/moving-averages

The collections of simple, weighted, exponential, smoothed moving averages.

Primary LanguageJavaScriptOtherNOASSERTION

Build Status Coverage

WARNING

This module is lack of maintainance.

If you are familiar with python programming maybe you could check stock-pandas which provides powerful statistic indicators support, and is backed by numpy and pandas, The performance of 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 utility methods for Moving average, including:

And moving-averages will also handle empty values.

install

$ npm i moving-averages

usage

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

// 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>

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

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