diff --git a/src/main/scala/com/amazon/deequ/analyzers/applicability/Applicability.scala b/src/main/scala/com/amazon/deequ/analyzers/applicability/Applicability.scala
index e2c282c14..2299c5e21 100644
--- a/src/main/scala/com/amazon/deequ/analyzers/applicability/Applicability.scala
+++ b/src/main/scala/com/amazon/deequ/analyzers/applicability/Applicability.scala
@@ -21,7 +21,7 @@ import java.util.Calendar
 
 import com.amazon.deequ.analyzers.{Analyzer, State}
 import com.amazon.deequ.checks.Check
-import com.amazon.deequ.constraints.{AnalysisBasedConstraint, Constraint, ConstraintDecorator}
+import com.amazon.deequ.constraints.{AnalysisBasedConstraint, AnomalyBasedConstraint, Constraint, ConstraintDecorator}
 import com.amazon.deequ.metrics.Metric
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{DataFrame, Row, SparkSession}
@@ -187,9 +187,13 @@ private[deequ] class Applicability(session: SparkSession) {
         case (name, nc: ConstraintDecorator) => name -> nc.inner
         case (name, c: Constraint) => name -> c
       }
-      .collect { case (name, constraint: AnalysisBasedConstraint[_, _, _]) =>
-        val metric = constraint.analyzer.calculate(data).value
-        name -> metric
+      .collect {
+        case (name, constraint: AnalysisBasedConstraint[_, _, _]) =>
+          val metric = constraint.analyzer.calculate(data).value
+          name -> metric
+        case (name, constraint: AnomalyBasedConstraint[_, _, _]) =>
+          val metric = constraint.analyzer.calculate(data).value
+          name -> metric
       }
 
     val constraintApplicabilities = check.constraints.zip(namedMetrics).map {
diff --git a/src/main/scala/com/amazon/deequ/anomalydetection/AnomalyDetectionStrategy.scala b/src/main/scala/com/amazon/deequ/anomalydetection/AnomalyDetectionStrategy.scala
index 0c3f6805e..042983a0f 100644
--- a/src/main/scala/com/amazon/deequ/anomalydetection/AnomalyDetectionStrategy.scala
+++ b/src/main/scala/com/amazon/deequ/anomalydetection/AnomalyDetectionStrategy.scala
@@ -28,5 +28,5 @@ trait AnomalyDetectionStrategy {
     */
   def detect(
     dataSeries: Vector[Double],
-    searchInterval: (Int, Int) = (0, Int.MaxValue)): Seq[(Int, Anomaly)]
+    searchInterval: (Int, Int) = (0, Int.MaxValue)): Seq[(Int, AnomalyDetectionDataPoint)]
 }
diff --git a/src/main/scala/com/amazon/deequ/anomalydetection/AnomalyDetector.scala b/src/main/scala/com/amazon/deequ/anomalydetection/AnomalyDetector.scala
index e7146c0e9..0f8a18f87 100644
--- a/src/main/scala/com/amazon/deequ/anomalydetection/AnomalyDetector.scala
+++ b/src/main/scala/com/amazon/deequ/anomalydetection/AnomalyDetector.scala
@@ -39,7 +39,7 @@ case class AnomalyDetector(strategy: AnomalyDetectionStrategy) {
   def isNewPointAnomalous(
       historicalDataPoints: Seq[DataPoint[Double]],
       newPoint: DataPoint[Double])
-    : DetectionResult = {
+    : AnomalyDetectionResult = {
 
     require(historicalDataPoints.nonEmpty, "historicalDataPoints must not be empty!")
 
@@ -57,11 +57,7 @@ case class AnomalyDetector(strategy: AnomalyDetectionStrategy) {
     val allDataPoints = sortedDataPoints :+ newPoint
 
     // Run anomaly
-    val anomalies = detectAnomaliesInHistory(allDataPoints, (newPoint.time, Long.MaxValue))
-      .anomalies
-
-    // Create a Detection result with all anomalies
-    DetectionResult(anomalies)
+    detectAnomaliesInHistory(allDataPoints, (newPoint.time, Long.MaxValue))
   }
 
   /**
@@ -74,7 +70,7 @@ case class AnomalyDetector(strategy: AnomalyDetectionStrategy) {
   def detectAnomaliesInHistory(
       dataSeries: Seq[DataPoint[Double]],
       searchInterval: (Long, Long) = (Long.MinValue, Long.MaxValue))
-    : DetectionResult = {
+    : AnomalyDetectionResult = {
 
     def findIndexForBound(sortedTimestamps: Seq[Long], boundValue: Long): Int = {
       sortedTimestamps.search(boundValue).insertionPoint
@@ -97,6 +93,6 @@ case class AnomalyDetector(strategy: AnomalyDetectionStrategy) {
     val anomalies = strategy.detect(
       sortedSeries.flatMap { _.metricValue }.toVector, (lowerBoundIndex, upperBoundIndex))
 
-    DetectionResult(anomalies.map { case (index, anomaly) => (sortedTimestamps(index), anomaly) })
+    AnomalyDetectionResult(anomalies.map { case (index, anomaly) => (sortedTimestamps(index), anomaly) })
   }
 }
diff --git a/src/main/scala/com/amazon/deequ/anomalydetection/BaseChangeStrategy.scala b/src/main/scala/com/amazon/deequ/anomalydetection/BaseChangeStrategy.scala
index e00c86772..86a183719 100644
--- a/src/main/scala/com/amazon/deequ/anomalydetection/BaseChangeStrategy.scala
+++ b/src/main/scala/com/amazon/deequ/anomalydetection/BaseChangeStrategy.scala
@@ -80,7 +80,7 @@ trait BaseChangeStrategy
   override def detect(
     dataSeries: Vector[Double],
     searchInterval: (Int, Int))
-  : Seq[(Int, Anomaly)] = {
+  : Seq[(Int, AnomalyDetectionDataPoint)] = {
     val (start, end) = searchInterval
 
     require(start <= end,
@@ -89,15 +89,24 @@ trait BaseChangeStrategy
     val startPoint = Seq(start - order, 0).max
     val data = diff(DenseVector(dataSeries.slice(startPoint, end): _*), order).data
 
-    data.zipWithIndex.filter { case (value, _) =>
-      (value < maxRateDecrease.getOrElse(Double.MinValue)
-        || value > maxRateIncrease.getOrElse(Double.MaxValue))
-    }
-      .map { case (change, index) =>
-      (index + startPoint + order, Anomaly(Option(dataSeries(index + startPoint + order)), 1.0,
-        Some(s"[AbsoluteChangeStrategy]: Change of $change is not in bounds [" +
-          s"${maxRateDecrease.getOrElse(Double.MinValue)}, " +
-          s"${maxRateIncrease.getOrElse(Double.MaxValue)}]. Order=$order")))
+    val lowerBound = maxRateDecrease.getOrElse(Double.MinValue)
+    val upperBound = maxRateIncrease.getOrElse(Double.MaxValue)
+
+
+    data.zipWithIndex.map {
+      case (change, index) =>
+        val outputSequenceIndex = index + startPoint + order
+        val value = dataSeries(outputSequenceIndex)
+        val (detail, isAnomaly) = if (change < lowerBound || change > upperBound) {
+          (Some(s"[AbsoluteChangeStrategy]: Change of $change is not in bounds [" +
+            s"$lowerBound, " +
+            s"$upperBound]. Order=$order"), true)
+        }
+        else {
+          (None, false)
+        }
+        (outputSequenceIndex, AnomalyDetectionDataPoint(value, change,
+          AnomalyThreshold(lowerBound = Bound(lowerBound), upperBound = Bound(upperBound)), isAnomaly, 1.0, detail))
     }
   }
 }
diff --git a/src/main/scala/com/amazon/deequ/anomalydetection/BatchNormalStrategy.scala b/src/main/scala/com/amazon/deequ/anomalydetection/BatchNormalStrategy.scala
index baff49c03..f4b7cba5b 100644
--- a/src/main/scala/com/amazon/deequ/anomalydetection/BatchNormalStrategy.scala
+++ b/src/main/scala/com/amazon/deequ/anomalydetection/BatchNormalStrategy.scala
@@ -51,7 +51,7 @@ case class BatchNormalStrategy(
     */
   override def detect(
     dataSeries: Vector[Double],
-    searchInterval: (Int, Int)): Seq[(Int, Anomaly)] = {
+    searchInterval: (Int, Int)): Seq[(Int, AnomalyDetectionDataPoint)] = {
 
     val (searchStart, searchEnd) = searchInterval
 
@@ -83,13 +83,15 @@ case class BatchNormalStrategy(
 
     dataSeries.zipWithIndex
       .slice(searchStart, searchEnd)
-      .filter { case (value, _) => value > upperBound || value < lowerBound }
       .map { case (value, index) =>
-
-        val detail = Some(s"[BatchNormalStrategy]: Value $value is not in " +
-          s"bounds [$lowerBound, $upperBound].")
-
-        (index, Anomaly(Option(value), 1.0, detail))
+        val (detail, isAnomaly) = if (value > upperBound || value < lowerBound) {
+          (Some(s"[BatchNormalStrategy]: Value $value is not in " +
+            s"bounds [$lowerBound, $upperBound]."), true)
+        } else {
+          (None, false)
+        }
+        (index, AnomalyDetectionDataPoint(value, value,
+          AnomalyThreshold(lowerBound = Bound(lowerBound), upperBound = Bound(upperBound)), isAnomaly, 1.0, detail))
       }
   }
 }
diff --git a/src/main/scala/com/amazon/deequ/anomalydetection/DetectionResult.scala b/src/main/scala/com/amazon/deequ/anomalydetection/DetectionResult.scala
index 01c5f171c..6f0ab9f5d 100644
--- a/src/main/scala/com/amazon/deequ/anomalydetection/DetectionResult.scala
+++ b/src/main/scala/com/amazon/deequ/anomalydetection/DetectionResult.scala
@@ -16,24 +16,47 @@
 
 package com.amazon.deequ.anomalydetection
 
-class Anomaly(
-    val value: Option[Double],
-    val confidence: Double,
-    val detail: Option[String] = None) {
+
+/**
+ * Anomaly Detection Data Point class (previously Anomaly)
+ *
+ * @param dataMetricValue The metric value that is the data point
+ * @param anomalyMetricValue The metric value that is being used in the anomaly calculation.
+ *                           This usually aligns with dataMetricValue but not always,
+ *                           like in a rate of change strategy where the rate of change is the anomaly metric
+ *                           which may not equal the actual data point value
+ * @param anomalyThreshold The thresholds used in the anomaly check, the anomalyMetricValue is
+ *                         compared to this threshold
+ * @param isAnomaly If the data point is an anomaly
+ * @param confidence TODO fill in more info about this
+ * @param detail Detailed error message
+ */
+class AnomalyDetectionDataPoint(
+                                 val dataMetricValue: Double,
+                                 val anomalyMetricValue: Double,
+                                 val anomalyThreshold: AnomalyThreshold,
+                                 val isAnomaly: Boolean,
+                                 val confidence: Double,
+                                 val detail: Option[String]) {
 
   def canEqual(that: Any): Boolean = {
-    that.isInstanceOf[Anomaly]
+    that.isInstanceOf[AnomalyDetectionDataPoint]
   }
 
   /**
-    * Tests anomalies for equality. Ignores detailed explanation.
+    * Tests anomalyDetectionDataPoints for equality. Ignores detailed explanation.
     *
     * @param obj The object/ anomaly to compare against
-    * @return true, if and only if the value and confidence are the same
+    * @return true, if and only if the dataMetricValue, anomalyMetricValue, anomalyThreshold, isAnomaly
+   *         and confidence are the same
     */
   override def equals(obj: Any): Boolean = {
     obj match {
-      case anomaly: Anomaly => anomaly.value == value && anomaly.confidence == confidence
+      case anomaly: AnomalyDetectionDataPoint => anomaly.dataMetricValue == dataMetricValue &&
+        anomaly.anomalyMetricValue == anomalyMetricValue &&
+        anomaly.anomalyThreshold == anomalyThreshold &&
+        anomaly.isAnomaly == isAnomaly &&
+        anomaly.confidence == confidence
       case _ => false
     }
   }
@@ -41,16 +64,72 @@ class Anomaly(
   override def hashCode: Int = {
     val prime = 31
     var result = 1
-    result = prime * result + (if (value == null) 0 else value.hashCode)
-    prime * result + confidence.hashCode
+    result = prime * result + dataMetricValue.hashCode()
+    result = prime * result + anomalyMetricValue.hashCode()
+    result = prime * result + anomalyThreshold.hashCode()
+    result = prime * result + isAnomaly.hashCode()
+    result = prime * result + confidence.hashCode()
+    result
   }
 
 }
 
-object Anomaly {
-  def apply(value: Option[Double], confidence: Double, detail: Option[String] = None): Anomaly = {
-    new Anomaly(value, confidence, detail)
+object AnomalyDetectionDataPoint {
+  def apply(dataMetricValue: Double, anomalyMetricValue: Double,
+            anomalyThreshold: AnomalyThreshold = AnomalyThreshold(), isAnomaly: Boolean = false,
+            confidence: Double, detail: Option[String] = None
+           ): AnomalyDetectionDataPoint = {
+    new AnomalyDetectionDataPoint(dataMetricValue, anomalyMetricValue, anomalyThreshold, isAnomaly, confidence, detail)
   }
 }
 
