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feat(LINREG): Add Linear Regression
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‎.lintstagedrc.json

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{
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"*.{js,jsx,ts,tsx}": ["npm run fix:code"]
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"*.{js,jsx,ts,tsx}": ["eslint --fix"]
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}

‎README.md

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1. Double Exponential Moving Average (DEMA)
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1. Dual Moving Average (DMA)
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1. Exponential Moving Average (EMA)
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1. Linear Regression (LINREG)
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1. Mean Absolute Deviation (MAD)
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1. Momentum (MOM / MTM)
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1. Moving Average Convergence Divergence (MACD)

‎docs/classes/LinearRegression.html

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<!doctype html>
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<html class="default" lang="en" data-base="..">
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<head>
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<meta charset="utf-8" />
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<meta http-equiv="x-ua-compatible" content="IE=edge" />
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<title>LinearRegression | trading-signals</title>
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<meta name="description" content="Documentation for trading-signals" />
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<meta name="viewport" content="width=device-width, initial-scale=1" />
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<svg width="16" height="16" viewBox="0 0 16 16" fill="none">
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<use href="../assets/icons.svg#icon-search"></use>
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</svg>
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<input type="text" id="tsd-search-field" aria-label="Search" />
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</div>
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<div class="field"><div id="tsd-toolbar-links"></div></div>
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<ul class="results">
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<li class="state loading">Preparing search index...</li>
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<li class="state failure">The search index is not available</li>
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</ul>
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<a href="../index.html" class="title">trading-signals</a>
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</div>
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</div>
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</header>
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<div class="container container-main">
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<div class="col-content">
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<div class="tsd-page-title">
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<ul class="tsd-breadcrumb">
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<li><a href="../modules.html">trading-signals</a></li>
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<li><a href="LinearRegression.html">LinearRegression</a></li>
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</ul>
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<h1>Classes LinearRegression and FasterLinearRegression</h1>
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</div>
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<section class="tsd-panel tsd-comment">
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<div class="tsd-comment tsd-typography">
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<p>Linear Regression (LinearRegression) Type: Trend</p>
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<p>
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Linear regression is a statistical method used to find relationships between variables. In trading, it is
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used to identify and analyze price trends by fitting a straight line through a set of data points. This
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implementation calculates the basic regression line parameters (slope, intercept) and predicts the next
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value.
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</p>
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</div>
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</section>
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<section class="tsd-panel tsd-hierarchy">
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<h4>Hierarchy</h4>
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<ul class="tsd-hierarchy">
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<li>
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<span>TechnicalIndicator</span>
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<ul class="tsd-hierarchy">
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<li><span class="target">LinearRegression</span></li>
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<li><span class="target">FasterLinearRegression</span></li>
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</ul>
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</li>
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</ul>
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</section>
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<section class="tsd-panel-group tsd-index-group">
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<section class="tsd-panel tsd-index-panel">
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<h3>Properties</h3>
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<ul class="tsd-index-list">
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<li><a href="#prices">prices</a></li>
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<li><a href="#period">period</a></li>
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</ul>
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</section>
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</section>
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<section class="tsd-panel">
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<h3>Constructor</h3>
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<ul class="tsd-signatures">
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<li class="tsd-signature">
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<span class="tsd-signature-keyword">new</span> <span class="tsd-kind-class">LinearRegression</span>
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<span class="tsd-signature-symbol">(</span>
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<span class="tsd-kind-parameter">config</span>
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<span class="tsd-signature-symbol">: </span>
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<span class="tsd-signature-type">LinearRegressionConfig</span>
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<span class="tsd-signature-symbol">)</span>
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</li>
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</ul>
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<h4>Parameters</h4>
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<table>
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<thead>
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<tr>
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<th>Name</th>
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<th>Type</th>
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<th>Description</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td><code>config</code></td>
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<td><span class="tsd-signature-type">LinearRegressionConfig</span></td>
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<td>Configuration object containing the period for regression analysis</td>
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</tr>
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</tbody>
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</table>
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</section>
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<section class="tsd-panel">
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<h3>Methods</h3>
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<ul class="tsd-signatures">
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<li class="tsd-signature">
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<span class="tsd-kind-method">update</span>
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<span class="tsd-signature-symbol">(</span>
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<span class="tsd-kind-parameter">price</span>
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<span class="tsd-signature-symbol">: </span>
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<span class="tsd-signature-type">BigSource | number</span>,
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<span class="tsd-kind-parameter">replace</span>
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<span class="tsd-signature-symbol">: </span>
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<span class="tsd-signature-type">boolean</span>
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<span class="tsd-signature-symbol">)</span>
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<span class="tsd-signature-symbol">: </span>
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<span class="tsd-signature-type">LinearRegressionResult | FasterLinearRegressionResult</span>
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<span class="tsd-signature-symbol"> | </span>
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<span class="tsd-signature-type">null</span>
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</li>
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</ul>
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<h4>Parameters</h4>
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<table>
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<thead>
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<tr>
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<th>Name</th>
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<th>Type</th>
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<th>Description</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td><code>price</code></td>
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<td><span class="tsd-signature-type">BigSource | number</span></td>
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<td>The new price value</td>
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</tr>
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<tr>
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<td><code>replace</code></td>
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<td><span class="tsd-signature-type">boolean</span></td>
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<td>Whether to replace the last value</td>
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</tr>
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</tbody>
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</table>
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<h4>Returns</h4>
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<p>The updated regression results or null if insufficient data</p>
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<h3>Results</h3>
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<p>Both implementations return an object containing:</p>
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<ul>
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<li><code>prediction</code>: The predicted value for the next period</li>
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<li><code>slope</code>: The slope of the regression line</li>
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<li><code>intercept</code>: The y-intercept of the regression line</li>
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</ul>
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<h3>Optimizations</h3>
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<p>
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The FasterLinearRegression implementation includes special handling for perfect linear trends and uses
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native JavaScript numbers for better performance.
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</p>
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</section>
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</div>
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</div>
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<footer>
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<p class="tsd-generator">Generated using <a href="https://typedoc.org/" target="_blank">TypeDoc</a></p>
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</footer>
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</body>
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</html>

