feature: add G-H filter and Kalman filter

- implement G-H (alpha beta) filter and Kalman filter
- compare the predict accurateness with other indicator
This commit is contained in:
Raphanus Lo 2022-08-15 00:07:26 +08:00
parent 843b81e132
commit 9c684c124c
6 changed files with 12576 additions and 0 deletions

96
pkg/indicator/ghfilter.go Normal file
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package indicator
import (
"github.com/c9s/bbgo/pkg/datatype/floats"
"github.com/c9s/bbgo/pkg/types"
"math"
)
// Refer: https://jamesgoulding.com/Research_II/Ehlers/Ehlers%20(Optimal%20Tracking%20Filters).doc
// Ehler's Optimal Tracking Filter, an alpha-beta filter, also called g-h filter
//go:generate callbackgen -type GHFilter
type GHFilter struct {
types.SeriesBase
types.IntervalWindow
a float64 // maneuverability uncertainty
b float64 // measurement uncertainty
lastMeasurement float64
Values floats.Slice
UpdateCallbacks []func(value float64)
}
func (inc *GHFilter) Update(value float64) {
inc.update(value, math.Abs(value-inc.lastMeasurement))
}
func (inc *GHFilter) update(value, uncertainty float64) {
if len(inc.Values) == 0 {
inc.a = 0
inc.b = uncertainty / 2
inc.lastMeasurement = value
inc.Values.Push(value)
return
}
multiplier := 2.0 / float64(1+inc.Window) // EMA multiplier
inc.a = multiplier*(value-inc.lastMeasurement) + (1-multiplier)*inc.a
inc.b = multiplier*uncertainty/2 + (1-multiplier)*inc.b
lambda := inc.a / inc.b
lambda2 := lambda * lambda
alpha := (-lambda2 + math.Sqrt(lambda2*lambda2+16*lambda2)) / 8
filtered := alpha*value + (1-alpha)*inc.Values.Last()
inc.Values.Push(filtered)
inc.lastMeasurement = value
}
func (inc *GHFilter) Index(i int) float64 {
if inc.Values == nil {
return 0.0
}
return inc.Values.Index(i)
}
func (inc *GHFilter) Length() int {
if inc.Values == nil {
return 0
}
return inc.Values.Length()
}
func (inc *GHFilter) Last() float64 {
if inc.Values == nil {
return 0.0
}
return inc.Values.Last()
}
var _ types.SeriesExtend = &GHFilter{}
func (inc *GHFilter) PushK(k types.KLine) {
inc.update(k.Close.Float64(), k.High.Float64()-k.Low.Float64())
}
func (inc *GHFilter) CalculateAndUpdate(allKLines []types.KLine) {
if inc.Values != nil {
k := allKLines[len(allKLines)-1]
inc.PushK(k)
inc.EmitUpdate(inc.Last())
return
}
for _, k := range allKLines {
inc.PushK(k)
inc.EmitUpdate(inc.Last())
}
}
func (inc *GHFilter) handleKLineWindowUpdate(interval types.Interval, window types.KLineWindow) {
if inc.Interval != interval {
return
}
inc.CalculateAndUpdate(window)
}
func (inc *GHFilter) Bind(updater KLineWindowUpdater) {
updater.OnKLineWindowUpdate(inc.handleKLineWindowUpdate)
}

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// Code generated by "callbackgen -type GHFilter"; DO NOT EDIT.
package indicator
import ()
func (inc *GHFilter) OnUpdate(cb func(value float64)) {
inc.UpdateCallbacks = append(inc.UpdateCallbacks, cb)
}
func (inc *GHFilter) EmitUpdate(value float64) {
for _, cb := range inc.UpdateCallbacks {
cb(value)
}
}

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package indicator
import (
"github.com/c9s/bbgo/pkg/datatype/floats"
"github.com/c9s/bbgo/pkg/types"
"math"
)
// Refer: https://www.kalmanfilter.net/kalman1d.html
// One-dimensional Kalman filter
//go:generate callbackgen -type KalmanFilter
type KalmanFilter struct {
types.SeriesBase
types.IntervalWindow
AdditionalSmoothWindow uint
amp2 *types.Queue // measurement uncertainty
k float64 // Kalman gain
measurements *types.Queue
Values floats.Slice
UpdateCallbacks []func(value float64)
}
func (inc *KalmanFilter) Update(value float64) {
var measureMove = value
if inc.measurements != nil {
measureMove = value - inc.measurements.Last()
}
inc.update(value, math.Abs(measureMove))
}
func (inc *KalmanFilter) update(value, amp float64) {
if len(inc.Values) == 0 {
inc.amp2 = types.NewQueue(inc.Window)
inc.amp2.Update(amp * amp)
inc.measurements = types.NewQueue(inc.Window)
inc.measurements.Update(value)
inc.Values.Push(value)
return
}
// measurement
inc.measurements.Update(value)
inc.amp2.Update(amp * amp)
q := math.Sqrt(types.Mean(inc.amp2)) * float64(1+inc.AdditionalSmoothWindow)
// update
lastPredict := inc.Values.Last()
curState := value + (value - lastPredict)
estimated := lastPredict + inc.k*(curState-lastPredict)
// predict
inc.Values.Push(estimated)
p := math.Abs(curState - estimated)
inc.k = p / (p + q)
}
func (inc *KalmanFilter) Index(i int) float64 {
if inc.Values == nil {
return 0.0
}
return inc.Values.Index(i)
}
func (inc *KalmanFilter) Length() int {
if inc.Values == nil {
return 0
}
return inc.Values.Length()
}
func (inc *KalmanFilter) Last() float64 {
if inc.Values == nil {
return 0.0
}
return inc.Values.Last()
}
var _ types.SeriesExtend = &KalmanFilter{}
func (inc *KalmanFilter) PushK(k types.KLine) {
inc.update(k.Close.Float64(), (k.High.Float64()-k.Low.Float64())/2)
}
func (inc *KalmanFilter) CalculateAndUpdate(allKLines []types.KLine) {
if inc.Values != nil {
k := allKLines[len(allKLines)-1]
inc.PushK(k)
inc.EmitUpdate(inc.Last())
return
}
for _, k := range allKLines {
inc.PushK(k)
inc.EmitUpdate(inc.Last())
}
}
func (inc *KalmanFilter) handleKLineWindowUpdate(interval types.Interval, window types.KLineWindow) {
if inc.Interval != interval {
return
}
inc.CalculateAndUpdate(window)
}
func (inc *KalmanFilter) Bind(updater KLineWindowUpdater) {
updater.OnKLineWindowUpdate(inc.handleKLineWindowUpdate)
}

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// Code generated by "callbackgen -type KalmanFilter"; DO NOT EDIT.
package indicator
import ()
func (inc *KalmanFilter) OnUpdate(cb func(value float64)) {
inc.UpdateCallbacks = append(inc.UpdateCallbacks, cb)
}
func (inc *KalmanFilter) EmitUpdate(value float64) {
for _, cb := range inc.UpdateCallbacks {
cb(value)
}
}

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