-case class DetectionResult(anomalies: Seq[(Long, Anomaly)] = Seq.empty)
+
+/**
+ * AnomalyThreshold class
+ * Defines threshold for the anomaly detection, defaults to inclusive bounds of Double.Min and Double.Max
+ * @param upperBound The upper bound or threshold
+ * @param lowerBound The lower bound or threshold
+ */
+case class AnomalyThreshold(lowerBound: Bound = Bound(Double.MinValue), upperBound: Bound = Bound(Double.MaxValue))
+
+/**
+ * Bound Class
+ * Class representing a threshold/bound, with value and inclusive/exclusive boolean
+ * @param value The value of the bound as a Double
+ * @param inclusive Boolean indicating if the Bound is inclusive or not
+ */
+case class Bound(value: Double, inclusive: Boolean = true)
+
+
+
+/**
+ * AnomalyDetectionResult Class
+ * This class is returned from the detectAnomaliesInHistory function
+ * @param anomalyDetectionDataPointSequence The sequence of (timestamp, anomaly) pairs
+ */
+case class AnomalyDetectionResult(anomalyDetectionDataPointSequence: Seq[(Long, AnomalyDetectionDataPoint)] = Seq.empty)
+
+/**
+ * AnomalyDetectionAssertionResult Class
+ * This class is returned by the anomaly detection assertion function
+ * @param hasNoAnomaly Boolean indicating if anomaly was detected
+ * @param anomalyDetectionMetadata Anomaly Detection metadata class containing anomaly details
+ *                                 about the data point being checked
+ */
+case class AnomalyDetectionAssertionResult(hasNoAnomaly: Boolean, anomalyDetectionMetadata: AnomalyDetectionMetadata)
+
+
+/**
+ * AnomalyDetectionMetadata Class
+ * This class containst anomaly detection metadata and is currently an optional field
+ * in the ConstraintResult class that is exposed to users
+ *
+ * Currently, anomaly detection only runs on "newest" data point (referring to the dataframe being
+ * run on by the verification suite) and not multiple data points, so this metadata class only contains
+ * one anomalyDetectionDataPoint for now
+ * In the future, if we allow the anomaly check to detect multiple points, we can return the anomalyDetectionResult
+ * instead, which contains a sequence of (Long, AnomalyDetectionDataPoints)
+ * @param anomalyDetectionDataPoint Anomaly detection data point
+ */
+case class AnomalyDetectionMetadata(anomalyDetectionDataPoint: AnomalyDetectionDataPoint)
+
diff --git a/src/main/scala/com/amazon/deequ/anomalydetection/OnlineNormalStrategy.scala b/src/main/scala/com/amazon/deequ/anomalydetection/OnlineNormalStrategy.scala
index 8bf8b634c..69afac963 100644
--- a/src/main/scala/com/amazon/deequ/anomalydetection/OnlineNormalStrategy.scala
+++ b/src/main/scala/com/amazon/deequ/anomalydetection/OnlineNormalStrategy.scala
@@ -130,7 +130,7 @@ case class OnlineNormalStrategy(
   override def detect(
       dataSeries: Vector[Double],
       searchInterval: (Int, Int))
-    : Seq[(Int, Anomaly)] = {
+    : Seq[(Int, AnomalyDetectionDataPoint)] = {
 
     val (searchStart, searchEnd) = searchInterval
 
@@ -139,7 +139,6 @@ case class OnlineNormalStrategy(
     computeStatsAndAnomalies(dataSeries, searchInterval)
       .zipWithIndex
       .slice(searchStart, searchEnd)
-      .filter { case (result, _) => result.isAnomaly }
       .map { case (calcRes, index) =>
         val lowerBound =
           calcRes.mean - lowerDeviationFactor.getOrElse(Double.MaxValue) * calcRes.stdDev
@@ -149,7 +148,11 @@ case class OnlineNormalStrategy(
         val detail = Some(s"[OnlineNormalStrategy]: Value ${dataSeries(index)} is not in " +
           s"bounds [$lowerBound, $upperBound].")
 
-        (index, Anomaly(Option(dataSeries(index)), 1.0, detail))
+        val value = dataSeries(index)
+
+        (index, AnomalyDetectionDataPoint(value, value,
+          AnomalyThreshold(lowerBound = Bound(lowerBound), upperBound = Bound(upperBound)),
+          calcRes.isAnomaly, 1.0, detail))
       }
   }
 }
diff --git a/src/main/scala/com/amazon/deequ/anomalydetection/SimpleThresholdStrategy.scala b/src/main/scala/com/amazon/deequ/anomalydetection/SimpleThresholdStrategy.scala
index ec7f5df74..5564c2ed5 100644
--- a/src/main/scala/com/amazon/deequ/anomalydetection/SimpleThresholdStrategy.scala
+++ b/src/main/scala/com/amazon/deequ/anomalydetection/SimpleThresholdStrategy.scala
@@ -38,7 +38,7 @@ case class SimpleThresholdStrategy(
     */
   override def detect(
     dataSeries: Vector[Double],
-    searchInterval: (Int, Int)): Seq[(Int, Anomaly)] = {
+    searchInterval: (Int, Int)): Seq[(Int, AnomalyDetectionDataPoint)] = {
 
     val (searchStart, searchEnd) = searchInterval
 
@@ -46,13 +46,17 @@ case class SimpleThresholdStrategy(
 
     dataSeries.zipWithIndex
       .slice(searchStart, searchEnd)
-      .filter { case (value, _) => value < lowerBound || value > upperBound }
       .map { case (value, index) =>
 
-        val detail = Some(s"[SimpleThresholdStrategy]: Value $value is not in " +
-          s"bounds [$lowerBound, $upperBound]")
+        val (detail, isAnomaly) = if ( value < lowerBound || value > upperBound ) {
+          (Some(s"[SimpleThresholdStrategy]: Value $value is not in " +
+            s"bounds [$lowerBound, $upperBound]"), true)
+        } else {
+          (None, false)
+        }
 
-        (index, Anomaly(Option(value), 1.0, detail))
+        (index, AnomalyDetectionDataPoint(value, value,
+          AnomalyThreshold(lowerBound = Bound(lowerBound), upperBound = Bound(upperBound)), isAnomaly, 1.0, detail))
       }
   }
 }
diff --git a/src/main/scala/com/amazon/deequ/anomalydetection/seasonal/HoltWinters.scala b/src/main/scala/com/amazon/deequ/anomalydetection/seasonal/HoltWinters.scala
index 0ee0ac25f..a129352f4 100644
--- a/src/main/scala/com/amazon/deequ/anomalydetection/seasonal/HoltWinters.scala
+++ b/src/main/scala/com/amazon/deequ/anomalydetection/seasonal/HoltWinters.scala
@@ -18,7 +18,7 @@ package com.amazon.deequ.anomalydetection.seasonal
 
 import breeze.linalg.DenseVector
 import breeze.optimize.{ApproximateGradientFunction, DiffFunction, LBFGSB}
-import com.amazon.deequ.anomalydetection.{Anomaly, AnomalyDetectionStrategy}
+import com.amazon.deequ.anomalydetection.{AnomalyDetectionDataPoint, AnomalyDetectionStrategy, AnomalyThreshold, Bound}
 
 import collection.mutable.ListBuffer
 
@@ -178,17 +178,27 @@ class HoltWinters(
       forecasts: Seq[Double],
       startIndex: Int,
       residualSD: Double)
-    : Seq[(Int, Anomaly)] = {
+    : Seq[(Int, AnomalyDetectionDataPoint)] = {
 
     testSeries.zip(forecasts).zipWithIndex
-      .collect { case ((inputValue, forecastedValue), detectionIndex)
-        if math.abs(inputValue - forecastedValue) > 1.96 * residualSD =>
-
-        detectionIndex + startIndex -> Anomaly(
-          value = Some(inputValue),
-          confidence = 1.0,
-          detail = Some(s"Forecasted $forecastedValue for observed value $inputValue")
+      .collect { case ((inputValue, forecastedValue), detectionIndex) =>
+        val anomalyMetricValue = math.abs(inputValue - forecastedValue)
+        val upperBound = 1.96 * residualSD
+
+        val (detail, isAnomaly) = if (anomalyMetricValue > upperBound) {
+          (Some(s"Forecasted $forecastedValue for observed value $inputValue"), true)
+        } else {
+          (None, false)
+        }
+        detectionIndex + startIndex -> AnomalyDetectionDataPoint(
+            dataMetricValue = inputValue,
+            anomalyMetricValue = anomalyMetricValue,
+            anomalyThreshold = AnomalyThreshold(upperBound = Bound(upperBound)),
+            isAnomaly = isAnomaly,
+            confidence = 1.0,
+            detail = detail
         )
+
     }
   }
 
@@ -202,7 +212,7 @@ class HoltWinters(
   override def detect(
       dataSeries: Vector[Double],
       searchInterval: (Int, Int) = (0, Int.MaxValue))
-    : Seq[(Int, Anomaly)] = {
+    : Seq[(Int, AnomalyDetectionDataPoint)] = {
 
     require(dataSeries.nonEmpty, "Provided data series is empty")
 
diff --git a/src/main/scala/com/amazon/deequ/checks/Check.scala b/src/main/scala/com/amazon/deequ/checks/Check.scala
index 9f6f6ea03..ce60b4a24 100644
--- a/src/main/scala/com/amazon/deequ/checks/Check.scala
+++ b/src/main/scala/com/amazon/deequ/checks/Check.scala
@@ -16,19 +16,22 @@
 
 package com.amazon.deequ.checks
 
+import com.amazon.deequ.analyzers.AnalyzerOptions
+import com.amazon.deequ.anomalydetection.AnomalyDetectionAssertionResult
+import com.amazon.deequ.anomalydetection.AnomalyDetectionMetadata
+import com.amazon.deequ.anomalydetection.AnomalyDetectionResult
+import com.amazon.deequ.anomalydetection.AnomalyDetectionStrategy
+import com.amazon.deequ.anomalydetection.AnomalyDetector
+import com.amazon.deequ.anomalydetection.DataPoint
+import com.amazon.deequ.anomalydetection.HistoryUtils
 import com.amazon.deequ.analyzers.runners.AnalyzerContext
 import com.amazon.deequ.analyzers.Analyzer
-import com.amazon.deequ.analyzers.AnalyzerOptions
 import com.amazon.deequ.analyzers.DataSynchronizationAnalyzer
 import com.amazon.deequ.analyzers.DataSynchronizationState
 import com.amazon.deequ.analyzers.Histogram
 import com.amazon.deequ.analyzers.KLLParameters
 import com.amazon.deequ.analyzers.Patterns
 import com.amazon.deequ.analyzers.State
-import com.amazon.deequ.anomalydetection.HistoryUtils
-import com.amazon.deequ.anomalydetection.AnomalyDetectionStrategy
-import com.amazon.deequ.anomalydetection.AnomalyDetector
-import com.amazon.deequ.anomalydetection.DataPoint
 import com.amazon.deequ.checks.ColumnCondition.isAnyNotNull
 import com.amazon.deequ.checks.ColumnCondition.isEachNotNull
 import com.amazon.deequ.constraints.Constraint._
@@ -39,7 +42,6 @@ import com.amazon.deequ.metrics.Metric
 import com.amazon.deequ.repository.MetricsRepository
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.expressions.UserDefinedFunction
-
 import scala.util.matching.Regex
 
 object CheckLevel extends Enumeration {
@@ -1159,6 +1161,7 @@ case class Check(
       }
       .collect {
         case constraint: AnalysisBasedConstraint[_, _, _] => constraint.analyzer
+        case constraint: AnomalyBasedConstraint[_, _, _] => constraint.analyzer
       }
       .map { _.asInstanceOf[Analyzer[_, Metric[_]]] }
       .toSet
@@ -1201,7 +1204,7 @@ object Check {
       afterDate: Option[Long],
       beforeDate: Option[Long])(
       currentMetricValue: Double)
-    : Boolean = {
+    : AnomalyDetectionAssertionResult = {
 
     // Get history keys
     var repositoryLoader = metricsRepository.load()
@@ -1245,10 +1248,32 @@ object Check {
 
     // Run given anomaly detection strategy and return false if the newest value is an Anomaly
     val anomalyDetector = AnomalyDetector(anomalyDetectionStrategy)
-    val detectedAnomalies = anomalyDetector.isNewPointAnomalous(
+    val anomalyDetectionResult: AnomalyDetectionResult = anomalyDetector.isNewPointAnomalous(
       HistoryUtils.extractMetricValues[Double](historicalMetrics),
       DataPoint(testDateTime, Some(currentMetricValue)))
 
