WARNING

This module is still under development

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 utility methods for Moving average, including:

• simple moving average (MA)
• dynamic weighted moving average (DMA)
• exponential moving average (EMA)
• smoothed moving average (SMA)
• weighted moving average (WMA)

And moving-averages will also handle empty values.

install

$ npm i moving-averages

usage

import { dynamicWeightedMA, smoothingMA, ExponentialMA, MA, WeightedMA } 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> 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> | null

Special Cases

import { MA } from "moving-average-ts";

// 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

import { dynamicWeightedMA } from "moving-average-ts";

dynamicWeightedMA([1, 2, 3], 2); // [<3 empty items>]

dynamicWeightedMA([1, 2, 3], 0.5); // [1, 1.5, 2.25]

dynamicWeightedMA([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>

MIT