The collections of simple, weighted, exponential, smoothed moving averages.
OTHER License
This module is lack of maintenance.
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 mathematical utility methods for FinTech , including:
And all finmath methods also handle empty values.
$ npm i finmath
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]
ma(data, size)
type Data = EmptyableArray<number>
Data
the collection of data inside which empty values are allowed. Empty values are useful if a stock is suspended.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 ❌
// 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.
dma(data, alpha, noHead)
Data
the coefficient or list of coefficients alpha
represents the degree of weighting decrease for each datum.
alpha
is a number, then the weighting decrease for each datum is the same.alpha
larger than 1
is invalid, then the return value will be an empty array of the same length of the original data.alpha
is an array, then it could provide different decreasing degree for each datum.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]
ema(data, size)
Calulates the most frequent used exponential average which covers about 86% of the total weight (when alpha = 2 / (N + 1)
).
Number
the size of the periods.Returns Data
sma(data, size, times)
Also known as the modified moving average or running moving average, with alpha = times / size
.
Number=1
Returns Data
wma(data, size)
Calculates convolution of the datum points with a fixed weighting function.
Returns Data
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
the collection of pricesnumber=26
the size of slow periods. Defaults to 26
number=12
the size of fast periods. Defaults to 12
number=9
the size of periods to calculate the MACD signal line.Returns MACDGraph
macd(data)
// which returns:
// {
// MACD: <Array>,
// signal: <Array>,
// histogram: <Array>
// }
MACDGraph
Data
the difference between EMAs of the fast periods and EMAs of the slow periods.Data
the EMAs of the MACD
Data
MACD
minus signal
In some countries, such as China, the three series above are commonly known as:
MACD -> DIF
signal -> DEA
histogram -> MACD
boll([1, 2, 4, 8], 2, 2)
// {
// upper: [, 2.5, 5, 10],
// mid : [, 1.5, 3, 6],
// lower: [, 0.5, 1, 2]
// }
Data
the collection of dataNumber=20
the period size, defaults to 20
Number=2
the times of standard deviation between the upper band and the moving average.Object=
optional options
Data=
the moving averages of the provided datum
and period size
. This option is used to prevent duplicate calculation of moving average.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
}
Data
the collection of datanumber
the sample size ofReturns 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
// ]
Data
the array of closing prices.number
the size of periodsReturns 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]
Instead, returns Data
the lowest low values.