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https://github.com/c9s/bbgo.git
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feature: add G-H filter and Kalman filter
- implement G-H (alpha beta) filter and Kalman filter - compare the predict accurateness with other indicator
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96
pkg/indicator/ghfilter.go
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96
pkg/indicator/ghfilter.go
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package indicator
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import (
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"github.com/c9s/bbgo/pkg/datatype/floats"
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"github.com/c9s/bbgo/pkg/types"
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"math"
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)
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// Refer: https://jamesgoulding.com/Research_II/Ehlers/Ehlers%20(Optimal%20Tracking%20Filters).doc
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// Ehler's Optimal Tracking Filter, an alpha-beta filter, also called g-h filter
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//go:generate callbackgen -type GHFilter
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type GHFilter struct {
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types.SeriesBase
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types.IntervalWindow
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a float64 // maneuverability uncertainty
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b float64 // measurement uncertainty
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lastMeasurement float64
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Values floats.Slice
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UpdateCallbacks []func(value float64)
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}
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func (inc *GHFilter) Update(value float64) {
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inc.update(value, math.Abs(value-inc.lastMeasurement))
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}
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func (inc *GHFilter) update(value, uncertainty float64) {
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if len(inc.Values) == 0 {
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inc.a = 0
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inc.b = uncertainty / 2
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inc.lastMeasurement = value
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inc.Values.Push(value)
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return
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}
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multiplier := 2.0 / float64(1+inc.Window) // EMA multiplier
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inc.a = multiplier*(value-inc.lastMeasurement) + (1-multiplier)*inc.a
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inc.b = multiplier*uncertainty/2 + (1-multiplier)*inc.b
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lambda := inc.a / inc.b
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lambda2 := lambda * lambda
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alpha := (-lambda2 + math.Sqrt(lambda2*lambda2+16*lambda2)) / 8
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filtered := alpha*value + (1-alpha)*inc.Values.Last()
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inc.Values.Push(filtered)
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inc.lastMeasurement = value
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}
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func (inc *GHFilter) Index(i int) float64 {
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if inc.Values == nil {
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return 0.0
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}
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return inc.Values.Index(i)
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}
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func (inc *GHFilter) Length() int {
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if inc.Values == nil {
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return 0
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}
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return inc.Values.Length()
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}
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func (inc *GHFilter) Last() float64 {
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if inc.Values == nil {
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return 0.0
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}
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return inc.Values.Last()
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}
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var _ types.SeriesExtend = &GHFilter{}
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func (inc *GHFilter) PushK(k types.KLine) {
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inc.update(k.Close.Float64(), k.High.Float64()-k.Low.Float64())
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}
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func (inc *GHFilter) CalculateAndUpdate(allKLines []types.KLine) {
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if inc.Values != nil {
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k := allKLines[len(allKLines)-1]
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inc.PushK(k)
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inc.EmitUpdate(inc.Last())
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return
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}
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for _, k := range allKLines {
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inc.PushK(k)
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inc.EmitUpdate(inc.Last())
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}
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}
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func (inc *GHFilter) handleKLineWindowUpdate(interval types.Interval, window types.KLineWindow) {
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if inc.Interval != interval {
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return
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}
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inc.CalculateAndUpdate(window)
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}
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func (inc *GHFilter) Bind(updater KLineWindowUpdater) {
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updater.OnKLineWindowUpdate(inc.handleKLineWindowUpdate)
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}
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15
pkg/indicator/ghfilter_callbacks.go
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15
pkg/indicator/ghfilter_callbacks.go
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// Code generated by "callbackgen -type GHFilter"; DO NOT EDIT.
