import isNumber from 'lodash-es/isNumber.js'
import size from 'lodash-es/size.js'
import each from 'lodash-es/each.js'
/**
* 計算各週期之ATS(Average Trade Size，平均單筆成交額)
*
* ats(i)=NumberOfTrades(i)>0 ? QuoteAssetVolume(i)/NumberOfTrades(i) : 0
* atsRatio為當根ats相對最近len根(含當根)簡單平均之倍數，baseline為0時回1；atsChange為當根ats相對前一根之變化率，前一根ats為0時回0
* 第一筆對應輸入索引len-1，資料筆數n小於len+1時該期vs回空陣列
*
* Unit Test: {@link https://github.com/yuda-lyu/w-data-trade/blob/master/test/unit-calcAts.test.mjs Github}
* @function
* @param {Array} arr 輸入K線陣列，各元素需含time、QuoteAssetVolume、NumberOfTrades欄位
* @param {String} key 輸入計算所用數值欄位名稱字串，本指標固定讀取QuoteAssetVolume/NumberOfTrades欄位，key目前未被使用，僅為與其他指標介面一致而保留
* @param {Object} [opt={}] 輸入設定物件，預設{}，本指標目前無可用設定鍵
* @returns {Promise} 回傳Promise，resolve為各期結果陣列，各元素為{period,len,vs}，vs內各元素為{time,atsRatio,atsChange}
* @example
*
* let arr = [
* { time: '2020-01-01T00:00:00', QuoteAssetVolume: 33441599, NumberOfTrades: 46046 },
* { time: '2020-01-01T04:00:00', QuoteAssetVolume: 15032938, NumberOfTrades: 29738 },
* { time: '2020-01-01T08:00:00', QuoteAssetVolume: 20445895, NumberOfTrades: 32476 },
* { time: '2020-01-01T12:00:00', QuoteAssetVolume: 14890182, NumberOfTrades: 29991 },
* ]
*
* calcAts(arr, 'Close')
* .then((rs) => {
* console.log(rs[0])
* // => {
* // period: '12hr',
* // len: 3,
* // vs: [
* // { time: '2020-01-01T08:00:00', atsRatio: 1.0146995613283087, atsChange: 0.2454075213472537 },
* // { time: '2020-01-01T12:00:00', atsRatio: 0.9129026732554982, atsChange: -0.21138421472562993 }
* // ]
* // }
* })
*
*/
let calcAts = (() => {
//ATS (Average Trade Size) = QuoteAssetVolume / NumberOfTrades = 平均單筆成交額 (USDT)
//反映「大戶介入 vs 散戶 FOMO」之單筆規模維度, 與既有 volumn 系列完全互補
//
//len = baseline SMA 視窗期數 (n × 4hr)
//atsRatio = ats[i] / SMA(ats, len) (相對 N 期均值倍數, 跨時期可比, 對應 d12 vol_volumeSpike 的 atsSpike)
//atsChange = (ats[i] - ats[i-1]) / ats[i-1] (相對前一根%變化, 抓突發大單事件)
let kp = {
'12hr': 3,
'16hr': 4,
'20hr': 5,
'1day': 6,
'2day': 12,
'4day': 24,
'7day': 42,
'15day': 90,
'30day': 180,
}
let caAts = (arr, len, opt = {}) => {
//check
if (!isNumber(len)) {
throw new Error(`len is not a number`)
}
//n
let n = size(arr)
//check, 需 len 根供 SMA baseline + 至少 1 根算 atsChange
if (n < len + 1) {
return []
}
let kTime = 'time'
let kQuoteVol = 'QuoteAssetVolume'
let kNumTrades = 'NumberOfTrades'
//計算各根 raw ATS
//nt=0 時 ats=0 (d12 行為; ETH 4hr 不會踩到, 防呆而已)
let atss = new Array(n).fill(0)
for (let i = 0; i < n; i++) {
let qv = arr[i][kQuoteVol]
let nt = arr[i][kNumTrades]
//check
if (!isNumber(qv)) {
throw new Error(`invalid qv[${qv}]`)
}
if (!isNumber(nt)) {
throw new Error(`invalid nt[${nt}]`)
}
if (nt > 0) {
atss[i] = qv / nt
}
//else: atss[i] = 0 (預設值)
}
//rolling SMA baseline (視窗 [i-len+1, i] 之 ATS 均值)
//running sum 維護, 第一個 baseline 對應 index = len-1
let sumAts = 0
for (let i = 0; i < len; i++) {
sumAts += atss[i]
}
//rs
let rs = []
//第一筆 atsRatio 對應 index = len-1, atsChange 對應 index = len-1 (需 len-2 之前根存在)
{
let i = len - 1
let ats = atss[i]
let baseline = sumAts / len
let atsRatio = baseline !== 0 ? ats / baseline : 1
let atsPrev = atss[i - 1]
let atsChange = atsPrev !== 0 ? (ats - atsPrev) / atsPrev : 0
rs.push({
time: arr[i][kTime],
atsRatio,
atsChange,
})
}
//後續 i >= len
for (let i = len; i < n; i++) {
//視窗滑動: 排掉 i-len, 加上 i
sumAts = sumAts - atss[i - len] + atss[i]
let ats = atss[i]
let baseline = sumAts / len
let atsRatio = baseline !== 0 ? ats / baseline : 1
let atsPrev = atss[i - 1]
let atsChange = atsPrev !== 0 ? (ats - atsPrev) / atsPrev : 0
rs.push({
time: arr[i][kTime],
atsRatio,
atsChange,
})
}
// console.log('rs', rs)
return rs
}
let caAtss = (arr, opt = {}) => {
//rrs
let rrs = []
each(kp, (len, period) => {
//caAts
let rs = caAts(arr, len, opt)
//push
rrs.push({
period,
len,
vs: rs,
})
// console.log('rrs', rrs)
})
return rrs
}
let calcAts = async(arr, key, opt = {}) => {
// arr = [
// {"time":"2020-01-01T00:00:00",...,"QuoteAssetVolume":33441599.81960844,"NumberOfTrades":46046,...},
// ...
// ]
//caAtss
let rs = caAtss(arr, opt)
return rs
}
return calcAts
})()
export default calcAts