‎docs/classes/LinearRegression.md

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[trading-signals](../README.md) / [Exports](../modules.md) / LinearRegression
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# Class: LinearRegression
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Linear Regression (LinearRegression) Type: Trend
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Linear regression is a statistical method used to find relationships between variables. In trading, it is used to identify and analyze price trends by fitting a straight line through a set of data points. This implementation calculates the basic regression line parameters (slope, intercept) and predicts the next value.
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## Hierarchy
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- [`TechnicalIndicator`](TechnicalIndicator.md)\<[`LinearRegressionResult`](../interfaces/LinearRegressionResult.md), `BigSource`\>
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↳ **`LinearRegression`**
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## Table of contents
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### Constructors
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- [constructor](LinearRegression.md#constructor)
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### Properties
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- [prices](LinearRegression.md#prices)
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- [period](LinearRegression.md#period)
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### Accessors
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- [isStable](LinearRegression.md#isstable)
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### Methods
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- [update](LinearRegression.md#update)
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- [getResultOrThrow](LinearRegression.md#getresultorthrow)
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## Constructors
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### constructor
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• **new LinearRegression**(`config`)
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#### Parameters
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| Name | Type | Description |
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| :------- | :----------------------- | :----------------------------------------------------------------- |
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| `config` | `LinearRegressionConfig` | Configuration object containing the period for regression analysis |
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#### Overrides
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[TechnicalIndicator](TechnicalIndicator.md).[constructor](TechnicalIndicator.md#constructor)
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## Properties
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### prices
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• `Readonly` **prices**: `BigSource`[] = `[]`
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Array of price values used in the calculation
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### period
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• `Private` `Readonly` **period**: `number`
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The number of periods to analyze
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## Methods
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### update
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▸ **update**(`price`, `replace`): `LinearRegressionResult` | `null`
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Update the indicator's state with a new price value
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#### Parameters
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| Name | Type | Description |
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| :-------- | :---------- | :-------------------------------- |
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| `price` | `BigSource` | The new price value |
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| `replace` | `boolean` | Whether to replace the last value |
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#### Returns
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`LinearRegressionResult` | `null`
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The updated regression results or null if insufficient data
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### getResultOrThrow
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▸ **getResultOrThrow**(): `LinearRegressionResult`
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Get the current result or throw an error if the indicator is not stable
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#### Returns
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`LinearRegressionResult`
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The current regression results containing slope, intercept, and prediction
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#### Throws
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`NotEnoughDataError`
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If there is not enough data to calculate the regression
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## Accessors
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### isStable
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• `get` **isStable**(): `boolean`
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#### Returns
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`boolean`
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Whether the indicator has enough data to provide stable results
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# Class: FasterLinearRegression
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A faster implementation of LinearRegression using number type instead of Big.js for calculations.
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## Hierarchy
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- [`TechnicalIndicator`](TechnicalIndicator.md)\<[`FasterLinearRegressionResult`](../interfaces/FasterLinearRegressionResult.md), `number`\>
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↳ **`FasterLinearRegression`**
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## Description
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This implementation provides the same functionality as LinearRegression but uses native JavaScript numbers instead of Big.js for better performance. It includes special optimizations for perfect linear trends.

‎docs/interfaces/LinearRegressionResults.md

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[trading-signals](../README.md) / [Exports](../modules.md) / LinearRegressionResult
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# Interface: LinearRegressionResult
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Results returned by the Linear Regression (LinearRegression) indicator calculations.
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## Properties
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### prediction
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• **prediction**: `Big` | `number`
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The predicted value for the next period based on the regression line.
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---
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### slope
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• **slope**: `Big` | `number`
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The slope of the regression line, representing the average rate of change per period.
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---
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### intercept
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• **intercept**: `Big` | `number`
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The y-intercept of the regression line, representing the base level (value at x=0).
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---
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### rSquared
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• **rSquared**: `Big`
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The R-squared value (coefficient of determination), indicating how well the regression line fits the data (0-1)
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---
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### standardError
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• **standardError**: `Big`
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The standard error of the regression, measuring the average distance between the data points and the regression line
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---
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### meanX
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• **meanX**: `Big`
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The mean of the x values (time periods)
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---
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### meanY
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• **meanY**: `Big`
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The mean of the y values (prices)
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---
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### confidenceInterval
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• **confidenceInterval**: `Object`
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Confidence intervals for the regression parameters
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#### Type declaration
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| Name | Type | Description |
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| :---------- | :------------- | :-------------------------------------------------------------- |
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| `slope` | [`Big`, `Big`] | The lower and upper bounds of the slope confidence interval |
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| `intercept` | [`Big`, `Big`] | The lower and upper bounds of the intercept confidence interval |

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