feature: alma indicator add test

pkg/indicator/alma.go

@@ -13,14 +13,14 @@ import (
 // @param sigma: the standard deviation applied to the combo line. This makes the combo line sharper
 //go:generate callbackgen -type ALMA
 type ALMA struct {
-	types.IntervalWindow
-	Offset float64
-	Sigma int
-	Weight []float64
-	Sum float64
-	input []float64
-	Values types.Float64Slice
-	UpdateCallbacks []func(value float64)
+	types.IntervalWindow         // required
+	Offset               float64 // required: recommend to be 5
+	Sigma                int     // required: recommend to be 0.5
+	Weight               []float64
+	Sum                  float64
+	input                []float64
+	Values               types.Float64Slice
+	UpdateCallbacks      []func(value float64)
 }
 
 const MaxNumOfALMA = 5_000
@@ -34,17 +34,17 @@ func (inc *ALMA) Update(value float64) {
 		inc.Sum = 0.
 		for i := 0; i < inc.Window; i++ {
 			diff := float64(i) - m
-			wt := math.Exp(-diff*diff/2./s/s)
+			wt := math.Exp(-diff * diff / 2. / s / s)
 			inc.Sum += wt
 			inc.Weight[i] = wt
 		}
 	}
 	inc.input = append(inc.input, value)
 	if len(inc.input) >= inc.Window {
 		weightedSum := 0.0
-		inc.input = inc.input[len(inc.input) - inc.Window:]
+		inc.input = inc.input[len(inc.input)-inc.Window:]
 		for i := 0; i < inc.Window; i++ {
-			weightedSum += inc.Weight[i] * inc.input[i]
+			weightedSum += inc.Weight[inc.Window-i-1] * inc.input[i]
 		}
 		inc.Values.Push(weightedSum / inc.Sum)
 		if len(inc.Values) > MaxNumOfALMA {
@@ -54,7 +54,7 @@ func (inc *ALMA) Update(value float64) {
 }
 
 func (inc *ALMA) Last() float64 {
-	if len(inc.Values)  == 0 {
+	if len(inc.Values) == 0 {
 		return 0
 	}
 	return inc.Values[len(inc.Values)-1]

pkg/indicator/alma_test.go

@@ -0,0 +1,61 @@
+package indicator
+
+import (
+	"encoding/json"
+	"testing"
+
+	"github.com/c9s/bbgo/pkg/fixedpoint"
+	"github.com/c9s/bbgo/pkg/types"
+	"github.com/stretchr/testify/assert"
+)
+
+/*
+python:
+
+import pandas as pd
+import pandas_ta as ta
+
+data = pd.Series([0,1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9])
+sigma = 6
+offset = 0.9
+size = 5
+
+result = ta.alma(data, size, sigma, offset)
+print(result)
+*/
+func Test_ALMA(t *testing.T) {
+	var Delta = 0.01
+	var randomPrices = []byte(`[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9]`)
+	var input []fixedpoint.Value
+	if err := json.Unmarshal(randomPrices, &input); err != nil {
+		panic(err)
+	}
+	tests := []struct {
+		name   string
+		kLines []types.KLine
+		want   float64
+		next   float64
+		all    int
+	}{
+		{
+			name:   "random_case",
+			kLines: buildKLines(input),
+			want:   5.60785,
+			next:   4.60785,
+			all:    26,
+		},
+	}
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			alma := ALMA{
+				IntervalWindow: types.IntervalWindow{Window: 5},
+				Offset:         0.9,
+				Sigma:          6,
+			}
+			alma.calculateAndUpdate(tt.kLines)
+			assert.InDelta(t, tt.want, alma.Last(), Delta)
+			assert.InDelta(t, tt.next, alma.Index(1), Delta)
+			assert.Equal(t, tt.all, alma.Length())
+		})
+	}
+}