-    detectedAnomalies.anomalies.isEmpty
+
+    // this function checks if the newest point is anomalous and returns a boolean for assertion,
+    // along with that newest point with anomaly check details
+    getNewestPointAnomalyResults(anomalyDetectionResult)
+  }
+
+  private[deequ] def getNewestPointAnomalyResults(anomalyDetectionResult: AnomalyDetectionResult):
+  AnomalyDetectionAssertionResult = {
+    val (hasNoAnomaly, anomalyDetectionMetaData): (Boolean, AnomalyDetectionMetadata) = {
+
+      // Based on upstream code, this anomaly detection data point sequence should never be empty
+      require(anomalyDetectionResult.anomalyDetectionDataPointSequence != Nil,
+        "AnomalyDetectionDataPointSequence cannot be empty")
+
+      // get the last anomaly detection data point of sequence (there should only be one element for now)
+      // and check the isAnomaly boolean, also return the last anomaly detection data point
+      // wrapped in the anomaly detection metadata class
+      anomalyDetectionResult.anomalyDetectionDataPointSequence match {
+        case _ :+ lastAnomalyDataPoint =>
+          (!lastAnomalyDataPoint._2.isAnomaly, AnomalyDetectionMetadata(lastAnomalyDataPoint._2))
+      }
+    }
+    AnomalyDetectionAssertionResult(hasNoAnomaly = hasNoAnomaly, anomalyDetectionMetadata = anomalyDetectionMetaData)
   }
 }
diff --git a/src/main/scala/com/amazon/deequ/constraints/AnomalyBasedConstraint.scala b/src/main/scala/com/amazon/deequ/constraints/AnomalyBasedConstraint.scala
new file mode 100644
index 000000000..4c3392fdb
--- /dev/null
+++ b/src/main/scala/com/amazon/deequ/constraints/AnomalyBasedConstraint.scala
@@ -0,0 +1,134 @@
+/**
+ * Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"). You may not
+ * use this file except in compliance with the License. A copy of the License
+ * is located at
+ *
+ *     http://aws.amazon.com/apache2.0/
+ *
+ * or in the "license" file accompanying this file. This file is distributed on
+ * an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
+ * express or implied. See the License for the specific language governing
+ * permissions and limitations under the License.
+ *
+ */
+
+package com.amazon.deequ.constraints
+
+import com.amazon.deequ.analyzers.{Analyzer, State}
+import com.amazon.deequ.anomalydetection.AnomalyDetectionAssertionResult
+import com.amazon.deequ.metrics.Metric
+import org.apache.spark.sql.DataFrame
+
+import scala.util.{Failure, Success}
+
+/**
+ * Case class for anomaly based constraints that provides unified way to access
+ * AnalyzerContext and metrics stored in it.
+ * TODO this differs from AnalysisBasedConstraint only in that it uses an assertion function that
+ *   returns an AnomalyAssertionResult with an anomaly detection metadata as well as the assertion boolean.
+ *   Figure out if it's better to use some inheritance/composition from a common trait/abstract class.
+ *
+ * Runs the analysis and get the value of the metric returned by the analysis,
+ * picks the numeric value that will be used in the assertion function with metric picker
+ * runs the assertion.
+ *
+ * @param analyzer    Analyzer to be run on the data frame
+ * @param assertion   Assertion function that returns an AnomalyDetectionAssertionResult with
+ *                    anomaly detection metadata as well as the assertion boolean
+ * @param valuePicker Optional function to pick the interested part of the metric value that the
+ *                    assertion will be running on. Absence of such function means the metric
+ *                    value would be used in the assertion as it is.
+ * @param hint A hint to provide additional context why a constraint could have failed
+ * @tparam M : Type of the metric value generated by the Analyzer
+ * @tparam V : Type of the value being used in assertion function
+ *
+ */
+private[deequ] case class AnomalyBasedConstraint[S <: State[S], M, V](
+                          analyzer: Analyzer[S, Metric[M]],
+                          private[deequ] val assertion: V => AnomalyDetectionAssertionResult,
+                          private[deequ] val valuePicker: Option[M => V] = None,
+                          private[deequ] val hint: Option[String] = None)
+  extends Constraint {
+
+  private[deequ] def calculateAndEvaluate(data: DataFrame) = {
+    val metric = analyzer.calculate(data)
+    evaluate(Map(analyzer -> metric))
+  }
+
+  override def evaluate(
+                         analysisResults: Map[Analyzer[_, Metric[_]], Metric[_]])
+  : ConstraintResult = {
+
+    val metric = analysisResults.get(analyzer).map(_.asInstanceOf[Metric[M]])
+
+    metric.map(pickValueAndAssert).getOrElse(
+      // Analysis is missing
+      ConstraintResult(this, ConstraintStatus.Failure,
+        message = Some(AnomalyBasedConstraint.MissingAnalysis), metric = metric)
+    )
+  }
+
+  private[this] def pickValueAndAssert(metric: Metric[M]): ConstraintResult = {
+
+    metric.value match {
+      // Analysis done successfully and result metric is there
+      case Success(metricValue) =>
+        try {
+          val assertOn = runPickerOnMetric(metricValue)
+          val anomalyAssertionResult = runAssertion(assertOn)
+
+          if (anomalyAssertionResult.hasNoAnomaly) {
+            ConstraintResult(this, ConstraintStatus.Success, metric = Some(metric),
+              anomalyDetectionMetadata = Some(anomalyAssertionResult.anomalyDetectionMetadata))
+          } else {
+            var errorMessage = s"Value: $assertOn does not meet the constraint requirement," +
+              s" check the anomaly detection metadata!"
+            hint.foreach(hint => errorMessage += s" $hint")
+
+            ConstraintResult(this, ConstraintStatus.Failure, Some(errorMessage), Some(metric),
+              anomalyDetectionMetadata = Some(anomalyAssertionResult.anomalyDetectionMetadata))
+          }
+
+        } catch {
+          case AnomalyBasedConstraint.ConstraintAssertionException(msg) =>
+            ConstraintResult(this, ConstraintStatus.Failure,
+              message = Some(s"${AnomalyBasedConstraint.AssertionException}: $msg!"), metric = Some(metric))
+          case AnomalyBasedConstraint.ValuePickerException(msg) =>
+            ConstraintResult(this, ConstraintStatus.Failure,
+              message = Some(s"${AnomalyBasedConstraint.ProblematicMetricPicker}: $msg!"), metric = Some(metric))
+        }
+      // An exception occurred during analysis
+      case Failure(e) => ConstraintResult(this,
+        ConstraintStatus.Failure, message = Some(e.getMessage), metric = Some(metric))
+    }
+  }
+
+  private def runPickerOnMetric(metricValue: M): V =
+    try {
+      valuePicker.map(function => function(metricValue)).getOrElse(metricValue.asInstanceOf[V])
+    } catch {
+      case e: Exception => throw AnomalyBasedConstraint.ValuePickerException(e.getMessage)
+    }
+
+  private def runAssertion(assertOn: V): AnomalyDetectionAssertionResult =
+    try {
+      assertion(assertOn)
+    } catch {
+      case e: Exception => throw AnomalyBasedConstraint.ConstraintAssertionException(e.getMessage)
+    }
+
+  // 'assertion' and 'valuePicker' are lambdas we have to represent them like '<function1>'
+  override def toString: String =
+    s"AnomalyBasedConstraint($analyzer,<function1>,${valuePicker.map(_ => "<function1>")},$hint)"
+}
+
+private[deequ] object AnomalyBasedConstraint {
+  val MissingAnalysis = "Missing Analysis, can't run the constraint!"
+  val ProblematicMetricPicker = "Can't retrieve the value to assert on"
+  val AssertionException = "Can't execute the assertion"
+
+  private case class ValuePickerException(message: String) extends RuntimeException(message)
+  private case class ConstraintAssertionException(message: String) extends RuntimeException(message)
+}
diff --git a/src/main/scala/com/amazon/deequ/constraints/Constraint.scala b/src/main/scala/com/amazon/deequ/constraints/Constraint.scala
index 5bb8d477e..0cf82d1cd 100644
--- a/src/main/scala/com/amazon/deequ/constraints/Constraint.scala
+++ b/src/main/scala/com/amazon/deequ/constraints/Constraint.scala
@@ -17,6 +17,8 @@
 package com.amazon.deequ.constraints
 
 import com.amazon.deequ.analyzers._
+import com.amazon.deequ.anomalydetection.AnomalyDetectionAssertionResult
+import com.amazon.deequ.anomalydetection.AnomalyDetectionMetadata
 import com.amazon.deequ.metrics.BucketDistribution
 import com.amazon.deequ.metrics.Distribution
 import com.amazon.deequ.metrics.Metric
@@ -30,11 +32,24 @@ object ConstraintStatus extends Enumeration {
   val Success, Failure = Value
 }
 
+/**
+ * ConstraintResult Class
+ *
+ * @param constraint Constraint associated with result
+ * @param status Status of constraint (Success, Failure)
+ * @param message Optional message for errors
+ * @param metric Optional Metric from calculation
+ * @param anomalyDetectionMetadata anomaly detection metadata details (including anomaly thresholds) for users
+ * TODO figure out if the new anomaly metadata field should be here or use inheritance/composition
+ *   in a separate AnomalyConstraintResult class that extends a ConstraintResult trait/abstract class
+ *   since it will only be populated for anomaly constraints and not other constraints
+ */
 case class ConstraintResult(
     constraint: Constraint,
     status: ConstraintStatus.Value,
     message: Option[String] = None,
-    metric: Option[Metric[_]] = None)
+    metric: Option[Metric[_]] = None,
+    anomalyDetectionMetadata: Option[AnomalyDetectionMetadata] = None)
 
 /** Common trait for all data quality constraints */
 trait Constraint extends Serializable {
@@ -219,17 +234,16 @@ object Constraint {
     *
     * @param analyzer           Analyzer for the metric to do Anomaly Detection on
     * @param anomalyAssertion   Function that receives a double input parameter
-    *                           (since the metric is double metric) and returns a boolean
+    *                           (since the metric is double metric) and returns an Anomaly Detection Assertion Result
     * @param hint A hint to provide additional context why a constraint could have failed
     */
   def anomalyConstraint[S <: State[S]](
-      analyzer: Analyzer[S, Metric[Double]],
-      anomalyAssertion: Double => Boolean,
-      hint: Option[String] = None)
+                                        analyzer: Analyzer[S, Metric[Double]],
+                                        anomalyAssertion: Double => AnomalyDetectionAssertionResult,
+                                        hint: Option[String] = None)
     : Constraint = {
 
-    val constraint = AnalysisBasedConstraint[S, Double, Double](analyzer, anomalyAssertion,
-      hint = hint)
+    val constraint = AnomalyBasedConstraint[S, Double, Double](analyzer, anomalyAssertion, hint = hint)
 
     new NamedConstraint(constraint, s"AnomalyConstraint($analyzer)")
   }
diff --git a/src/test/scala/com/amazon/deequ/VerificationSuiteTest.scala b/src/test/scala/com/amazon/deequ/VerificationSuiteTest.scala
index e260d2f18..10c55ccdb 100644
--- a/src/test/scala/com/amazon/deequ/VerificationSuiteTest.scala
+++ b/src/test/scala/com/amazon/deequ/VerificationSuiteTest.scala
@@ -18,7 +18,7 @@ package com.amazon.deequ
 
 import com.amazon.deequ.analyzers._
 import com.amazon.deequ.analyzers.runners.AnalyzerContext
-import com.amazon.deequ.anomalydetection.AbsoluteChangeStrategy
+import com.amazon.deequ.anomalydetection.{AbsoluteChangeStrategy, AnomalyDetectionDataPoint, AnomalyThreshold, Bound}
 import com.amazon.deequ.checks.Check
 import com.amazon.deequ.checks.CheckLevel
 import com.amazon.deequ.checks.CheckStatus
@@ -629,12 +629,33 @@ class VerificationSuiteTest extends WordSpec with Matchers with SparkContextSpec
           .run()
 
         val checkResultsOne = verificationResultOne.checkResults.head._2.status
+        val actualResultsOneAnomalyDetectionDataPoint =
+          verificationResultOne.checkResults.head._2.constraintResults.head
+            .anomalyDetectionMetadata.get.anomalyDetectionDataPoint
+        val expectedResultsOneAnomalyDetectionDataPoint =
+          AnomalyDetectionDataPoint(11.0, 7.0, AnomalyThreshold(Bound(-2.0), Bound(2.0)), isAnomaly = true, 1.0)
+
+
         val checkResultsTwo = verificationResultTwo.checkResults.head._2.status
+        val actualResultsTwoAnomalyDetectionDataPoint =
+          verificationResultTwo.checkResults.head._2.constraintResults.head
+          .anomalyDetectionMetadata.get.anomalyDetectionDataPoint
+        val expectedResultsTwoAnomalyDetectionDataPoint =
+          AnomalyDetectionDataPoint(11.0, 0.0, AnomalyThreshold(Bound(-7.0), Bound(7.0)), isAnomaly = false, 1.0)
+
         val checkResultsThree = verificationResultThree.checkResults.head._2.status
+        val actualResultsThreeAnomalyDetectionDataPoint =
+          verificationResultThree.checkResults.head._2.constraintResults.head
+          .anomalyDetectionMetadata.get.anomalyDetectionDataPoint
+        val expectedResultsThreeAnomalyDetectionDataPoint =
+          AnomalyDetectionDataPoint(11.0, 0.0, AnomalyThreshold(Bound(-7.0), Bound(7.0)), isAnomaly = false, 1.0)
 
         assert(checkResultsOne == CheckStatus.Warning)
+        assert(actualResultsOneAnomalyDetectionDataPoint == expectedResultsOneAnomalyDetectionDataPoint)
         assert(checkResultsTwo == CheckStatus.Success)
+        assert(actualResultsTwoAnomalyDetectionDataPoint == expectedResultsTwoAnomalyDetectionDataPoint)
         assert(checkResultsThree == CheckStatus.Success)
+        assert(actualResultsThreeAnomalyDetectionDataPoint == expectedResultsThreeAnomalyDetectionDataPoint)
       }
     }
 
@@ -674,10 +695,23 @@ class VerificationSuiteTest extends WordSpec with Matchers with SparkContextSpec
           .run()
 
         val checkResultsOne = verificationResultOne.checkResults.values.toSeq(1).status
+        val actualResultsOneAnomalyDetectionDataPoint =
+          verificationResultOne.checkResults.values.toSeq(1).constraintResults.head
+            .anomalyDetectionMetadata.get.anomalyDetectionDataPoint
+        val expectedResultsOneAnomalyDetectionDataPoint =
+          AnomalyDetectionDataPoint(11.0, 7.0, AnomalyThreshold(Bound(-2.0), Bound(2.0)), isAnomaly = true, 1.0)
+
         val checkResultsTwo = verificationResultTwo.checkResults.head._2.status
+        val actualResultsTwoAnomalyDetectionDataPoint =
+          verificationResultTwo.checkResults.head._2.constraintResults.head
+          .anomalyDetectionMetadata.get.anomalyDetectionDataPoint
+        val expectedResultsTwoAnomalyDetectionDataPoint =
+          AnomalyDetectionDataPoint(11.0, 0.0, AnomalyThreshold(Bound(-7.0), Bound(7.0)), isAnomaly = false, 1.0)
 
         assert(checkResultsOne == CheckStatus.Warning)
+        assert(actualResultsOneAnomalyDetectionDataPoint == expectedResultsOneAnomalyDetectionDataPoint)
         assert(checkResultsTwo == CheckStatus.Success)
+        assert(actualResultsTwoAnomalyDetectionDataPoint == expectedResultsTwoAnomalyDetectionDataPoint)
       }
     }
 
diff --git a/src/test/scala/com/amazon/deequ/anomalydetection/AbsoluteChangeStrategyTest.scala b/src/test/scala/com/amazon/deequ/anomalydetection/AbsoluteChangeStrategyTest.scala
index 66d3c737a..f92497c4e 100644
--- a/src/test/scala/com/amazon/deequ/anomalydetection/AbsoluteChangeStrategyTest.scala
+++ b/src/test/scala/com/amazon/deequ/anomalydetection/AbsoluteChangeStrategyTest.scala
@@ -19,6 +19,8 @@ package com.amazon.deequ.anomalydetection
 import breeze.linalg.DenseVector
 import org.scalatest.{Matchers, WordSpec}
 
+import scala.collection.mutable
+
 class AbsoluteChangeStrategyTest extends WordSpec with Matchers {
 
   "Absolute Change Strategy" should {
@@ -32,99 +34,139 @@ class AbsoluteChangeStrategyTest extends WordSpec with Matchers {
       }
     }).toVector
 
+
     "detect all anomalies if no interval specified" in {
-      val anomalyResult = strategy.detect(data)
-      val expected = for (i <- 20 to 31) yield {
-        (i, Anomaly(Option(data(i)), 1.0))
-      }
-      assert(anomalyResult == expected)
+      val anomalyResult = strategy.detect(data).filter({case (_, anom) => anom.isAnomaly})
+      val expectedAnomalyThreshold = AnomalyThreshold(Bound(-2.0), Bound(2.0))
+      val expectedResult: Seq[(Int, AnomalyDetectionDataPoint)] = Seq(
+        (20, AnomalyDetectionDataPoint(20, 19, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (21, AnomalyDetectionDataPoint(-21, -41, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (22, AnomalyDetectionDataPoint(22, 43, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (23, AnomalyDetectionDataPoint(-23, -45, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (24, AnomalyDetectionDataPoint(24, 47, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (25, AnomalyDetectionDataPoint(-25, -49, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (26, AnomalyDetectionDataPoint(26, 51, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (27, AnomalyDetectionDataPoint(-27, -53, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (28, AnomalyDetectionDataPoint(28, 55, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (29, AnomalyDetectionDataPoint(-29, -57, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (30, AnomalyDetectionDataPoint(30, 59, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (31, AnomalyDetectionDataPoint(1, -29, expectedAnomalyThreshold, isAnomaly = true, 1.0))
+      )
+      assert(anomalyResult == expectedResult)
     }
 
     "only detect anomalies in interval" in {
-      val anomalyResult = strategy.detect(data, (25, 50))
-      val expected = for (i <- 25 to 31) yield {
-        (i, Anomaly(Option(data(i)), 1.0))
-      }
-      assert(anomalyResult == expected)
+      val anomalyResult = strategy.detect(data, (25, 50)).filter({case (_, anom) => anom.isAnomaly})
+      val expectedAnomalyThreshold = AnomalyThreshold(Bound(-2.0), Bound(2.0))
+      val expectedResult: Seq[(Int, AnomalyDetectionDataPoint)] = Seq(
+        (25, AnomalyDetectionDataPoint(-25, -49, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (26, AnomalyDetectionDataPoint(26, 51, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (27, AnomalyDetectionDataPoint(-27, -53, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (28, AnomalyDetectionDataPoint(28, 55, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (29, AnomalyDetectionDataPoint(-29, -57, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (30, AnomalyDetectionDataPoint(30, 59, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (31, AnomalyDetectionDataPoint(1, -29, expectedAnomalyThreshold, isAnomaly = true, 1.0))
+      )
+      assert(anomalyResult == expectedResult)
     }
 
