indicator: Kalman & GH filters: compare estimation accurateness

Author: COLDTURNIP

pkg/indicator/ghfilter_test.go

@@ -6067,7 +6067,7 @@ func Test_GHFilter(t *testing.T) {
 	}
 }
 
-func Test_GHFilterAccurate(t *testing.T) {
+func Test_GHFilterEstimationAccurate(t *testing.T) {
 	type args struct {
 		allKLines []types.KLine
 		priceF    KLineValueMapper
@@ -6122,22 +6122,27 @@ func Test_GHFilterAccurate(t *testing.T) {
 
 			var filterDiff2Sum, ewmaDiff2Sum float64
 			var filterCloseDiff2Sum, ewmaCloseDiff2Sum float64
-			for _, k := range klines {
+			for i, k := range klines {
+				// square error between last estimated state and current actual state
+				if i > 0 {
+					filterDiff2Sum += klineSquareError(filter.Last(), k)
+					ewmaDiff2Sum += klineSquareError(ewma.Last(), k)
+					filterCloseDiff2Sum += closeSquareError(filter.Last(), k)
+					ewmaCloseDiff2Sum += closeSquareError(ewma.Last(), k)
+				}
+
+				// update estimations
 				filter.PushK(k)
 				ewma.PushK(k)
-
-				filterDiff2Sum += klineSquareError(filter.Last(), k)
-				ewmaDiff2Sum += klineSquareError(ewma.Last(), k)
-				filterCloseDiff2Sum += closeSquareError(filter.Last(), k)
-				ewmaCloseDiff2Sum += closeSquareError(ewma.Last(), k)
 			}
-			filterSquareErr := math.Sqrt(filterDiff2Sum / float64(len(klines)*4))
-			ewmaSquareErr := math.Sqrt(ewmaDiff2Sum / float64(len(klines)*4))
+			numEstimations := len(klines) - 1
+			filterSquareErr := math.Sqrt(filterDiff2Sum / float64(numEstimations*4))
+			ewmaSquareErr := math.Sqrt(ewmaDiff2Sum / float64(numEstimations*4))
 			if filterSquareErr > ewmaSquareErr {
 				t.Errorf("filter K-Line square error %f > EWMA K-Line square error %v", filterSquareErr, ewmaSquareErr)
 			}
-			filterCloseSquareErr := math.Sqrt(filterCloseDiff2Sum / float64(len(klines)))
-			ewmaCloseSquareErr := math.Sqrt(ewmaCloseDiff2Sum / float64(len(klines)))
+			filterCloseSquareErr := math.Sqrt(filterCloseDiff2Sum / float64(numEstimations))
+			ewmaCloseSquareErr := math.Sqrt(ewmaCloseDiff2Sum / float64(numEstimations))
 			if filterCloseSquareErr > ewmaCloseSquareErr {
 				t.Errorf("filter close price square error %f > EWMA close price square error %v", filterCloseSquareErr, ewmaCloseSquareErr)
 			}

pkg/indicator/kalmanfilter_test.go

@@ -6067,7 +6067,7 @@ func Test_KalmanFilter(t *testing.T) {
 	}
 }
 
-func Test_KalmanFilterAccurate(t *testing.T) {
+func Test_KalmanFilterEstimationAccurate(t *testing.T) {
 	type args struct {
 		allKLines []types.KLine
 		window    int
@@ -6121,22 +6121,27 @@ func Test_KalmanFilterAccurate(t *testing.T) {
 
 			var filterDiff2Sum, ewmaDiff2Sum float64
 			var filterCloseDiff2Sum, ewmaCloseDiff2Sum float64
-			for _, k := range klines {
+			for i, k := range klines {
+				// square error between last estimated state and current actual state
+				if i > 0 {
+					filterDiff2Sum += klineSquareError(filter.Last(), k)
+					ewmaDiff2Sum += klineSquareError(ewma.Last(), k)
+					filterCloseDiff2Sum += closeSquareError(filter.Last(), k)
+					ewmaCloseDiff2Sum += closeSquareError(ewma.Last(), k)
+				}
+
+				// update estimations
 				filter.PushK(k)
 				ewma.PushK(k)
-
-				filterDiff2Sum += klineSquareError(filter.Last(), k)
-				ewmaDiff2Sum += klineSquareError(ewma.Last(), k)
-				filterCloseDiff2Sum += closeSquareError(filter.Last(), k)
-				ewmaCloseDiff2Sum += closeSquareError(ewma.Last(), k)
 			}
-			filterSquareErr := math.Sqrt(filterDiff2Sum / float64(len(klines)*4))
-			ewmaSquareErr := math.Sqrt(ewmaDiff2Sum / float64(len(klines)*4))
+			numEstimations := len(klines) - 1
+			filterSquareErr := math.Sqrt(filterDiff2Sum / float64(numEstimations*4))
+			ewmaSquareErr := math.Sqrt(ewmaDiff2Sum / float64(numEstimations*4))
 			if filterSquareErr > ewmaSquareErr {
 				t.Errorf("filter K-Line square error %f > EWMA K-Line square error %v", filterSquareErr, ewmaSquareErr)
 			}
-			filterCloseSquareErr := math.Sqrt(filterCloseDiff2Sum / float64(len(klines)))
-			ewmaCloseSquareErr := math.Sqrt(ewmaCloseDiff2Sum / float64(len(klines)))
+			filterCloseSquareErr := math.Sqrt(filterCloseDiff2Sum / float64(numEstimations))
+			ewmaCloseSquareErr := math.Sqrt(ewmaCloseDiff2Sum / float64(numEstimations))
 			if filterCloseSquareErr > ewmaCloseSquareErr {
 				t.Errorf("filter close price square error %f > EWMA close price square error %v", filterCloseSquareErr, ewmaCloseSquareErr)
 			}