93 lines
3.0 KiB
Go
93 lines
3.0 KiB
Go
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package indicator
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import (
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"math"
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"git.qtrade.icu/lychiyu/qbtrade/pkg/datatype/floats"
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"git.qtrade.icu/lychiyu/qbtrade/pkg/types"
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)
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// Refer: Arnaud Legoux Moving Average
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// Refer: https://capital.com/arnaud-legoux-moving-average
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// Also check https://github.com/DaveSkender/Stock.Indicators/blob/main/src/a-d/Alma/Alma.cs
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//
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// The Arnaud Legoux Moving Average (ALMA) is a technical analysis indicator that is used to smooth price data and reduce the lag associated
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// with traditional moving averages. It was developed by Arnaud Legoux and is based on the weighted moving average, with the weighting factors
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// determined using a Gaussian function. The ALMA is calculated by taking the weighted moving average of the input data using weighting factors
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// that are based on the standard deviation of the data and the specified length of the moving average. This resulting average is then plotted
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// on the price chart as a line, which can be used to make predictions about future price movements. The ALMA is typically more responsive to
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// changes in the underlying data than a simple moving average, but may be less reliable in trending markets.
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//
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// @param offset: Gaussian applied to the combo line. 1->ema, 0->sma
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// @param sigma: the standard deviation applied to the combo line. This makes the combo line sharper
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//
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//go:generate callbackgen -type ALMA
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type ALMA struct {
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types.SeriesBase
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types.IntervalWindow // required
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Offset float64 // required: recommend to be 0.5
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Sigma int // required: recommend to be 5
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weight []float64
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sum float64
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input []float64
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Values floats.Slice
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UpdateCallbacks []func(value float64)
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}
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const MaxNumOfALMA = 5_000
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const MaxNumOfALMATruncateSize = 100
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func (inc *ALMA) Update(value float64) {
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if inc.weight == nil {
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inc.SeriesBase.Series = inc
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inc.weight = make([]float64, inc.Window)
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m := inc.Offset * (float64(inc.Window) - 1.)
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s := float64(inc.Window) / float64(inc.Sigma)
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inc.sum = 0.
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for i := 0; i < inc.Window; i++ {
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diff := float64(i) - m
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wt := math.Exp(-diff * diff / 2. / s / s)
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inc.sum += wt
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inc.weight[i] = wt
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}
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}
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inc.input = append(inc.input, value)
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if len(inc.input) >= inc.Window {
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weightedSum := 0.0
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inc.input = inc.input[len(inc.input)-inc.Window:]
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for i := 0; i < inc.Window; i++ {
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weightedSum += inc.weight[inc.Window-i-1] * inc.input[i]
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}
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inc.Values.Push(weightedSum / inc.sum)
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if len(inc.Values) > MaxNumOfALMA {
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inc.Values = inc.Values[MaxNumOfALMATruncateSize-1:]
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}
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}
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}
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func (inc *ALMA) Last(i int) float64 {
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return inc.Values.Last(i)
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}
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func (inc *ALMA) Index(i int) float64 {
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return inc.Last(i)
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}
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func (inc *ALMA) Length() int {
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return len(inc.Values)
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}
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var _ types.SeriesExtend = &ALMA{}
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func (inc *ALMA) CalculateAndUpdate(allKLines []types.KLine) {
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if inc.input == nil {
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for _, k := range allKLines {
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inc.Update(k.Close.Float64())
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inc.EmitUpdate(inc.Last(0))
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}
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return
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}
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inc.Update(allKLines[len(allKLines)-1].Close.Float64())
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inc.EmitUpdate(inc.Last(0))
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}
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