     "ignore min rate if none is given" in {
       val strategy = AbsoluteChangeStrategy(None, Some(1.0))
-      val anomalyResult = strategy.detect(data)
-
+      val anomalyResult = strategy.detect(data).filter({case (_, anom) => anom.isAnomaly})
+      val expectedAnomalyThreshold = AnomalyThreshold(upperBound = Bound(1.0))
       // Anomalies with positive values only
-      val expected = for (i <- 20 to 30 by 2) yield {
-        (i, Anomaly(Option(data(i)), 1.0))
-      }
-      assert(anomalyResult == expected)
+      val expectedResult: Seq[(Int, AnomalyDetectionDataPoint)] = Seq(
+        (20, AnomalyDetectionDataPoint(20, 19, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (22, AnomalyDetectionDataPoint(22, 43, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (24, AnomalyDetectionDataPoint(24, 47, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (26, AnomalyDetectionDataPoint(26, 51, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (28, AnomalyDetectionDataPoint(28, 55, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (30, AnomalyDetectionDataPoint(30, 59, expectedAnomalyThreshold, isAnomaly = true, 1.0))
+      )
+
+      assert(anomalyResult == expectedResult)
     }
 
     "ignore max rate if none is given" in {
       val strategy = AbsoluteChangeStrategy(Some(-1.0), None)
-      val anomalyResult = strategy.detect(data)
+      val anomalyResult = strategy.detect(data).filter({case (_, anom) => anom.isAnomaly})
+      val expectedAnomalyThreshold = AnomalyThreshold(lowerBound = Bound(-1.0))
 
       // Anomalies with negative values only
-      val expected = for (i <- 21 to 31 by 2) yield {
-        (i, Anomaly(Option(data(i)), 1.0))
-      }
-      assert(anomalyResult == expected)
+      val expectedResult: Seq[(Int, AnomalyDetectionDataPoint)] = Seq(
+        (21, AnomalyDetectionDataPoint(-21, -41, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (23, AnomalyDetectionDataPoint(-23, -45, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (25, AnomalyDetectionDataPoint(-25, -49, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (27, AnomalyDetectionDataPoint(-27, -53, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (29, AnomalyDetectionDataPoint(-29, -57, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (31, AnomalyDetectionDataPoint(1, -29, expectedAnomalyThreshold, isAnomaly = true, 1.0))
+      )
+      assert(anomalyResult == expectedResult)
     }
 
     "detect no anomalies if rates are set to min/ max value" in {
       val strategy = AbsoluteChangeStrategy(Some(Double.MinValue), Some(Double.MaxValue))
-      val anomalyResult = strategy.detect(data)
+      val anomalyResult = strategy.detect(data).filter({case (_, anom) => anom.isAnomaly})
 
-      val expected: List[(Int, Anomaly)] = List()
-      assert(anomalyResult == expected)
+      val expectedResult: List[(Int, AnomalyDetectionDataPoint)] = List()
+      assert(anomalyResult == expectedResult)
     }
 
     "derive first order correctly" in {
       val data = DenseVector(1.0, 2.0, 4.0, 1.0, 2.0, 8.0)
       val result = strategy.diff(data, 1).data
 
-      val expected = Array(1.0, 2.0, -3.0, 1.0, 6.0)
-      assert(result === expected)
+      val expectedResult = Array(1.0, 2.0, -3.0, 1.0, 6.0)
+      assert(result === expectedResult)
     }
 
     "derive second order correctly" in {
       val data = DenseVector(1.0, 2.0, 4.0, 1.0, 2.0, 8.0)
       val result = strategy.diff(data, 2).data
 
-      val expected = Array(1.0, -5.0, 4.0, 5.0)
-      assert(result === expected)
+      val expectedResult = Array(1.0, -5.0, 4.0, 5.0)
+      assert(result === expectedResult)
     }
     "derive third order correctly" in {
       val data = DenseVector(1.0, 5.0, -10.0, 3.0, 100.0, 0.01, 0.0065)
       val result = strategy.diff(data, 3).data
 
-      val expected = Array(47, 56, -280.99, 296.9765)
-      assert(result === expected)
+      val expectedResult = Array(47, 56, -280.99, 296.9765)
+      assert(result === expectedResult)
     }
 
     "attribute indices correctly for higher orders without search interval" in {
       val data = Vector(0.0, 1.0, 3.0, 6.0, 18.0, 72.0)
       val strategy = AbsoluteChangeStrategy(None, Some(8.0), order = 2)
-      val result = strategy.detect(data)
+      val result = strategy.detect(data).filter({ case (_, anom) => anom.isAnomaly })
 
-      val expected = Seq((4, Anomaly(Option(18.0), 1.0)), (5, Anomaly(Option(72.0), 1.0)))
-      assert(result == expected)
+      val expectedResult = Seq(
+        (4, AnomalyDetectionDataPoint(18.0, 9.0, AnomalyThreshold(upperBound = Bound(8.0)), isAnomaly = true, 1.0)),
+        (5, AnomalyDetectionDataPoint(72.0, 42.0, AnomalyThreshold(upperBound = Bound(8.0)), isAnomaly = true, 1.0))
+      )
+      assert(result == expectedResult)
     }
 
     "attribute indices correctly for higher orders with search interval" in {
       val data = Vector(0.0, 1.0, 3.0, 6.0, 18.0, 72.0)
       val strategy = AbsoluteChangeStrategy(None, Some(8.0), order = 2)
-      val result = strategy.detect(data, (5, 6))
+      val result = strategy.detect(data, (5, 6)).filter({case (_, anom) => anom.isAnomaly})
 
-      val expected = Seq((5, Anomaly(Option(72.0), 1.0)))
-      assert(result == expected)
+      val expectedResult = Seq(
+        (5, AnomalyDetectionDataPoint(72.0, 42.0, AnomalyThreshold(upperBound = Bound(8.0)), isAnomaly = true, 1.0))
+      )
+      assert(result == expectedResult)
     }
 
     "behave like the threshold strategy when order is 0" in {
       val data = Vector(1.0, -1.0, 4.0, -7.0)
-      val result = strategy.detect(data)
+      val result = strategy.detect(data).filter({case (_, anom) => anom.isAnomaly})
 
-      val expected = Seq((2, Anomaly(Option(4.0), 1.0)), (3, Anomaly(Option(-7.0), 1.0)))
-      assert(result == expected)
+      val expectedResult = Seq(
+        (2, AnomalyDetectionDataPoint(4.0, 5.0, AnomalyThreshold(Bound(-2.0), Bound(2.0)), isAnomaly = true, 1.0)),
+        (3, AnomalyDetectionDataPoint(-7.0, -11.0, AnomalyThreshold(Bound(-2.0), Bound(2.0)), isAnomaly = true, 1.0))
+      )
+      assert(result == expectedResult)
     }
 
     "throw an error when rates aren't ordered" in {
@@ -141,18 +183,31 @@ class AbsoluteChangeStrategyTest extends WordSpec with Matchers {
 
     "work fine with empty input" in {
       val emptySeries = Vector[Double]()
-      val anomalyResult = strategy.detect(emptySeries)
+      val anomalyResult = strategy.detect(emptySeries).filter({case (_, anom) => anom.isAnomaly})
 
-      assert(anomalyResult == Seq[(Int, Anomaly)]())
+      assert(anomalyResult == Seq[(Int, AnomalyDetectionDataPoint)]())
     }
 
     "produce error message with correct value and bounds" in {
-      val result = strategy.detect(data)
+      val result = strategy.detect(data).filter({case (_, anom) => anom.isAnomaly})
 
       result.foreach { case (_, anom) =>
         val (value, lowerBound, upperBound) =
           AnomalyDetectionTestUtils.firstThreeDoublesFromString(anom.detail.get)
 
+        assert(value === anom.anomalyMetricValue)
+        assert(value < lowerBound || value > upperBound)
+      }
+    }
+
+    "assert anomalies are outside of anomaly bounds" in {
+      val result = strategy.detect(data).filter({ case (_, anom) => anom.isAnomaly })
+
+      result.foreach { case (_, anom) =>
+        val value = anom.anomalyMetricValue
+        val upperBound = anom.anomalyThreshold.upperBound.value
+        val lowerBound = anom.anomalyThreshold.lowerBound.value
+
         assert(value < lowerBound || value > upperBound)
       }
     }
diff --git a/src/test/scala/com/amazon/deequ/anomalydetection/AnomalyDetectorTest.scala b/src/test/scala/com/amazon/deequ/anomalydetection/AnomalyDetectorTest.scala
index 08f411bd1..5852bca7e 100644
--- a/src/test/scala/com/amazon/deequ/anomalydetection/AnomalyDetectorTest.scala
+++ b/src/test/scala/com/amazon/deequ/anomalydetection/AnomalyDetectorTest.scala
@@ -35,20 +35,20 @@ class AnomalyDetectorTest extends WordSpec with Matchers with MockFactory with P
         DataPoint[Double](2L, None), DataPoint[Double](3L, Option(1.0)))
 
       (fakeAnomalyDetector.detect _ when(Vector(1.0, 2.0, 1.0), (0, 3)))
-        .returns(Seq((1, Anomaly(Option(2.0), 1.0))))
+        .returns(Seq((1, AnomalyDetectionDataPoint(2.0, 2.0, AnomalyThreshold(), confidence = 1.0))))
 
       val anomalyResult = aD.detectAnomaliesInHistory(data, (0L, 4L))
 
-      assert(anomalyResult == DetectionResult(Seq((1L, Anomaly(Option(2.0), 1.0)))))
+      assert(anomalyResult == AnomalyDetectionResult(Seq((1L, AnomalyDetectionDataPoint(2.0, 2.0, confidence = 1.0)))))
     }
 
     "only detect values in range" in {
       (fakeAnomalyDetector.detect _ when(Vector(-1.0, 2.0, 3.0, 0.5), (2, 4)))
-        .returns(Seq((2, Anomaly(Option(3.0), 1.0))))
+        .returns(Seq((2, AnomalyDetectionDataPoint(3.0, 3.0, confidence = 1.0))))
 
       val anomalyResult = aD.detectAnomaliesInHistory(data, (2L, 4L))
 
-      assert(anomalyResult == DetectionResult(Seq((2L, Anomaly(Option(3.0), 1.0)))))
+      assert(anomalyResult == AnomalyDetectionResult(Seq((2L, AnomalyDetectionDataPoint(3.0, 3.0, confidence = 1.0)))))
     }
 
     "throw an error when intervals are not ordered" in {
@@ -65,12 +65,18 @@ class AnomalyDetectorTest extends WordSpec with Matchers with MockFactory with P
       }
 
       (fakeAnomalyDetector.detect _ when(data.map(_.metricValue.get).toVector, (0, 2)))
-        .returns (Seq((0, Anomaly(Option(5.0), 1.0)), (1, Anomaly(Option(5.0), 1.0))))
+        .returns (
+          Seq(
+            (0, AnomalyDetectionDataPoint(5.0, 5.0, confidence = 1.0)),
+            (1, AnomalyDetectionDataPoint(5.0, 5.0, confidence = 1.0))
+          )
+        )
 
       val anomalyResult = aD.detectAnomaliesInHistory(data, (200L, 401L))
 
-      assert(anomalyResult == DetectionResult(Seq((200L, Anomaly(Option(5.0), 1.0)),
-        (400L, Anomaly(Option(5.0), 1.0)))))
+      assert(anomalyResult == AnomalyDetectionResult(Seq(
+            (200L, AnomalyDetectionDataPoint(5.0, 5.0, confidence = 1.0)),
+            (400L, AnomalyDetectionDataPoint(5.0, 5.0, confidence = 1.0)))))
     }
 
     "treat unordered values with time gaps correctly" in {
@@ -80,13 +86,20 @@ class AnomalyDetectorTest extends WordSpec with Matchers with MockFactory with P
       val tS = AnomalyDetector(SimpleThresholdStrategy(lowerBound = -0.5, upperBound = 1.0))
 
       (fakeAnomalyDetector.detect _ when(Vector(0.5, -1.0, 3.0, 2.0), (0, 4)))
-        .returns(Seq((1, Anomaly(Option(-1.0), 1.0)), (2, Anomaly(Option(3.0), 1.0)),
-          (3, Anomaly(Option(2.0), 1.0))))
+        .returns(
+          Seq(
+            (1, AnomalyDetectionDataPoint(-1.0, -1.0, confidence = 1.0)),
+            (2, AnomalyDetectionDataPoint(3.0, 3.0, confidence = 1.0)),
+            (3, AnomalyDetectionDataPoint(2.0, 2.0, confidence = 1.0))
+          )
+        )
 
       val anomalyResult = aD.detectAnomaliesInHistory(data)
 
-      assert(anomalyResult == DetectionResult(Seq((10L, Anomaly(Option(-1.0), 1.0)),
-        (11L, Anomaly(Option(3.0), 1.0)), (25L, Anomaly(Option(2.0), 1.0)))))
+      assert(anomalyResult == AnomalyDetectionResult(
+        Seq((10L, AnomalyDetectionDataPoint(-1.0, -1.0, confidence = 1.0)),
+          (11L, AnomalyDetectionDataPoint(3.0, 3.0, confidence = 1.0)),
+          (25L, AnomalyDetectionDataPoint(2.0, 2.0, confidence = 1.0)))))
     }
 
     "treat unordered values without time gaps correctly" in {
@@ -95,13 +108,16 @@ class AnomalyDetectorTest extends WordSpec with Matchers with MockFactory with P
       }
 
       (fakeAnomalyDetector.detect _ when(Vector(0.5, -1.0, 3.0, 2.0), (0, 4)))
-        .returns(Seq((1, Anomaly(Option(-1.0), 1.0)), (2, Anomaly(Option(3.0), 1.0)),
-          (3, Anomaly(Option(2.0), 1.0))))
+        .returns(Seq((1, AnomalyDetectionDataPoint(-1.0, -1.0, confidence = 1.0)),
+          (2, AnomalyDetectionDataPoint(3.0, 3.0, confidence = 1.0)),
+          (3, AnomalyDetectionDataPoint(2.0, 2.0, confidence = 1.0))))
 
       val anomalyResult = aD.detectAnomaliesInHistory(data)
 
-      assert(anomalyResult == DetectionResult(Seq((1L, Anomaly(Option(-1.0), 1.0)),
-        (2L, Anomaly(Option(3.0), 1.0)), (3L, Anomaly(Option(2.0), 1.0)))))
+      assert(anomalyResult == AnomalyDetectionResult(Seq(
+        (1L, AnomalyDetectionDataPoint(-1.0, -1.0, confidence = 1.0)),
+        (2L, AnomalyDetectionDataPoint(3.0, 3.0, confidence = 1.0)),
+        (3L, AnomalyDetectionDataPoint(2.0, 2.0, confidence = 1.0)))))
     }
 
