diff --git a/pkg/strategy/harmonic/strategy.go b/pkg/strategy/harmonic/strategy.go
index d379f3262..4d98fda34 100644
--- a/pkg/strategy/harmonic/strategy.go
+++ b/pkg/strategy/harmonic/strategy.go
@@ -13,7 +13,6 @@ import (
 	"github.com/c9s/bbgo/pkg/indicator"
 	"github.com/c9s/bbgo/pkg/types"
 	"github.com/sirupsen/logrus"
-	floats2 "gonum.org/v1/gonum/floats"
 )
 
 const ID = "harmonic"
@@ -342,9 +341,9 @@ func (s *Strategy) Run(ctx context.Context, orderExecutor bbgo.OrderExecutor, se
 	states.Update(0)
 	s.session.MarketDataStream.OnKLineClosed(types.KLineWith(s.Symbol, s.Interval, func(kline types.KLine) {
 
-		log.Infof("Shark Score: %f, Current Price: %f", s.shark.Last(), kline.Close.Float64())
+		log.Infof("shark score: %f, current price: %f", s.shark.Last(), kline.Close.Float64())
 
-		nextState := alpha(s.shark.Array(s.Window), states.Array(s.Window), s.Window)
+		nextState := hmm(s.shark.Array(s.Window), states.Array(s.Window), s.Window)
 		states.Update(nextState)
 		log.Infof("Denoised signal via HMM: %f", states.Last())
 
@@ -367,7 +366,7 @@ func (s *Strategy) Run(ctx context.Context, orderExecutor bbgo.OrderExecutor, se
 				Side:     types.SideTypeBuy,
 				Quantity: s.Quantity,
 				Type:     types.OrderTypeMarket,
-				Tag:      "shark long",
+				Tag:      "sharkLong",
 			})
 		} else if states.Mean(5) == -1 && direction != -1 {
 			_, _ = s.orderExecutor.SubmitOrders(ctx, types.SubmitOrder{
@@ -375,7 +374,7 @@ func (s *Strategy) Run(ctx context.Context, orderExecutor bbgo.OrderExecutor, se
 				Side:     types.SideTypeSell,
 				Quantity: s.Quantity,
 				Type:     types.OrderTypeMarket,
-				Tag:      "shark short",
+				Tag:      "sharkShort",
 			})
 		}
 	}))
@@ -402,7 +401,7 @@ func (s *Strategy) Run(ctx context.Context, orderExecutor bbgo.OrderExecutor, se
 }
 
 // TODO: dirichlet distribution is a too naive solution
-func observationDistribution(y_t, x_t float64) float64 {
+func observeDistribution(y_t, x_t float64) float64 {
 	if x_t == 0. && y_t == 0 {
 		// observed zero value from indicator when in neutral state
 		return 1.
@@ -417,7 +416,7 @@ func observationDistribution(y_t, x_t float64) float64 {
 	}
 }
 
-func transitionProbability(x_t0, x_t1 int) float64 {
+func transitProbability(x_t0, x_t1 int) float64 {
 	// stick to the same sate
 	if x_t0 == x_t1 {
 		return 0.99
@@ -426,7 +425,21 @@ func transitionProbability(x_t0, x_t1 int) float64 {
 	return 1 - 0.99
 }
 
-func alpha(y_t []float64, x_t []float64, l int) float64 {
+// HMM main function, ref: https://tr8dr.github.io/HMMFiltering/
+/*
+# initialize time step 0 using state priors and observation dist p(y | x = s)
+for si in states:
+    alpha[t = 0, state = si] = pi[si] * p(y[0] | x = si)
+
+# determine alpha for t = 1 .. n
+for t in 1 .. n:
+    for sj in states:
+        alpha[t,sj] = max([alpha[t-1,si] * M[si,sj] for si in states]) * p(y[t] | x = sj)
+
+# determine current state at time t
+return argmax(alpha[t,si] over si)
+*/
+func hmm(y_t []float64, x_t []float64, l int) float64 {
 	al := make([]float64, l)
 	an := make([]float64, l)
 	as := make([]float64, l)
@@ -440,26 +453,29 @@ func alpha(y_t []float64, x_t []float64, l int) float64 {
 			sin := make([]float64, 3)
 			sis := make([]float64, 3)
 			for i := -1; i <= 1; i++ {
-				sil = append(sil, x_t[n-1-1]*transitionProbability(i, j))
-				sin = append(sin, x_t[n-1-1]*transitionProbability(i, j))
-				sis = append(sis, x_t[n-1-1]*transitionProbability(i, j))
+				sil = append(sil, x_t[n-1-1]*transitProbability(i, j))
+				sin = append(sin, x_t[n-1-1]*transitProbability(i, j))
+				sis = append(sis, x_t[n-1-1]*transitProbability(i, j))
 			}
 			if j > 0 {
-				long = floats2.Max(sil) * observationDistribution(y_t[n-1], float64(j))
+				_, longArr := floats.MinMax(sil, 3)
+				long = longArr[0] * observeDistribution(y_t[n-1], float64(j))
 				al = append(al, long)
 			} else if j == 0 {
-				neut = floats2.Max(sin) * observationDistribution(y_t[n-1], float64(j))
+				_, neutArr := floats.MinMax(sin, 3)
+				neut = neutArr[0] * observeDistribution(y_t[n-1], float64(j))
 				an = append(an, neut)
 			} else if j < 0 {
-				short = floats2.Max(sis) * observationDistribution(y_t[n-1], float64(j))
+				_, shortArr := floats.MinMax(sis, 3)
+				short = shortArr[0] * observeDistribution(y_t[n-1], float64(j))
 				as = append(as, short)
 			}
 		}
 	}
-	maximum := floats2.Max([]float64{long, neut, short})
-	if maximum == long {
+	_, maximum := floats.MinMax([]float64{long, neut, short}, 3)
+	if maximum[0] == long {
 		return 1
-	} else if maximum == short {
+	} else if maximum[0] == short {
 		return -1
 	}
 	return 0