Finmath NPM | npm.io

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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.

finmath

The complete collection of mathematical utility methods for FinTech , including:

Moving averages
MACD
Bollinger bands
Standard deviations
Highest high values / Lowest low values

And all finmath methods also handle empty values.

install

$ npm i finmath

usage

import {
  ma, dma, ema, sma, wma,
  macd,
  boll,
  sd,
  hhv, llv,
  add, sub, mul, div
} from 'finmath'

ma([1, 2, 3, 4, 5], 2)
// [<1 empty item>, 1.5, 2.5, 3.5, 4.5]

Simple Moving Average: `ma(data, size)`

type Data = EmptyableArray<number>

data Data 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 Data

Type Array<number|Empty> represents an array of numbers or empty items. And every method of finmath does NOT accepts items that are not numbers.

[1,, 2, 3] // OK ✅

[1, undefined, 2, 3] // NOT OK ❌

[1, null, 2, 3] // NOT OK ❌

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 Data 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 Data

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 Data

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 Data

Weighted Moving Average: `wma(data, size)`

Calculates convolution of the datum points with a fixed weighting function.

Returns Data

MACD: macd(data, slowPeriods?, fastPeriods?, signalPeriods?)

MACD, short for Moving Average Convergence / Divergence, is a trading indicator used in technical analysis of stock prices, created by Gerald Appel in the late 1970s.

data Data the collection of prices
slowPeriods? number=26 the size of slow periods. Defaults to 26
fastPeriods? number=12 the size of fast periods. Defaults to 12
signalPeriods? number=9 the size of periods to calculate the MACD signal line.

Returns MACDGraph

macd(data)

// which returns:
// {
//   MACD: <Array>,
//   signal: <Array>,
//   histogram: <Array>
// }

struct `MACDGraph`

MACD Data the difference between EMAs of the fast periods and EMAs of the slow periods.
signal Data the EMAs of the MACD
histogram Data MACD minus signal

In some countries, such as China, the three series above are commonly known as:

MACD       -> DIF
signal     -> DEA
histogram  -> MACD

Bollinger Bands: boll(data, size?, times?, options?)

boll([1, 2, 4, 8], 2, 2)
// {
//   upper: [, 2.5, 5, 10],
//   mid  : [, 1.5, 3, 6],
//   lower: [, 0.5, 1, 2]
// }

data Data the collection of data
size? Number=20 the period size, defaults to 20
times? Number=2 the times of standard deviation between the upper band and the moving average.
options? Object= optional options
- ma? Data= the moving averages of the provided datum and period size. This option is used to prevent duplicate calculation of moving average.
- sd? Data= the standard average of the provided datum and period size

Returns Array<Band> the array of the Band object.

interface Band {
  // the value of the upper band
  upper: number
  // the value middle band (simple moving average)
  mid: number
  // the value of the lower band
  lower: number
}

Standard deviations: sd(data, size)

data Data the collection of data
size number the sample size of

Returns Data the array of standard deviations.

sd([1, 2, 4, 8], 2)         // [<1 empty item>, 0.5, 1, 2]

sd([1, 2, 3, 4, 5, 6], 4)
// [
//   <3 empty items>,
//   1.118033988749895,
//   1.118033988749895,
//   1.118033988749895
// ]

Highest High Values: hhv(data, periods)

data Data the array of closing prices.
periods number the size of periods

Returns Data the highest high values of closing prices over the preceding periods periods (periods includes the current time).

const array = [1, 2, 4, 1]

hhv(array, 2)    // [, 2, 4, 4]
hhv(array)       // 4
hhv(array, 5)    // [<4 empty items>]
hhv(array, 1)    // [1, 2, 4, 1]

hhv(array, 2)    // [, 1, 2, 2]