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month](http://img.shields.io/npm/dm/moving-averages.svg)](https://www.npmjs.org/package/moving-averages)\n-->\n<!-- optional dependency status\n[![Dependency Status](https://david-dm.org/kaelzhang/moving-averages.svg)](https://david-dm.org/kaelzhang/moving-averages)\n-->\n\n# WARNING\n\nThis module is lack of maintainance.\n\nIf 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.\n\n****\n\n# moving-averages\n\nThe complete collection of [FinTech](https://en.wikipedia.org/wiki/Financial_technology) utility methods for [Moving average](https://en.wikipedia.org/wiki/Moving_average), including:\n\n- [simple moving average (MA)](#simple-moving-average-madata-size)\n- [dynamic weighted moving average (DMA)](#dynamic-weighted-moving-average-dmadata-alpha-nohead)\n- [exponential moving average (EMA)](#exponential-moving-average-emadata-size)\n- [smoothed moving average (SMA)](#smoothed-moving-average-smadata-size-times)\n- [weighted moving average (WMA)](#weighted-moving-average-wmadata-size)\n\nAnd `moving-averages` will also handle empty values.\n\n## install\n\n```sh\n$ npm i moving-averages\n```\n\n## usage\n\n```js\nimport {\n  ma, dma, ema, sma, wma\n} from 'moving-averages'\n\nma([1, 2, 3, 4, 5], 2)\n// [<1 empty item>, 1.5, 2.5, 3.5, 4.5]\n```\n\n## Simple Moving Average: `ma(data, size)`\n\n- **data** `Array.<Number|undefined>` the collection of data inside which empty values are allowed. Empty values are useful if a stock is suspended.\n- **size** `Number` the size of the periods.\n\nReturns `Array.<Number|undefined>`\n\n#### Special Cases\n\n```js\n// If the size is less than `1`\nma([1, 2, 3], 0.5)       // [1, 2, 3]\n\n// If the size is larger than data length\nma([1, 2, 3], 5)         // [<3 empty items>]\n\nma([, 1,, 3, 4, 5], 2)\n// [<2 empty items>, 0.5, 1.5, 3.5, 4.5]\n```\n\nAnd all of the other moving average methods have similar mechanism.\n\n## Dynamic Weighted Moving Average: `dma(data, alpha, noHead)`\n\n- **data**\n- **alpha** `Number|Array.<Number>` the coefficient or list of coefficients `alpha` represents the degree of weighting decrease for each datum.\n  - If `alpha` is a number, then the weighting decrease for each datum is the same.\n  - If `alpha` larger than `1` is invalid, then the return value will be an empty array of the same length of the original data.\n  - If `alpha` is an array, then it could provide different decreasing degree for each datum.\n- **noHead** `Boolean=` whether we should abandon the first DMA.\n\nReturns `Array.<Number|undefined>`\n\n```js\ndma([1, 2, 3], 2)    // [<3 empty items>]\n\ndma([1, 2, 3], 0.5)  // [1, 1.5, 2.25]\n\ndma([1, 2, 3, 4, 5], [0.1, 0.2, 0.1])\n// [1, 1.2, 1.38]\n```\n\n## Exponential Moving Average: `ema(data, size)`\n\nCalulates the most frequent used exponential average which covers about 86% of the total weight (when `alpha = 2 / (N + 1)`).\n\n- **data**\n- **size** `Number` the size of the periods.\n\nReturns `Array.<Number|undefined>`\n\n## Smoothed Moving Average: `sma(data, size, times)`\n\nAlso known as the modified moving average or running moving average, with `alpha = times / size`.\n\n- **data**\n- **size**\n- **times** `Number=1`\n\nReturns `Array.<Number|undefined>`\n\n## Weighted Moving Average: `wma(data, size)`\n\nCalculates convolution of the datum points with a fixed weighting function.\n\nReturns `Array.<Number|undefined>`\n\n## Related FinTech Modules\n\n- [bollinger-bands](https://www.npmjs.com/package/bollinger-bands): Fintach math utility to calculate bollinger bands.\n- [s-deviation](https://www.npmjs.com/package/s-deviation): Math utility to calculate standard deviations.\n- [moving-averages](https://www.npmjs.com/package/moving-averages): The complete collection of utility methods for [Moving average](https://en.wikipedia.org/wiki/Moving_average).\n\nMIT\n","maintainers":[{"name":"kael","email":"i@kael.me"}],"time":{"modified":"2022-06-20T10:33:41.465Z","created":"2015-12-12T04:05:19.937Z","1.0.0":"2015-12-12T04:05:19.937Z","0.0.1":"2016-09-26T10:55:12.924Z","0.0.2":"2016-09-26T11:04:48.431Z","0.0.3":"2016-09-26T11:05:36.969Z","0.0.4":"2016-09-26T11:20:24.756Z","0.1.0":"2016-10-06T01:01:34.327Z","1.0.1":"2016-10-19T12:34:37.331Z","1.0.2":"2016-10-19T12:47:48.479Z","1.0.3":"2016-10-19T12:48:13.170Z","1.0.4":"2016-10-26T08:37:04.130Z","2.0.0":"2017-08-19T12:57:22.795Z","2.0.1":"2017-08-19T13:25:14.718Z","2.0.2":"2017-08-19T13:26:39.651Z","3.0.0":"2017-08-22T12:05:48.553Z","3.0.1":"2017-08-22T12:08:00.359Z","3.0.2":"2017-08-23T07:48:43.003Z","3.0.3":"2017-08-23T07:55:50.302Z","3.0.4":"2017-08-24T09:24:01.126Z","3.0.5":"2017-09-08T07:38:28.335Z","3.0.6":"2017-09-10T11:04:51.574Z","4.0.0":"2017-09-12T10:23:53.641Z","4.0.1":"2017-09-12T10:42:45.764Z","4.0.2":"2017-09-12T10:45:54.369Z","4.0.3":"2017-09-12T13:08:00.850Z","4.0.4":"2017-09-12T13:34:58.586Z","4.0.5":"2017-09-13T08:01:23.315Z","4.0.6":"2021-03-02T03:09:34.021Z"},"homepage":"https://github.com/kaelzhang/moving-averages#readme","keywords":["moving average","moving-average","moving-averages","simple moving average","exponential moving average","weighted moving average","smoothed moving average","dynamic weighted moving average","sma","ema","wma","sma","dma"],"repository":{"type":"git","url":"git://github.com/kaelzhang/moving-averages.git"},"author":{"name":"kaelzhang"},"bugs":{"url":"https://github.com/kaelzhang/moving-averages/issues"},"license":"MIT","readmeFilename":"README.md","users":{"khanh-fukori":true,"jacob-beltran":true}}