The FinTech utility collections of simple, cumulative, and exponential moving averages.

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2304.0.62 years ago7 years agoMinified + gzip package size for moving-averages in KB


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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.
The complete collection of FinTech utility methods for Moving average, including:

And moving-averages will also handle empty values.


$ npm i moving-averages


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>

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