   }
diff --git a/src/test/scala/com/amazon/deequ/anomalydetection/BatchNormalStrategyTest.scala b/src/test/scala/com/amazon/deequ/anomalydetection/BatchNormalStrategyTest.scala
index 05b9a6272..8fdf6e74e 100644
--- a/src/test/scala/com/amazon/deequ/anomalydetection/BatchNormalStrategyTest.scala
+++ b/src/test/scala/com/amazon/deequ/anomalydetection/BatchNormalStrategyTest.scala
@@ -39,57 +39,77 @@ class BatchNormalStrategyTest extends WordSpec with Matchers {
     val data = dist.toVector
 
     "only detect anomalies in interval" in {
-      val anomalyResult = strategy.detect(data, (25, 50))
-      val expected = for (i <- 25 to 30) yield {
-        (i, Anomaly(Option(data(i)), 1.0))
-      }
-      assert(anomalyResult == expected)
+      val anomalyResult = strategy.detect(data, (25, 50)).filter({case (_, anom) => anom.isAnomaly})
+
+      val expectedAnomalyThreshold = AnomalyThreshold(Bound(-9.280850004177061), Bound(10.639954755150061))
+      val expectedResult: Seq[(Int, AnomalyDetectionDataPoint)] = Seq(
+        (25, AnomalyDetectionDataPoint(data(25), data(25), expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (26, AnomalyDetectionDataPoint(data(26), data(26), expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (27, AnomalyDetectionDataPoint(data(27), data(27), expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (28, AnomalyDetectionDataPoint(data(28), data(28), expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (29, AnomalyDetectionDataPoint(data(29), data(29), expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (30, AnomalyDetectionDataPoint(data(30), data(30), expectedAnomalyThreshold, isAnomaly = true, 1.0))
+      )
+      assert(anomalyResult == expectedResult)
     }
 
     "ignore lower factor if none is given" in {
       val strategy = BatchNormalStrategy(None, Some(1.0))
-      val anomalyResult = strategy.detect(data, (20, 31))
+      val anomalyResult = strategy.detect(data, (20, 31)).filter({case (_, anom) => anom.isAnomaly})
 
+      val expectedAnomalyThreshold = AnomalyThreshold(Bound(Double.NegativeInfinity), Bound(0.7781496015857838))
       // Anomalies with positive values only
-      val expected = for (i <- 20 to 30 by 2) yield {
-        (i, Anomaly(Option(data(i)), 1.0))
-      }
-      assert(anomalyResult == expected)
+      val expectedResult: Seq[(Int, AnomalyDetectionDataPoint)] = Seq(
+        (20, AnomalyDetectionDataPoint(data(20), data(20), expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (22, AnomalyDetectionDataPoint(data(22), data(22), expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (24, AnomalyDetectionDataPoint(data(24), data(24), expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (26, AnomalyDetectionDataPoint(data(26), data(26), expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (28, AnomalyDetectionDataPoint(data(28), data(28), expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (30, AnomalyDetectionDataPoint(data(30), data(30), expectedAnomalyThreshold, isAnomaly = true, 1.0))
+      )
+      assert(anomalyResult == expectedResult)
     }
 
     "ignore upper factor if none is given" in {
       val strategy = BatchNormalStrategy(Some(1.0), None)
-      val anomalyResult = strategy.detect(data, (10, 30))
+      val anomalyResult = strategy.detect(data, (10, 30)).filter({case (_, anom) => anom.isAnomaly})
+      val expectedAnomalyThreshold = AnomalyThreshold(Bound(-5.063730045618394), Bound(Double.PositiveInfinity))
 
       // Anomalies with negative values only
-      val expected = for (i <- 21 to 29 by 2) yield {
-        (i, Anomaly(Option(data(i)), 1.0))
-      }
-      assert(anomalyResult == expected)
+      val expectedResult: Seq[(Int, AnomalyDetectionDataPoint)] = Seq(
+        (21, AnomalyDetectionDataPoint(data(21), data(21), expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (23, AnomalyDetectionDataPoint(data(23), data(23), expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (25, AnomalyDetectionDataPoint(data(25), data(25), expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (27, AnomalyDetectionDataPoint(data(27), data(27), expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (29, AnomalyDetectionDataPoint(data(29), data(29), expectedAnomalyThreshold, isAnomaly = true, 1.0))
+      )
+      assert(anomalyResult == expectedResult)
     }
 
     "ignore values in interval for mean/ stdDev if specified" in {
       val data = Vector(1.0, 1.0, 1.0, 1000.0, 500.0, 1.0)
       val strategy = BatchNormalStrategy(Some(3.0), Some(3.0))
-      val anomalyResult = strategy.detect(data, (3, 5))
-      val expected = Seq((3, Anomaly(Option(data(3)), 1.0)), (4, Anomaly(Option(data(4)), 1.0)))
+      val anomalyResult = strategy.detect(data, (3, 5)).filter({case (_, anom) => anom.isAnomaly})
 
-      assert(anomalyResult == expected)
+      val expectedResult: Seq[(Int, AnomalyDetectionDataPoint)] = Seq(
+        (3, AnomalyDetectionDataPoint(1000, 1000, AnomalyThreshold(Bound(1.0), Bound(1.0)), isAnomaly = true, 1.0)),
+        (4, AnomalyDetectionDataPoint(500, 500, AnomalyThreshold(Bound(1.0), Bound(1.0)), isAnomaly = true, 1.0))
+      )
+      assert(anomalyResult == expectedResult)
     }
 
     "throw an exception when trying to exclude all data points from calculation" in {
       val strategy = BatchNormalStrategy()
       intercept[IllegalArgumentException] {
-        strategy.detect(data)
+        strategy.detect(data).filter({case (_, anom) => anom.isAnomaly})
       }
     }
 
-
     "detect no anomalies if factors are set to max value" in {
       val strategy = BatchNormalStrategy(Some(Double.MaxValue), Some(Double.MaxValue))
-      val anomalyResult = strategy.detect(data, (30, 51))
+      val anomalyResult = strategy.detect(data, (30, 51)).filter({case (_, anom) => anom.isAnomaly})
 
-      val expected: List[(Int, Anomaly)] = List()
+      val expected: List[(Int, AnomalyDetectionDataPoint)] = List()
       assert(anomalyResult == expected)
     }
 
@@ -109,13 +129,25 @@ class BatchNormalStrategyTest extends WordSpec with Matchers {
     }
 
     "produce error message with correct value and bounds" in {
-      val result = strategy.detect(data, (25, 50))
+      val result = strategy.detect(data, (25, 50)).filter({case (_, anom) => anom.isAnomaly})
 
       result.foreach { case (_, anom) =>
         val (value, lowerBound, upperBound) =
           AnomalyDetectionTestUtils.firstThreeDoublesFromString(anom.detail.get)
 
-        assert(anom.value.isDefined && value === anom.value.get)
+        assert(value === anom.anomalyMetricValue)
+        assert(value < lowerBound || value > upperBound)
+      }
+    }
+
+    "assert anomalies are outside of anomaly bounds" in {
+      val result = strategy.detect(data, (25, 50)).filter({ case (_, anom) => anom.isAnomaly })
+
+      result.foreach { case (_, anom) =>
+        val value = anom.anomalyMetricValue
+        val upperBound = anom.anomalyThreshold.upperBound.value
+        val lowerBound = anom.anomalyThreshold.lowerBound.value
+
         assert(value < lowerBound || value > upperBound)
       }
     }
diff --git a/src/test/scala/com/amazon/deequ/anomalydetection/OnlineNormalStrategyTest.scala b/src/test/scala/com/amazon/deequ/anomalydetection/OnlineNormalStrategyTest.scala
index 781ffb7ad..d35439585 100644
--- a/src/test/scala/com/amazon/deequ/anomalydetection/OnlineNormalStrategyTest.scala
+++ b/src/test/scala/com/amazon/deequ/anomalydetection/OnlineNormalStrategyTest.scala
@@ -42,57 +42,111 @@ class OnlineNormalStrategyTest extends WordSpec with Matchers {
     "detect all anomalies if no interval specified" in {
       val strategy = OnlineNormalStrategy(lowerDeviationFactor = Some(3.5),
         upperDeviationFactor = Some(3.5), ignoreStartPercentage = 0.2)
-      val anomalyResult = strategy.detect(data)
-      val expected = for (i <- 20 to 30) yield {
-        (i, Anomaly(Option(data(i)), 1.0))
-      }
-      assert(anomalyResult == expected)
+      val anomalyResult = strategy.detect(data).filter({case (_, anom) => anom.isAnomaly})
+
+      val expectedResult: Seq[(Int, AnomalyDetectionDataPoint)] = Seq(
+        (20, AnomalyDetectionDataPoint(data(20), data(20),
+          AnomalyThreshold(Bound(-14.868489924421404), Bound(14.255383455388895)), isAnomaly = true, 1.0)),
+        (21, AnomalyDetectionDataPoint(data(21), data(21),
+          AnomalyThreshold(Bound(-13.6338479733374), Bound(13.02074150430489)), isAnomaly = true, 1.0)),
+        (22, AnomalyDetectionDataPoint(data(22), data(22),
+          AnomalyThreshold(Bound(-16.71733585267535), Bound(16.104229383642842)), isAnomaly = true, 1.0)),
+        (23, AnomalyDetectionDataPoint(data(23), data(23),
+          AnomalyThreshold(Bound(-17.346915620547467), Bound(16.733809151514958)), isAnomaly = true, 1.0)),
+        (24, AnomalyDetectionDataPoint(data(24), data(24),
+          AnomalyThreshold(Bound(-17.496117397890874), Bound(16.883010928858365)), isAnomaly = true, 1.0)),
+        (25, AnomalyDetectionDataPoint(data(25), data(25),
+          AnomalyThreshold(Bound(-17.90391150851199), Bound(17.29080503947948)), isAnomaly = true, 1.0)),
+        (26, AnomalyDetectionDataPoint(data(26), data(26),
+          AnomalyThreshold(Bound(-17.028892797350824), Bound(16.415786328318315)), isAnomaly = true, 1.0)),
+        (27, AnomalyDetectionDataPoint(data(27), data(27),
+          AnomalyThreshold(Bound(-17.720100310354653), Bound(17.106993841322144)), isAnomaly = true, 1.0)),
+        (28, AnomalyDetectionDataPoint(data(28), data(28),
+          AnomalyThreshold(Bound(-18.23663168508628), Bound(17.62352521605377)), isAnomaly = true, 1.0)),
+        (29, AnomalyDetectionDataPoint(data(29), data(29),
+          AnomalyThreshold(Bound(-19.32641622778204), Bound(18.71330975874953)), isAnomaly = true, 1.0)),
+        (30, AnomalyDetectionDataPoint(data(30), data(30),
+          AnomalyThreshold(Bound(-18.96540323993527), Bound(18.35229677090276)), isAnomaly = true, 1.0))
+      )
+      assert(anomalyResult == expectedResult)
     }
 
     "only detect anomalies in interval" in {
-      val anomalyResult = strategy.detect(data, (25, 31))
-      val expected = for (i <- 25 to 30) yield {
-        (i, Anomaly(Option(data(i)), 1.0))
-      }
-      assert(anomalyResult == expected)
+      val anomalyResult = strategy.detect(data, (25, 31)).filter({case (_, anom) => anom.isAnomaly})
+
+      val expectedResult: Seq[(Int, AnomalyDetectionDataPoint)] = Seq(
+        (25, AnomalyDetectionDataPoint(data(25), data(25),
+          AnomalyThreshold(Bound(-15.630116599125694), Bound(16.989221350098695)), isAnomaly = true, 1.0)),
+        (26, AnomalyDetectionDataPoint(data(26), data(26),
+          AnomalyThreshold(Bound(-14.963376676338362), Bound(16.322481427311363)), isAnomaly = true, 1.0)),
+        (27, AnomalyDetectionDataPoint(data(27), data(27),
+          AnomalyThreshold(Bound(-15.131834814393196), Bound(16.490939565366197)), isAnomaly = true, 1.0)),
+        (28, AnomalyDetectionDataPoint(data(28), data(28),
+          AnomalyThreshold(Bound(-14.76810451038132), Bound(16.12720926135432)), isAnomaly = true, 1.0)),
+        (29, AnomalyDetectionDataPoint(data(29), data(29),
+          AnomalyThreshold(Bound(-15.078145049879462), Bound(16.437249800852463)), isAnomaly = true, 1.0)),
+        (30, AnomalyDetectionDataPoint(data(30), data(30),
+          AnomalyThreshold(Bound(-14.540171084298914), Bound(15.899275835271913)), isAnomaly = true, 1.0))
+      )
+      assert(anomalyResult == expectedResult)
     }
 
     "ignore lower factor if none is given" in {
       val strategy = OnlineNormalStrategy(lowerDeviationFactor = None,
         upperDeviationFactor = Some(1.5))
-      val anomalyResult = strategy.detect(data)
+      val anomalyResult = strategy.detect(data).filter({case (_, anom) => anom.isAnomaly})
 
       // Anomalies with positive values only
-      val expected = for (i <- 20 to 30 by 2) yield {
-        (i, Anomaly(Option(data(i)), 1.0))
-      }
-      assert(anomalyResult == expected)
+      val expectedResult: Seq[(Int, AnomalyDetectionDataPoint)] = Seq(
+        (20, AnomalyDetectionDataPoint(data(20), data(20),
+          AnomalyThreshold(Bound(Double.NegativeInfinity), Bound(5.934276775443095)), isAnomaly = true, 1.0)),
+        (22, AnomalyDetectionDataPoint(data(22), data(22),
+          AnomalyThreshold(Bound(Double.NegativeInfinity), Bound(7.979098353666404)), isAnomaly = true, 1.0)),
+        (24, AnomalyDetectionDataPoint(data(24), data(24),
+          AnomalyThreshold(Bound(Double.NegativeInfinity), Bound(9.582136909647211)), isAnomaly = true, 1.0)),
+        (26, AnomalyDetectionDataPoint(data(26), data(26),
+          AnomalyThreshold(Bound(Double.NegativeInfinity), Bound(10.320400087389258)), isAnomaly = true, 1.0)),
+        (28, AnomalyDetectionDataPoint(data(28), data(28),
+          AnomalyThreshold(Bound(Double.NegativeInfinity), Bound(11.113502213504855)), isAnomaly = true, 1.0)),
+        (30, AnomalyDetectionDataPoint(data(30), data(30),
+          AnomalyThreshold(Bound(Double.NegativeInfinity), Bound(11.776810456746686)), isAnomaly = true, 1.0))
+      )
+      assert(anomalyResult == expectedResult)
     }
 
     "ignore upper factor if none is given" in {
       val strategy = OnlineNormalStrategy(lowerDeviationFactor = Some(1.5),
         upperDeviationFactor = None)
-      val anomalyResult = strategy.detect(data)
+      val anomalyResult = strategy.detect(data).filter({case (_, anom) => anom.isAnomaly})
 
