals
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What are Support Vector Machines?
A support vector machine (SVM) is a supervised classifier that separates two classes with the boundary maximizing the margin, the distance to the nearest training points on each side. Those nearest points are the support vectors; they alone determine the boundary, and everything farther away could move without changing it. A regularization parameter (conventionally written C) trades margin width against training errors, and the kernel trick lets the model draw nonlinear boundaries by implicitly working in a higher-dimensional space, with radial basis function (RBF) kernels a common default.
In trading use, an SVM maps a vector of engineered features for each bar or setup into a class defined by the label and horizon: up versus down, trade versus skip, trend versus range. SVMs are historically favored for small-to-medium datasets with many features, where the margin objective provides some built-in resistance to overfitting. The raw output is a score reflecting how far an example sits from the boundary, useful for ranking conviction but not a probability without an explicit calibration step.
How traders use it
- Directional classification: train on labeled historical bars and take signals only when the model's output clears a margin threshold, so borderline cases are skipped rather than traded.
- Setup ranking: because distance from the boundary orders examples by how cleanly they sit in a class, an SVM can rank candidate setups and let only the clearest through a pipeline.
- Regime classification: trained on volatility and efficiency features, an SVM can label conditions as trend or range and gate which sub-strategy is allowed to fire.
Related concepts · Learned models
Concept family
Machine Learning
32 concepts mapped · 21 in the Library
Support Vector Machines FAQ
When is an SVM a reasonable choice for market data?
When the training set is modest and the feature count is high relative to it: the maximum-margin objective plus regularization gives some protection against overfitting, and kernels capture smooth nonlinear boundaries without manual feature crosses. On very large datasets, training cost grows steeply and tree ensembles or neural networks become the more common choice. As always, out-of-sample validation decides.
Does an SVM output probabilities?
Not natively. The raw output is a signed score proportional to the example's distance from the decision boundary: the sign gives the class and the magnitude says how deep in that class's territory the example sits. Converting that score into a probability requires a calibration step such as Platt scaling, which fits a sigmoid to held-out outputs. Calibrate before using SVM scores to size positions.
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