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package indicator
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import ()
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func (inc *GHFilter) OnUpdate(cb func(value float64)) {
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inc.UpdateCallbacks = append(inc.UpdateCallbacks, cb)
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}
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func (inc *GHFilter) EmitUpdate(value float64) {
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for _, cb := range inc.UpdateCallbacks {
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cb(value)
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}
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}
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6151
pkg/indicator/ghfilter_test.go
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6151
pkg/indicator/ghfilter_test.go
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File diff suppressed because it is too large
Load Diff
108
pkg/indicator/kalmanfilter.go
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108
pkg/indicator/kalmanfilter.go
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package indicator
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import (
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"github.com/c9s/bbgo/pkg/datatype/floats"
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"github.com/c9s/bbgo/pkg/types"
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"math"
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)
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// Refer: https://www.kalmanfilter.net/kalman1d.html
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// One-dimensional Kalman filter
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//go:generate callbackgen -type KalmanFilter
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type KalmanFilter struct {
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types.SeriesBase
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types.IntervalWindow
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AdditionalSmoothWindow uint
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amp2 *types.Queue // measurement uncertainty
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k float64 // Kalman gain
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measurements *types.Queue
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Values floats.Slice
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UpdateCallbacks []func(value float64)
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}
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func (inc *KalmanFilter) Update(value float64) {
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var measureMove = value
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if inc.measurements != nil {
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measureMove = value - inc.measurements.Last()
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}
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inc.update(value, math.Abs(measureMove))
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}
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func (inc *KalmanFilter) update(value, amp float64) {
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if len(inc.Values) == 0 {
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inc.amp2 = types.NewQueue(inc.Window)
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inc.amp2.Update(amp * amp)
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inc.measurements = types.NewQueue(inc.Window)
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inc.measurements.Update(value)
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inc.Values.Push(value)
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return
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}
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// measurement
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inc.measurements.Update(value)
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inc.amp2.Update(amp * amp)
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q := math.Sqrt(types.Mean(inc.amp2)) * float64(1+inc.AdditionalSmoothWindow)
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// update
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lastPredict := inc.Values.Last()
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curState := value + (value - lastPredict)
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estimated := lastPredict + inc.k*(curState-lastPredict)
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// predict
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inc.Values.Push(estimated)
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p := math.Abs(curState - estimated)
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inc.k = p / (p + q)
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}
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func (inc *KalmanFilter) Index(i int) float64 {
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if inc.Values == nil {
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return 0.0
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}
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return inc.Values.Index(i)
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}
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func (inc *KalmanFilter) Length() int {
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if inc.Values == nil {
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return 0
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}
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return inc.Values.Length()
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}
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func (inc *KalmanFilter) Last() float64 {
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if inc.Values == nil {
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return 0.0
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}
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return inc.Values.Last()
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}
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var _ types.SeriesExtend = &KalmanFilter{}
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func (inc *KalmanFilter) PushK(k types.KLine) {
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inc.update(k.Close.Float64(), (k.High.Float64()-k.Low.Float64())/2)
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}
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func (inc *KalmanFilter) CalculateAndUpdate(allKLines []types.KLine) {
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if inc.Values != nil {
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k := allKLines[len(allKLines)-1]
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inc.PushK(k)
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inc.EmitUpdate(inc.Last())
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return
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}
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for _, k := range allKLines {
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inc.PushK(k)
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inc.EmitUpdate(inc.Last())
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}
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}
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func (inc *KalmanFilter) handleKLineWindowUpdate(interval types.Interval, window types.KLineWindow) {
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if inc.Interval != interval {
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return
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}
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inc.CalculateAndUpdate(window)
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}
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func (inc *KalmanFilter) Bind(updater KLineWindowUpdater) {
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updater.OnKLineWindowUpdate(inc.handleKLineWindowUpdate)
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}
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15
pkg/indicator/kalmanfilter_callbacks.go
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15
pkg/indicator/kalmanfilter_callbacks.go
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// Code generated by "callbackgen -type KalmanFilter"; DO NOT EDIT.
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package indicator
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import ()
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func (inc *KalmanFilter) OnUpdate(cb func(value float64)) {
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inc.UpdateCallbacks = append(inc.UpdateCallbacks, cb)
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}
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func (inc *KalmanFilter) EmitUpdate(value float64) {
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for _, cb := range inc.UpdateCallbacks {
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cb(value)
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}
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}
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6191
pkg/indicator/kalmanfilter_test.go
Normal file
6191
pkg/indicator/kalmanfilter_test.go
Normal file
File diff suppressed because it is too large
Load Diff
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