       // Anomalies with negative values only
-      val expected = for (i <- 21 to 29 by 2) yield {
-        (i, Anomaly(Option(data(i)), 1.0))
-      }
-      assert(anomalyResult == expected)
+      val expectedResult: Seq[(Int, AnomalyDetectionDataPoint)] = Seq(
+        (21, AnomalyDetectionDataPoint(data(21), data(21),
+          AnomalyThreshold(Bound(-7.855820681098751), Bound(Double.PositiveInfinity)), isAnomaly = true, 1.0)),
+        (23, AnomalyDetectionDataPoint(data(23), data(23),
+          AnomalyThreshold(Bound(-10.14631437278386), Bound(Double.PositiveInfinity)), isAnomaly = true, 1.0)),
+        (25, AnomalyDetectionDataPoint(data(25), data(25),
+          AnomalyThreshold(Bound(-11.038751996286909), Bound(Double.PositiveInfinity)), isAnomaly = true, 1.0)),
+        (27, AnomalyDetectionDataPoint(data(27), data(27),
+          AnomalyThreshold(Bound(-11.359107787232386), Bound(Double.PositiveInfinity)), isAnomaly = true, 1.0)),
+        (29, AnomalyDetectionDataPoint(data(29), data(29),
+          AnomalyThreshold(Bound(-12.097995027317015), Bound(Double.PositiveInfinity)), isAnomaly = true, 1.0))
+      )
+      assert(anomalyResult == expectedResult)
     }
 
     "work fine with empty input" in {
       val emptySeries = Vector[Double]()
-      val anomalyResult = strategy.detect(emptySeries)
+      val anomalyResult = strategy.detect(emptySeries).filter({case (_, anom) => anom.isAnomaly})
 
-      assert(anomalyResult == Seq[(Int, Anomaly)]())
+      assert(anomalyResult == Seq[(Int, AnomalyDetectionDataPoint)]())
     }
 
     "detect no anomalies if factors are set to max value" in {
       val strategy = OnlineNormalStrategy(Some(Double.MaxValue), Some(Double.MaxValue))
-      val anomalyResult = strategy.detect(data)
+      val anomalyResult = strategy.detect(data).filter({case (_, anom) => anom.isAnomaly})
 
-      val expected: List[(Int, Anomaly)] = List()
+      val expected: List[(Int, AnomalyDetectionDataPoint)] = List()
       assert(anomalyResult == expected)
     }
 
@@ -157,13 +211,25 @@ class OnlineNormalStrategyTest extends WordSpec with Matchers {
     }
 
     "produce error message with correct value and bounds" in {
-      val result = strategy.detect(data)
+      val result = strategy.detect(data).filter({case (_, anom) => anom.isAnomaly})
 
       result.foreach { case (_, anom) =>
         val (value, lowerBound, upperBound) =
           AnomalyDetectionTestUtils.firstThreeDoublesFromString(anom.detail.get)
 
-        assert(anom.value.isDefined && value === anom.value.get)
+        assert(value === anom.anomalyMetricValue)
+        assert(value < lowerBound || value > upperBound)
+      }
+    }
+
+    "assert anomalies are outside of anomaly bounds" in {
+      val result = strategy.detect(data).filter({ case (_, anom) => anom.isAnomaly })
+
+      result.foreach { case (_, anom) =>
+        val value = anom.anomalyMetricValue
+        val upperBound = anom.anomalyThreshold.upperBound.value
+        val lowerBound = anom.anomalyThreshold.lowerBound.value
+
         assert(value < lowerBound || value > upperBound)
       }
     }
diff --git a/src/test/scala/com/amazon/deequ/anomalydetection/RateOfChangeStrategyTest.scala b/src/test/scala/com/amazon/deequ/anomalydetection/RateOfChangeStrategyTest.scala
index 70f66f033..ddcaf013e 100644
--- a/src/test/scala/com/amazon/deequ/anomalydetection/RateOfChangeStrategyTest.scala
+++ b/src/test/scala/com/amazon/deequ/anomalydetection/RateOfChangeStrategyTest.scala
@@ -36,11 +36,24 @@ class RateOfChangeStrategyTest extends WordSpec with Matchers {
     }).toVector
 
     "detect all anomalies if no interval specified" in {
-      val anomalyResult = strategy.detect(data)
-      val expected = for (i <- 20 to 31) yield {
-        (i, Anomaly(Option(data(i)), 1.0))
-      }
-      assert(anomalyResult == expected)
+      val anomalyResult = strategy.detect(data).filter({case (_, anom) => anom.isAnomaly})
+
+      val expectedAnomalyThreshold = AnomalyThreshold(Bound(-2.0), Bound(2.0))
+      val expectedResult: Seq[(Int, AnomalyDetectionDataPoint)] = Seq(
+        (20, AnomalyDetectionDataPoint(20, 19, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (21, AnomalyDetectionDataPoint(-21, -41, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (22, AnomalyDetectionDataPoint(22, 43, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (23, AnomalyDetectionDataPoint(-23, -45, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (24, AnomalyDetectionDataPoint(24, 47, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (25, AnomalyDetectionDataPoint(-25, -49, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (26, AnomalyDetectionDataPoint(26, 51, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (27, AnomalyDetectionDataPoint(-27, -53, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (28, AnomalyDetectionDataPoint(28, 55, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (29, AnomalyDetectionDataPoint(-29, -57, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (30, AnomalyDetectionDataPoint(30, 59, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (31, AnomalyDetectionDataPoint(1, -29, expectedAnomalyThreshold, isAnomaly = true, 1.0))
+      )
+      assert(anomalyResult == expectedResult)
     }
   }
 }
diff --git a/src/test/scala/com/amazon/deequ/anomalydetection/RelativeRateOfChangeStrategyTest.scala b/src/test/scala/com/amazon/deequ/anomalydetection/RelativeRateOfChangeStrategyTest.scala
index bfde6ba18..10cf917ba 100644
--- a/src/test/scala/com/amazon/deequ/anomalydetection/RelativeRateOfChangeStrategyTest.scala
+++ b/src/test/scala/com/amazon/deequ/anomalydetection/RelativeRateOfChangeStrategyTest.scala
@@ -33,48 +33,81 @@ class RelativeRateOfChangeStrategyTest extends WordSpec with Matchers {
     }).toVector
 
     "detect all anomalies if no interval specified" in {
-      val anomalyResult = strategy.detect(data)
-      val expected = for (i <- 20 to 31) yield {
-        (i, Anomaly(Option(data(i)), 1.0))
-      }
-      assert(anomalyResult == expected)
+      val anomalyResult = strategy.detect(data).filter({case (_, anom) => anom.isAnomaly})
+
+      val expectedAnomalyThreshold = AnomalyThreshold(Bound(0.5), Bound(2.0))
+      val expectedResult: Seq[(Int, AnomalyDetectionDataPoint)] = Seq(
+        (20, AnomalyDetectionDataPoint(20, 20, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (21, AnomalyDetectionDataPoint(1, 0.05, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (22, AnomalyDetectionDataPoint(22, 22, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (23, AnomalyDetectionDataPoint(1, 0.045454545454545456, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (24, AnomalyDetectionDataPoint(24, 24, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (25, AnomalyDetectionDataPoint(1, 0.041666666666666664, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (26, AnomalyDetectionDataPoint(26, 26, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (27, AnomalyDetectionDataPoint(1, 0.038461538461538464, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (28, AnomalyDetectionDataPoint(28, 28, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (29, AnomalyDetectionDataPoint(1, 0.03571428571428571, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (30, AnomalyDetectionDataPoint(30, 30, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (31, AnomalyDetectionDataPoint(1, 0.03333333333333333, expectedAnomalyThreshold, isAnomaly = true, 1.0))
+      )
+      assert(anomalyResult == expectedResult)
     }
 
     "only detect anomalies in interval" in {
-      val anomalyResult = strategy.detect(data, (25, 50))
-      val expected = for (i <- 25 to 31) yield {
-        (i, Anomaly(Option(data(i)), 1.0))
-      }
-      assert(anomalyResult == expected)
+      val anomalyResult = strategy.detect(data, (25, 50)).filter({case (_, anom) => anom.isAnomaly})
+
+      val expectedAnomalyThreshold = AnomalyThreshold(Bound(0.5), Bound(2.0))
+      val expectedResult: Seq[(Int, AnomalyDetectionDataPoint)] = Seq(
+        (25, AnomalyDetectionDataPoint(1, 0.041666666666666664, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (26, AnomalyDetectionDataPoint(26, 26, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (27, AnomalyDetectionDataPoint(1, 0.038461538461538464, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (28, AnomalyDetectionDataPoint(28, 28, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (29, AnomalyDetectionDataPoint(1, 0.03571428571428571, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (30, AnomalyDetectionDataPoint(30, 30, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (31, AnomalyDetectionDataPoint(1, 0.03333333333333333, expectedAnomalyThreshold, isAnomaly = true, 1.0))
+      )
+      assert(anomalyResult == expectedResult)
     }
 
     "ignore min rate if none is given" in {
       val strategy = RelativeRateOfChangeStrategy(None, Some(1.0))
-      val anomalyResult = strategy.detect(data)
+      val anomalyResult = strategy.detect(data).filter({case (_, anom) => anom.isAnomaly})
 
       // Anomalies with positive values only
-      val expected = for (i <- 20 to 30 by 2) yield {
-        (i, Anomaly(Option(data(i)), 1.0))
-      }
-      assert(anomalyResult == expected)
+      val expectedAnomalyThreshold = AnomalyThreshold(Bound(-1.7976931348623157E308), Bound(1.0))
+      val expectedResult: Seq[(Int, AnomalyDetectionDataPoint)] = Seq(
+        (20, AnomalyDetectionDataPoint(20, 20, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (22, AnomalyDetectionDataPoint(22, 22, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (24, AnomalyDetectionDataPoint(24, 24, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (26, AnomalyDetectionDataPoint(26, 26, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (28, AnomalyDetectionDataPoint(28, 28, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (30, AnomalyDetectionDataPoint(30, 30, expectedAnomalyThreshold, isAnomaly = true, 1.0))
+      )
+      assert(anomalyResult == expectedResult)
     }
 
     "ignore max rate if none is given" in {
       val strategy = RelativeRateOfChangeStrategy(Some(0.5), None)
-      val anomalyResult = strategy.detect(data)
+      val anomalyResult = strategy.detect(data).filter({case (_, anom) => anom.isAnomaly})
 
       // Anomalies with negative values only
-      val expected = for (i <- 21 to 31 by 2) yield {
-        (i, Anomaly(Option(data(i)), 1.0))
-      }
-      assert(anomalyResult == expected)
+      val expectedAnomalyThreshold = AnomalyThreshold(Bound(0.5), Bound(1.7976931348623157E308))
+      val expectedResult: Seq[(Int, AnomalyDetectionDataPoint)] = Seq(
+        (21, AnomalyDetectionDataPoint(1, 0.05, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (23, AnomalyDetectionDataPoint(1, 0.045454545454545456, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (25, AnomalyDetectionDataPoint(1, 0.041666666666666664, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (27, AnomalyDetectionDataPoint(1, 0.038461538461538464, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (29, AnomalyDetectionDataPoint(1, 0.03571428571428571, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (31, AnomalyDetectionDataPoint(1, 0.03333333333333333, expectedAnomalyThreshold, isAnomaly = true, 1.0))
+      )
+      assert(anomalyResult == expectedResult)
     }
 
     "detect no anomalies if rates are set to min/ max value" in {
       val strategy = RelativeRateOfChangeStrategy(Some(Double.MinValue), Some(Double.MaxValue))
-      val anomalyResult = strategy.detect(data)
+      val anomalyResult = strategy.detect(data).filter({case (_, anom) => anom.isAnomaly})
 
-      val expected: List[(Int, Anomaly)] = List()
+      val expected: List[(Int, AnomalyDetectionDataPoint)] = List()
       assert(anomalyResult == expected)
     }
 
@@ -104,19 +137,27 @@ class RelativeRateOfChangeStrategyTest extends WordSpec with Matchers {
     "attribute indices correctly for higher orders without search interval" in {
       val data = Vector(0.0, 1.0, 3.0, 6.0, 18.0, 72.0)
       val strategy = RelativeRateOfChangeStrategy(None, Some(8.0), order = 2)
-      val result = strategy.detect(data)
-
-      val expected = Seq((2, Anomaly(Option(3.0), 1.0)), (5, Anomaly(Option(72.0), 1.0)))
-      assert(result == expected)
+      val anomalyResult = strategy.detect(data).filter({case (_, anom) => anom.isAnomaly})
+
+      val expectedResult: Seq[(Int, AnomalyDetectionDataPoint)] = Seq(
+        (2, AnomalyDetectionDataPoint(3, Double.PositiveInfinity,
+          AnomalyThreshold(Bound(-1.7976931348623157E308), Bound(8.0)), isAnomaly = true, 1.0)),
+        (5, AnomalyDetectionDataPoint(72, 12,
+          AnomalyThreshold(Bound(-1.7976931348623157E308), Bound(8.0)), isAnomaly = true, 1.0))
+      )
+      assert(anomalyResult == expectedResult)
     }
 
     "attribute indices correctly for higher orders with search interval" in {
       val data = Vector(0.0, 1.0, 3.0, 6.0, 18.0, 72.0)
       val strategy = RelativeRateOfChangeStrategy(None, Some(8.0), order = 2)
-      val result = strategy.detect(data, (5, 6))
+      val anomalyResult = strategy.detect(data, (5, 6)).filter({case (_, anom) => anom.isAnomaly})
 
-      val expected = Seq((5, Anomaly(Option(72.0), 1.0)))
-      assert(result == expected)
+      val expectedResult: Seq[(Int, AnomalyDetectionDataPoint)] = Seq(
+        (5, AnomalyDetectionDataPoint(72, 12,
+          AnomalyThreshold(Bound(-1.7976931348623157E308), Bound(8.0)), isAnomaly = true, 1.0))
+      )
+      assert(anomalyResult == expectedResult)
     }
 
 
@@ -134,19 +175,32 @@ class RelativeRateOfChangeStrategyTest extends WordSpec with Matchers {
 
     "work fine with empty input" in {
       val emptySeries = Vector[Double]()
-      val anomalyResult = strategy.detect(emptySeries)
+      val anomalyResult = strategy.detect(emptySeries).filter({case (_, anom) => anom.isAnomaly})
 
-      assert(anomalyResult == Seq[(Int, Anomaly)]())
+      assert(anomalyResult == Seq[(Int, AnomalyDetectionDataPoint)]())
     }
 
     "produce error message with correct value and bounds" in {
-      val result = strategy.detect(data)
+      val result = strategy.detect(data).filter({case (_, anom) => anom.isAnomaly})
 
       result.foreach { case (_, anom) =>
         val (value, lowerBound, upperBound) =
           AnomalyDetectionTestUtils.firstThreeDoublesFromString(anom.detail.get)
 
