use volume > mean + 2*std for volume check

pkg/strategy/sentinel/strategy.go

@@ -88,8 +88,13 @@ func (s *Strategy) Run(ctx context.Context, orderExecutor bbgo.OrderExecutor, se
 		volumes := s.extractVolumes(s.klines)
 		samples := s.generateSamples(volumes)
 
-		if s.shouldSkipIsolationForest(volumes, samples) {
-			s.logSkipIsolationForest(samples, volumes, kline)
+		lastVolume := volumes[len(volumes)-1]
+		lastMean := samples[len(samples)-1][0]
+		lastStd := samples[len(samples)-1][1]
+
+		// isVolumeSignificantlyAboveMean
+		if lastVolume < lastMean+2*lastStd {
+			log.Infof("Volume is not significantly above mean, skipping isolation forest calculation, symbol: %s, volume: %f, mean: %f, std: %f", s.Symbol, lastVolume, lastMean, lastStd)
 			return
 		}
 
@@ -145,21 +150,11 @@ func (s *Strategy) generateSamples(volumes floats.Slice) [][]float64 {
 
 		mean := subset.Mean()
 		std := subset.Std()
-		samples = append(samples, []float64{mean, std})
+		samples = append(samples, []float64{mean, std, subset[len(subset)-1]})
 	}
 	return samples
 }
 
-func (s *Strategy) shouldSkipIsolationForest(volumes floats.Slice, samples [][]float64) bool {
-	volumeMean := volumes.Mean()
-	lastMovingMean := samples[len(samples)-1][0]
-	return lastMovingMean < volumeMean
-}
-
-func (s *Strategy) logSkipIsolationForest(samples [][]float64, volumes floats.Slice, kline types.KLine) {
-	log.Infof("Skipping isolation forest calculation for symbol: %s, last moving mean: %f, average volume: %f, kline: %s", s.Symbol, samples[len(samples)-1][0], volumes.Mean(), kline.String())
-}
-
 func (s *Strategy) fitIsolationForest(samples [][]float64) {
 	if s.retrainingRateLimiter.Allow() {
 		s.IsolationForest = iforest.New()