moving-averages-js

[![Build Status](https://travis-ci.org/kaelzhang/moving-averages.svg?branch=master)](https://travis-ci.org/kaelzhang/moving-averages) [![Coverage](https://codecov.io/gh/kaelzhang/moving-averages/branch/master/graph/badge.svg)](https://codecov.io/gh/kaelzhang/moving-averages)    # WARNING This module is lack of maintainance. If you are familiar with python programming maybe you could check [**stock-pandas**](https://github.com/kaelzhang/stock-pandas) which provides powerful statistic indicators support, and is backed by [`numpy`](https://numpy.org/) and [`pandas`](https://pandas.pydata.org/), The performance of [**stock-pandas**](https://github.com/kaelzhang/stock-pandas) is many times higher than JavaScript libraries, and can be directly used by machine learning programs. **** # moving-averages The complete collection of [FinTech](https://en.wikipedia.org/wiki/Financial_technology) utility methods for [Moving average](https://en.wikipedia.org/wiki/Moving_average), including: - [simple moving average (MA)](#simple-moving-average-madata-size) - [dynamic weighted moving average (DMA)](#dynamic-weighted-moving-average-dmadata-alpha-nohead) - [exponential moving average (EMA)](#exponential-moving-average-emadata-size) - [smoothed moving average (SMA)](#smoothed-moving-average-smadata-size-times) - [weighted moving average (WMA)](#weighted-moving-average-wmadata-size) And `moving-averages` will also handle empty values. ## install ```sh $ npm i moving-averages ``` ## usage ```js import { ma, dma, ema, sma, wma } from 'moving-averages' ma([1, 2, 3, 4, 5], 2) // [<1 empty item>, 1.5, 2.5, 3.5, 4.5] ``` ## Simple Moving Average: `ma(data, size)` - **data** `Array.<Number|undefined>` the collection of data inside which empty values are allowed. Empty values are useful if a stock is suspended. - **size** `Number` the size of the periods. Returns `Array.<Number|undefined>` #### Special Cases ```js // If the size is less than `1` ma([1, 2, 3], 0.5) // [1, 2, 3] // If the size is larger than data length ma([1, 2, 3], 5) // [<3 empty items>] ma([, 1,, 3, 4, 5], 2) // [<2 empty items>, 0.5, 1.5, 3.5, 4.5] ``` And all of the other moving average methods have similar mechanism. ## Dynamic Weighted Moving Average: `dma(data, alpha, noHead)` - **data** - **alpha** `Number|Array.<Number>` the coefficient or list of coefficients `alpha` represents the degree of weighting decrease for each datum. - If `alpha` is a number, then the weighting decrease for each datum is the same. - If `alpha` larger than `1` is invalid, then the return value will be an empty array of the same length of the original data. - If `alpha` is an array, then it could provide different decreasing degree for each datum. - **noHead** `Boolean=` whether we should abandon the first DMA. Returns `Array.<Number|undefined>` ```js dma([1, 2, 3], 2) // [<3 empty items>] dma([1, 2, 3], 0.5) // [1, 1.5, 2.25] dma([1, 2, 3, 4, 5], [0.1, 0.2, 0.1]) // [1, 1.2, 1.38] ``` ## Exponential Moving Average: `ema(data, size)` Calulates the most frequent used exponential average which covers about 86% of the total weight (when `alpha = 2 / (N + 1)`). - **data** - **size** `Number` the size of the periods. Returns `Array.<Number|undefined>` ## Smoothed Moving Average: `sma(data, size, times)` Also known as the modified moving average or running moving average, with `alpha = times / size`. - **data** - **size** - **times** `Number=1` Returns `Array.<Number|undefined>` ## Weighted Moving Average: `wma(data, size)` Calculates convolution of the datum points with a fixed weighting function. Returns `Array.<Number|undefined>` ## Related FinTech Modules - [bollinger-bands](https://www.npmjs.com/package/bollinger-bands): Fintach math utility to calculate bollinger bands. - [s-deviation](https://www.npmjs.com/package/s-deviation): Math utility to calculate standard deviations. - [moving-averages](https://www.npmjs.com/package/moving-averages): The complete collection of utility methods for [Moving average](https://en.wikipedia.org/wiki/Moving_average). MIT