-        assert(value -1 < lowerBound || value -1 > upperBound)
+        assert(value === anom.anomalyMetricValue)
+        assert(value < lowerBound || value > upperBound)
+      }
+    }
+
+    "assert anomalies are outside of anomaly bounds" in {
+      val result = strategy.detect(data).filter({ case (_, anom) => anom.isAnomaly })
+
+      result.foreach { case (_, anom) =>
+        val value = anom.anomalyMetricValue
+        val upperBound = anom.anomalyThreshold.upperBound.value
+        val lowerBound = anom.anomalyThreshold.lowerBound.value
+
+        assert(value < lowerBound || value > upperBound)
       }
     }
   }
diff --git a/src/test/scala/com/amazon/deequ/anomalydetection/SimpleThresholdStrategyTest.scala b/src/test/scala/com/amazon/deequ/anomalydetection/SimpleThresholdStrategyTest.scala
index 92ead9e48..d05c0057d 100644
--- a/src/test/scala/com/amazon/deequ/anomalydetection/SimpleThresholdStrategyTest.scala
+++ b/src/test/scala/com/amazon/deequ/anomalydetection/SimpleThresholdStrategyTest.scala
@@ -24,33 +24,40 @@ class SimpleThresholdStrategyTest extends WordSpec with Matchers {
 
     val strategy = SimpleThresholdStrategy(upperBound = 1.0)
     val data = Vector(-1.0, 2.0, 3.0, 0.5)
-    val expected = Seq((1, Anomaly(Option(2.0), 1.0)), (2, Anomaly(Option(3.0), 1.0)))
+    val expectedAnomalyThreshold = AnomalyThreshold(upperBound = Bound(1.0))
+    val expectedResult = Seq(
+      (1, AnomalyDetectionDataPoint(2.0, 2.0, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+      (2, AnomalyDetectionDataPoint(3.0, 3.0, expectedAnomalyThreshold, isAnomaly = true, 1.0)))
 
     "detect values above threshold" in {
-      val anomalyResult = strategy.detect(data, (0, 4))
+      val anomalyResult = strategy.detect(data, (0, 4)).filter({case (_, anom) => anom.isAnomaly})
 
-      assert(anomalyResult == expected)
+      assert(anomalyResult == expectedResult)
     }
 
     "detect all values without range specified" in {
-      val anomalyResult = strategy.detect(data)
+      val anomalyResult = strategy.detect(data).filter({case (_, anom) => anom.isAnomaly})
 
-      assert(anomalyResult == expected)
+      assert(anomalyResult == expectedResult)
     }
 
     "work fine with empty input" in {
       val emptySeries = Vector[Double]()
-      val anomalyResult = strategy.detect(emptySeries)
+      val anomalyResult = strategy.detect(emptySeries).filter({case (_, anom) => anom.isAnomaly})
 
-      assert(anomalyResult == Seq[(Int, Anomaly)]())
+      assert(anomalyResult == Seq[(Int, AnomalyDetectionDataPoint)]())
     }
 
     "work with upper and lower threshold" in {
       val tS = SimpleThresholdStrategy(lowerBound = -0.5, upperBound = 1.0)
-      val anomalyResult = tS.detect(data)
-
-      assert(anomalyResult == Seq((0, Anomaly(Option(-1.0), 1.0)),
-        (1, Anomaly(Option(2.0), 1.0)), (2, Anomaly(Option(3.0), 1.0))))
+      val anomalyResult = tS.detect(data).filter({case (_, anom) => anom.isAnomaly})
+      val expectedAnomalyThreshold = AnomalyThreshold(Bound(-0.5), Bound(1.0))
+      val expectedResult = Seq(
+        (0, AnomalyDetectionDataPoint(-1.0, -1.0, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (1, AnomalyDetectionDataPoint(2.0, 2.0, expectedAnomalyThreshold, isAnomaly = true, 1.0)),
+        (2, AnomalyDetectionDataPoint(3.0, 3.0, expectedAnomalyThreshold, isAnomaly = true, 1.0)))
+
+      assert(anomalyResult == expectedResult)
     }
 
     "throw an error when thresholds are not ordered " in {
@@ -60,13 +67,25 @@ class SimpleThresholdStrategyTest extends WordSpec with Matchers {
     }
 
     "produce error message with correct value and bounds" in {
-      val result = strategy.detect(data)
+      val result = strategy.detect(data).filter({case (_, anom) => anom.isAnomaly})
 
       result.foreach { case (_, anom) =>
         val (value, lowerBound, upperBound) =
           AnomalyDetectionTestUtils.firstThreeDoublesFromString(anom.detail.get)
 
-        assert(anom.value.isDefined && value === anom.value.get)
+        assert(value === anom.anomalyMetricValue)
+        assert(value < lowerBound || value > upperBound)
+      }
+    }
+
+    "assert anomalies are outside of anomaly bounds" in {
+      val result = strategy.detect(data).filter({ case (_, anom) => anom.isAnomaly })
+
+      result.foreach { case (_, anom) =>
+        val value = anom.anomalyMetricValue
+        val upperBound = anom.anomalyThreshold.upperBound.value
+        val lowerBound = anom.anomalyThreshold.lowerBound.value
+
         assert(value < lowerBound || value > upperBound)
       }
     }
diff --git a/src/test/scala/com/amazon/deequ/anomalydetection/seasonal/HoltWintersTest.scala b/src/test/scala/com/amazon/deequ/anomalydetection/seasonal/HoltWintersTest.scala
index decf5a91c..fd6cab62f 100644
--- a/src/test/scala/com/amazon/deequ/anomalydetection/seasonal/HoltWintersTest.scala
+++ b/src/test/scala/com/amazon/deequ/anomalydetection/seasonal/HoltWintersTest.scala
@@ -16,7 +16,7 @@
 
 package com.amazon.deequ.anomalydetection.seasonal
 
-import com.amazon.deequ.anomalydetection.Anomaly
+import com.amazon.deequ.anomalydetection.AnomalyDetectionDataPoint
 import org.scalatest.matchers.should.Matchers
 import org.scalatest.wordspec.AnyWordSpec
 
@@ -76,6 +76,7 @@ class HoltWintersTest extends AnyWordSpec with Matchers {
     "predict no anomaly for normally distributed errors" in {
       val seriesWithOutlier = twoWeeksOfData ++ Vector(twoWeeksOfData.head)
       val anomalies = dailyMetricsWithWeeklySeasonalityAnomalies(seriesWithOutlier, 14 -> 15)
+        .filter({case (_, anom) => anom.isAnomaly})
       anomalies shouldBe empty
     }
 
@@ -92,19 +93,21 @@ class HoltWintersTest extends AnyWordSpec with Matchers {
     "predict no anomalies on longer series" in {
       val seriesWithOutlier = twoWeeksOfData ++ twoWeeksOfData
       val anomalies = dailyMetricsWithWeeklySeasonalityAnomalies(
-        seriesWithOutlier, 26 -> Int.MaxValue)
+        seriesWithOutlier, 26 -> Int.MaxValue).filter({case (_, anom) => anom.isAnomaly})
       anomalies shouldBe empty
     }
 
     "detect no anomalies on constant series" in {
       val series = (0 until 21).map(_ => 1.0).toVector
       val anomalies = dailyMetricsWithWeeklySeasonalityAnomalies(series, 14 -> Int.MaxValue)
+        .filter({case (_, anom) => anom.isAnomaly})
       anomalies shouldBe empty
     }
 
     "detect a single anomaly in constant series with a single error" in {
       val series = ((0 until 20).map(_ => 1.0) ++ Seq(0.0)).toVector
       val anomalies = dailyMetricsWithWeeklySeasonalityAnomalies(series, 14 -> Int.MaxValue)
+        .filter({case (_, anom) => anom.isAnomaly})
 
       anomalies should have size 1
       val (detectionIndex, _) = anomalies.head
@@ -114,6 +117,7 @@ class HoltWintersTest extends AnyWordSpec with Matchers {
     "detect no anomalies on exact linear trend series" in {
       val series = (0 until 48).map(_.toDouble).toVector
       val anomalies = dailyMetricsWithWeeklySeasonalityAnomalies(series, 36 -> Int.MaxValue)
+        .filter({case (_, anom) => anom.isAnomaly})
       anomalies shouldBe empty
     }
 
@@ -123,6 +127,7 @@ class HoltWintersTest extends AnyWordSpec with Matchers {
         .zipWithIndex.map { case (s, level) => s + level }.toVector
 
       val anomalies = dailyMetricsWithWeeklySeasonalityAnomalies(series, 36 -> Int.MaxValue)
+        .filter({case (_, anom) => anom.isAnomaly})
       anomalies shouldBe empty
     }
 
@@ -132,6 +137,7 @@ class HoltWintersTest extends AnyWordSpec with Matchers {
       val series = train ++ test
 
       val anomalies = dailyMetricsWithWeeklySeasonalityAnomalies(series, 14 -> 21)
+        .filter({case (_, anom) => anom.isAnomaly})
 
       anomalies should have size 1
       val (detectionIndex, _) = anomalies.head
@@ -170,7 +176,7 @@ class HoltWintersTest extends AnyWordSpec with Matchers {
       val anomalies = strategy.detect(
         monthlyMilkProduction.take(nTotal),
         trainSize -> nTotal
-      )
+      ).filter({case (_, anom) => anom.isAnomaly})
 
       anomalies should have size 7
     }
@@ -202,7 +208,7 @@ class HoltWintersTest extends AnyWordSpec with Matchers {
       val anomalies = strategy.detect(
         monthlyCarSalesQuebec.take(nTotal),
         trainSize -> nTotal
-      )
+      ).filter({case (_, anom) => anom.isAnomaly})
 
       anomalies should have size 3
     }
@@ -213,7 +219,7 @@ object HoltWintersTest {
 
   def dailyMetricsWithWeeklySeasonalityAnomalies(
                                                   series: Vector[Double],
-                                                  interval: (Int, Int)): Seq[(Int, Anomaly)] = {
+                                                  interval: (Int, Int)): Seq[(Int, AnomalyDetectionDataPoint)] = {
 
     val strategy = new HoltWinters(
       HoltWinters.MetricInterval.Daily,
diff --git a/src/test/scala/com/amazon/deequ/checks/ApplicabilityTest.scala b/src/test/scala/com/amazon/deequ/checks/ApplicabilityTest.scala
index 542f40fcf..25ce8bb6b 100644
--- a/src/test/scala/com/amazon/deequ/checks/ApplicabilityTest.scala
+++ b/src/test/scala/com/amazon/deequ/checks/ApplicabilityTest.scala
@@ -18,11 +18,14 @@ package com.amazon.deequ
 package checks
 
 import com.amazon.deequ.analyzers.applicability.Applicability
-import com.amazon.deequ.analyzers.{Completeness, Compliance, Maximum, Minimum}
+import com.amazon.deequ.analyzers.{Completeness, Compliance, Maximum, Minimum, Size}
+import com.amazon.deequ.anomalydetection.AnomalyDetectionStrategy
+import com.amazon.deequ.repository.MetricsRepository
 import org.apache.spark.sql.types._
+import org.scalamock.scalatest.MockFactory
 import org.scalatest.wordspec.AnyWordSpec
 
-class ApplicabilityTest extends AnyWordSpec with SparkContextSpec {
+class ApplicabilityTest extends AnyWordSpec with SparkContextSpec with MockFactory {
 
   private[this] val schema = StructType(Array(
       StructField("stringCol", StringType, nullable = true),
@@ -48,7 +51,7 @@ class ApplicabilityTest extends AnyWordSpec with SparkContextSpec {
 
   "Applicability tests for checks" should {
 
-    "recognize applicable checks as applicable" in withSparkSession { session =>
+    "recognize applicable analysis based checks as applicable" in withSparkSession { session =>
 
       val applicability = new Applicability(session)
 
@@ -66,6 +69,25 @@ class ApplicabilityTest extends AnyWordSpec with SparkContextSpec {
       }
     }
 
+    "recognize applicable anomaly based checks as applicable" in withSparkSession { session =>
+
+        val applicability = new Applicability(session)
+        val fakeAnomalyDetector = mock[AnomalyDetectionStrategy]
+        val repository = mock[MetricsRepository]
+        val validCheck = Check(CheckLevel.Error, "anomaly test")
+          .isNewestPointNonAnomalous(repository, fakeAnomalyDetector, Size(), Map.empty,
+            None, None)
+
+        val resultForValidCheck = applicability.isApplicable(validCheck, schema)
+
+        assert(resultForValidCheck.isApplicable)
+        assert(resultForValidCheck.failures.isEmpty)
+        assert(resultForValidCheck.constraintApplicabilities.size == validCheck.constraints.size)
+        resultForValidCheck.constraintApplicabilities.foreach { case (_, applicable) =>
+          assert(applicable)
+        }
+    }
+
     "detect checks with non existing columns" in withSparkSession { session =>
 
       val applicability = new Applicability(session)
diff --git a/src/test/scala/com/amazon/deequ/checks/CheckTest.scala b/src/test/scala/com/amazon/deequ/checks/CheckTest.scala
index 505e6d137..3fb6783e8 100644
--- a/src/test/scala/com/amazon/deequ/checks/CheckTest.scala
+++ b/src/test/scala/com/amazon/deequ/checks/CheckTest.scala
@@ -19,7 +19,8 @@ package checks
 
 import com.amazon.deequ.analyzers._
 import com.amazon.deequ.analyzers.runners.{AnalysisRunner, AnalyzerContext}
-import com.amazon.deequ.anomalydetection.{Anomaly, AnomalyDetectionStrategy}
+import com.amazon.deequ.anomalydetection.{AnomalyDetectionAssertionResult, AnomalyDetectionDataPoint, AnomalyDetectionResult, AnomalyDetectionStrategy}
+import com.amazon.deequ.checks.Check.getNewestPointAnomalyResults
 import com.amazon.deequ.constraints.{ConstrainableDataTypes, ConstraintStatus}
 import com.amazon.deequ.metrics.{DoubleMetric, Entity}
 import com.amazon.deequ.repository.memory.InMemoryMetricsRepository
@@ -88,7 +89,7 @@ class CheckTest extends AnyWordSpec with Matchers with SparkContextSpec with Fix
         assertEvaluatesTo(check1, context, CheckStatus.Success)
         assertEvaluatesTo(check2, context, CheckStatus.Error)
         assertEvaluatesTo(check3, context, CheckStatus.Warning)
-    }
+      }
 
