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https://github.com/kaelzhang/finmath
The collections of simple, weighted, exponential, smoothed moving averages.
https://github.com/kaelzhang/finmath
dmarc dynamic-weighted-moving-average ema exponential-moving-average fintech ma math math-library moving-average sma weighted-moving-average
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The collections of simple, weighted, exponential, smoothed moving averages.
- Host: GitHub
- URL: https://github.com/kaelzhang/finmath
- Owner: kaelzhang
- License: other
- Created: 2015-12-12T04:04:16.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2021-03-02T03:29:33.000Z (over 3 years ago)
- Last Synced: 2024-11-07T01:16:48.516Z (6 days ago)
- Topics: dmarc, dynamic-weighted-moving-average, ema, exponential-moving-average, fintech, ma, math, math-library, moving-average, sma, weighted-moving-average
- Language: JavaScript
- Homepage:
- Size: 63.5 KB
- Stars: 55
- Watchers: 5
- Forks: 20
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://travis-ci.org/kaelzhang/finmath)
[](https://codecov.io/gh/kaelzhang/finmath)
# MAINTENANCE WARNING
This module is lack of maintenance.
If you are familiar with python programming maybe you could check [**stock-pandas**](https://github.com/kaelzhang/stock-pandas) which provides powerful statistic indicators support, and is backed by [`numpy`](https://numpy.org/) and [`pandas`](https://pandas.pydata.org/).
The performance of [**stock-pandas**](https://github.com/kaelzhang/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](https://en.wikipedia.org/wiki/Financial_technology) , including:
- [Moving averages](https://en.wikipedia.org/wiki/Moving_average)
- [MACD](https://en.wikipedia.org/wiki/MACD)
- [Bollinger bands](https://en.wikipedia.org/wiki/Bollinger_Bands)
- [Standard deviations](https://en.wikipedia.org/wiki/Standard_deviation)
- Highest high values / Lowest low values
And all finmath methods also handle empty values.
# Table of Contents
- [simple Moving Average (MA)](#simple-moving-average-madata-size)
- [Dynamic weighted Moving Average (DMA)](#dynamic-weighted-moving-average-dmadata-alpha-nohead)
- [Exponential Moving Average (EMA)](#exponential-moving-average-emadata-size)
- [Smoothed Moving Average (SMA)](#smoothed-moving-average-smadata-size-times)
- [Weighted Moving Average (WMA)](#weighted-moving-average-wmadata-size)
- [MACD](#MACD-macddata-slowPeriods-fastPeriods-signalPeriods)
- [BOLLinger bands (BOLL)](#bollinger-bands-bolldata-size-times-options)
- [Standard Deviations (SD)](#standard-deviations-sddata-size)
- [Highest High Values (HHV)](#highest-high-values-hhvdata-periods)
- [Lowest Low Values (LLV)](#lowest-low-values-llvdata-periods)
## install
```sh
$ npm i finmath
```
## usage
```js
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)`
```ts
type Data = EmptyableArray
```
- **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` represents an array of numbers or empty items. And every method of `finmath` does **NOT** accepts items that are not numbers.
```js
[1,, 2, 3] // OK ✅
[1, undefined, 2, 3] // NOT OK ❌
[1, null, 2, 3] // NOT OK ❌
```
#### Special Cases
```js
// 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`
```js
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`
```js
macd(data)
// which returns:
// {
// MACD: ,
// signal: ,
// histogram:
// }
```
### struct `MACDGraph`
- **MACD** `Data` the difference between [EMAs](https://www.npmjs.com/package/moving-averages#exponential-moving-average-emadata-size) 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:
```sh
MACD -> DIF
signal -> DEA
histogram -> MACD
```
## Bollinger Bands: boll(data, size?, times?, options?)
```js
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` the array of the `Band` object.
```ts
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.
```js
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).
```js
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]
```
## Lowest Low Values: llv(data, periods)
Instead, returns `Data` the lowest low values.
## License
[MIT](LICENSE)