     "return the correct check status for combined completeness with . in column name" in
       withSparkSession { sparkSession =>
@@ -963,18 +964,39 @@ class CheckTest extends AnyWordSpec with Matchers with SparkContextSpec with Fix
             // Size results
             (fakeAnomalyDetector.detect _)
               .expects(Vector(1.0, 2.0, 3.0, 4.0, 11.0), (4, 5))
-              .returns(Seq())
+              .returns(Seq(
+                (0, AnomalyDetectionDataPoint(1.0, 1.0, confidence = 1.0)),
+                (1, AnomalyDetectionDataPoint(2.0, 2.0, confidence = 1.0)),
+                (2, AnomalyDetectionDataPoint(3.0, 3.0, confidence = 1.0)),
+                (3, AnomalyDetectionDataPoint(4.0, 4.0, confidence = 1.0)),
+                (4, AnomalyDetectionDataPoint(11.0, 11.0, confidence = 1.0))))
               .once()
             (fakeAnomalyDetector.detect _).expects(Vector(1.0, 2.0, 3.0, 4.0, 4.0), (4, 5))
-              .returns(Seq((4, Anomaly(Option(4.0), 1.0))))
+              .returns(Seq(
+                (0, AnomalyDetectionDataPoint(1.0, 1.0, confidence = 1.0)),
+                (1, AnomalyDetectionDataPoint(2.0, 2.0, confidence = 1.0)),
+                (2, AnomalyDetectionDataPoint(3.0, 3.0, confidence = 1.0)),
+                (3, AnomalyDetectionDataPoint(4.0, 4.0, confidence = 1.0)),
+                (4, AnomalyDetectionDataPoint(4.0, 4.0, isAnomaly = true, confidence = 1.0))))
               .once()
             // Distinctness results
             (fakeAnomalyDetector.detect _)
               .expects(Vector(1.0, 2.0, 3.0, 4.0, 1), (4, 5))
-              .returns(Seq())
+              .returns(Seq(
+                (0, AnomalyDetectionDataPoint(1.0, 1.0, confidence = 1.0)),
+                (1, AnomalyDetectionDataPoint(2.0, 2.0, confidence = 1.0)),
+                (2, AnomalyDetectionDataPoint(3.0, 3.0, confidence = 1.0)),
+                (3, AnomalyDetectionDataPoint(4.0, 4.0, confidence = 1.0)),
+                (4, AnomalyDetectionDataPoint(1.0, 1.0, confidence = 1.0))))
               .once()
-            (fakeAnomalyDetector.detect _).expects(Vector(1.0, 2.0, 3.0, 4.0, 1), (4, 5))
-              .returns(Seq((4, Anomaly(Option(4.0), 0))))
+            (fakeAnomalyDetector.detect _)
+              .expects(Vector(1.0, 2.0, 3.0, 4.0, 1), (4, 5))
+              .returns(Seq(
+                (0, AnomalyDetectionDataPoint(1.0, 1.0, confidence = 1.0)),
+                (1, AnomalyDetectionDataPoint(2.0, 2.0, confidence = 1.0)),
+                (2, AnomalyDetectionDataPoint(3.0, 3.0, confidence = 1.0)),
+                (3, AnomalyDetectionDataPoint(4.0, 4.0, confidence = 1.0)),
+                (4, AnomalyDetectionDataPoint(1.0, 1.0, isAnomaly = true, confidence = 1.0))))
               .once()
           }
 
@@ -1014,10 +1036,16 @@ class CheckTest extends AnyWordSpec with Matchers with SparkContextSpec with Fix
             // Size results
             (fakeAnomalyDetector.detect _)
               .expects(Vector(1.0, 2.0, 11.0), (2, 3))
-              .returns(Seq())
+              .returns(Seq(
+                (0, AnomalyDetectionDataPoint(1.0, 1.0, confidence = 1.0)),
+                (1, AnomalyDetectionDataPoint(2.0, 2.0, confidence = 1.0)),
+                (2, AnomalyDetectionDataPoint(11.0, 11.0, confidence = 1.0))))
               .once()
             (fakeAnomalyDetector.detect _).expects(Vector(1.0, 2.0, 4.0), (2, 3))
-              .returns(Seq((4, Anomaly(Option(4.0), 1.0))))
+              .returns(Seq(
+                (0, AnomalyDetectionDataPoint(1.0, 1.0, confidence = 1.0)),
+                (1, AnomalyDetectionDataPoint(2.0, 2.0, confidence = 1.0)),
+                (2, AnomalyDetectionDataPoint(4.0, 4.0, isAnomaly = true, confidence = 1.0))))
               .once()
           }
 
@@ -1048,10 +1076,16 @@ class CheckTest extends AnyWordSpec with Matchers with SparkContextSpec with Fix
             // Size results
             (fakeAnomalyDetector.detect _)
               .expects(Vector(3.0, 4.0, 11.0), (2, 3))
-              .returns(Seq())
+              .returns(Seq(
+                (0, AnomalyDetectionDataPoint(3.0, 3.0, confidence = 1.0)),
+                (1, AnomalyDetectionDataPoint(4.0, 4.0, confidence = 1.0)),
+                (2, AnomalyDetectionDataPoint(11.0, 11.0, confidence = 1.0))))
               .once()
             (fakeAnomalyDetector.detect _).expects(Vector(3.0, 4.0, 4.0), (2, 3))
-              .returns(Seq((4, Anomaly(Option(4.0), 1.0))))
+              .returns(Seq(
+                (0, AnomalyDetectionDataPoint(3.0, 3.0, confidence = 1.0)),
+                (1, AnomalyDetectionDataPoint(4.0, 4.0, confidence = 1.0)),
+                (2, AnomalyDetectionDataPoint(4.0, 4.0, isAnomaly = true, confidence = 1.0))))
               .once()
           }
 
@@ -1082,10 +1116,16 @@ class CheckTest extends AnyWordSpec with Matchers with SparkContextSpec with Fix
             // Size results
             (fakeAnomalyDetector.detect _)
               .expects(Vector(1.0, 2.0, 11.0), (2, 3))
-              .returns(Seq())
+              .returns(Seq(
+                (0, AnomalyDetectionDataPoint(1.0, 1.0, confidence = 1.0)),
+                (1, AnomalyDetectionDataPoint(2.0, 2.0, confidence = 1.0)),
+                (2, AnomalyDetectionDataPoint(11.0, 11.0, confidence = 1.0))))
               .once()
             (fakeAnomalyDetector.detect _).expects(Vector(1.0, 2.0, 4.0), (2, 3))
-              .returns(Seq((4, Anomaly(Option(4.0), 1.0))))
+              .returns(Seq(
+                (0, AnomalyDetectionDataPoint(1.0, 1.0, confidence = 1.0)),
+                (1, AnomalyDetectionDataPoint(2.0, 2.0, confidence = 1.0)),
+                (2, AnomalyDetectionDataPoint(4.0, 4.0, isAnomaly = true, confidence = 1.0))))
               .once()
           }
 
@@ -1106,6 +1146,40 @@ class CheckTest extends AnyWordSpec with Matchers with SparkContextSpec with Fix
           assert(sizeAnomalyCheck.evaluate(contextNoRows).status == CheckStatus.Error)
         }
       }
+
+    "getNewestPointAnomalyResults returns correct assertion result from anomaly detection data point sequence " +
+      "with multiple data points" in {
+      val anomalySequence: Seq[(Long, AnomalyDetectionDataPoint)] =
+        Seq(
+          (0, AnomalyDetectionDataPoint(1.0, 1.0, confidence = 1.0)),
+          (1, AnomalyDetectionDataPoint(2.0, 2.0, confidence = 1.0)),
+          (2, AnomalyDetectionDataPoint(11.0, 11.0, isAnomaly = true, confidence = 1.0)))
+      val result: AnomalyDetectionAssertionResult =
+        getNewestPointAnomalyResults(AnomalyDetectionResult(anomalySequence))
+      assert(!result.hasNoAnomaly)
+      assert(result.anomalyDetectionMetadata.anomalyDetectionDataPoint ==
+        AnomalyDetectionDataPoint(11.0, 11.0, isAnomaly = true, confidence = 1.0))
+    }
+
+    "getNewestPointAnomalyResults returns correct assertion result from anomaly detection data point sequence " +
+      "with one data point" in {
+      val anomalySequence: Seq[(Long, AnomalyDetectionDataPoint)] =
+        Seq(
+          (0, AnomalyDetectionDataPoint(11.0, 11.0, confidence = 1.0)))
+      val result: AnomalyDetectionAssertionResult =
+        getNewestPointAnomalyResults(AnomalyDetectionResult(anomalySequence))
+      assert(result.hasNoAnomaly)
+      assert(result.anomalyDetectionMetadata.anomalyDetectionDataPoint ==
+        AnomalyDetectionDataPoint(11.0, 11.0, confidence = 1.0))
+    }
+
+    "assert getNewestPointAnomalyResults throws exception from empty anomaly detection sequence" in {
+      val anomalySequence: Seq[(Long, AnomalyDetectionDataPoint)] = Seq()
+      intercept[IllegalArgumentException] {
+        getNewestPointAnomalyResults(AnomalyDetectionResult(anomalySequence))
+      }
+    }
+
   }
 
   /**
diff --git a/src/test/scala/com/amazon/deequ/constraints/AnomalyBasedConstraintTest.scala b/src/test/scala/com/amazon/deequ/constraints/AnomalyBasedConstraintTest.scala
new file mode 100644
index 000000000..a21038ccd
--- /dev/null
+++ b/src/test/scala/com/amazon/deequ/constraints/AnomalyBasedConstraintTest.scala
@@ -0,0 +1,285 @@
+/**
+ * Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"). You may not
+ * use this file except in compliance with the License. A copy of the License
+ * is located at
+ *
+ *     http://aws.amazon.com/apache2.0/
+ *
+ * or in the "license" file accompanying this file. This file is distributed on
+ * an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
+ * express or implied. See the License for the specific language governing
+ * permissions and limitations under the License.
+ *
+ */
+package com.amazon.deequ.constraints
+
+import com.amazon.deequ.SparkContextSpec
+import com.amazon.deequ.analyzers.runners.MetricCalculationException
+import com.amazon.deequ.analyzers.{Analyzer, NumMatches, StateLoader, StatePersister}
+import com.amazon.deequ.anomalydetection.{AnomalyDetectionAssertionResult, AnomalyDetectionDataPoint, AnomalyDetectionMetadata}
+import com.amazon.deequ.constraints.ConstraintUtils.calculate
+import com.amazon.deequ.metrics.{DoubleMetric, Entity, Metric}
+import com.amazon.deequ.utils.FixtureSupport
+import org.apache.spark.sql.DataFrame
+import org.scalamock.scalatest.MockFactory
+import org.scalatest.wordspec.AnyWordSpec
+import org.scalatest.PrivateMethodTester
+
+import scala.util.{Failure, Try}
+
+class AnomalyBasedConstraintTest extends AnyWordSpec with SparkContextSpec
+  with FixtureSupport with MockFactory with PrivateMethodTester {
+  /**
+   * Sample function to use as value picker
+   *
+   * @return Returns input multiplied by 2
+   */
+  def valueDoubler(value: Double): Double = {
+    value * 2
+  }
+
+  /**
+   * Sample analyzer that returns a 1.0 value if the given column exists and fails otherwise.
+   */
+  case class SampleAnalyzer(column: String) extends Analyzer[NumMatches, DoubleMetric] {
+    override def toFailureMetric(exception: Exception): DoubleMetric = {
+      DoubleMetric(Entity.Column, "sample", column, Failure(MetricCalculationException
+        .wrapIfNecessary(exception)))
+    }
+
+
+    override def calculate(
+                            data: DataFrame,
+                            stateLoader: Option[StateLoader],
+                            statePersister: Option[StatePersister])
+    : DoubleMetric = {
+      val value: Try[Double] = Try {
+        require(data.columns.contains(column), s"Missing column $column")
+        1.0
+      }
+      DoubleMetric(Entity.Column, "sample", column, value)
+    }
+
+    override def computeStateFrom(data: DataFrame): Option[NumMatches] = {
+      throw new NotImplementedError()
+    }
+
+
+    override def computeMetricFrom(state: Option[NumMatches]): DoubleMetric = {
+      throw new NotImplementedError()
+    }
+  }
+
+  "Analysis based constraint" should {
+
+
+    "assert correctly on values if analysis is successful" in
+      withSparkSession { sparkSession =>
+        val df = getDfMissing(sparkSession)
+
+        // Analysis result should equal to 1.0 for an existing column
+        val functionA = (metric: Double) => { AnomalyDetectionAssertionResult(metric == 1.0,
+          AnomalyDetectionMetadata(AnomalyDetectionDataPoint(1.0, 1.0, confidence = 1.0))) }
+        val resultA = calculate(AnomalyBasedConstraint[NumMatches, Double, Double](
+          SampleAnalyzer("att1"), functionA), df)
+
+        assert(resultA.status == ConstraintStatus.Success)
+        assert(resultA.message.isEmpty)
+        assert(resultA.metric.isDefined)
+
+        // Analysis result should equal to 1.0 for an existing column
+        val functionB = (metric: Double) => {
+          AnomalyDetectionAssertionResult(metric != 1.0,
+            AnomalyDetectionMetadata(AnomalyDetectionDataPoint(1.0, 1.0, confidence = 1.0)))
+        }
+        val resultB = calculate(AnomalyBasedConstraint[NumMatches, Double, Double](
+          SampleAnalyzer("att1"), functionB), df)
+
+        assert(resultB.status == ConstraintStatus.Failure)
+        assert(resultB.message.contains("Value: 1.0 does not meet the constraint requirement, " +
+          "check the anomaly detection metadata!"))
+        assert(resultB.metric.isDefined)
+
+        // Analysis should fail for a non existing column
+        val functionC = (metric: Double) => {
+          AnomalyDetectionAssertionResult(metric == 1.0,
+            AnomalyDetectionMetadata(AnomalyDetectionDataPoint(1.0, 1.0, confidence = 1.0)))
+        }
+        val resultC = calculate(AnomalyBasedConstraint[NumMatches, Double, Double](
+          SampleAnalyzer("someMissingColumn"), functionC), df)
+
+        assert(resultC.status == ConstraintStatus.Failure)
+        assert(resultC.message.contains("requirement failed: Missing column someMissingColumn"))
+        assert(resultC.metric.isDefined)
+      }
+
+    "execute value picker on the analysis result value, if provided" in
+      withSparkSession { sparkSession =>
+
+
+        val df = getDfMissing(sparkSession)
+
+        // Analysis result should equal to 100.0 for an existing column
+        val functionA = (metric: Double) => {
+          AnomalyDetectionAssertionResult(metric == 2.0,
+            AnomalyDetectionMetadata(AnomalyDetectionDataPoint(2.0, 2.0, confidence = 1.0)))
+        }
+        assert(calculate(AnomalyBasedConstraint[NumMatches, Double, Double](
+          SampleAnalyzer("att1"), functionA, Some(valueDoubler)), df).status ==
+          ConstraintStatus.Success)
+
+        val functionB = (metric: Double) => {
+          AnomalyDetectionAssertionResult(metric != 2.0,
+            AnomalyDetectionMetadata(AnomalyDetectionDataPoint(2.0, 2.0, confidence = 1.0)))
+        }
+        assert(calculate(AnomalyBasedConstraint[NumMatches, Double, Double](
+          SampleAnalyzer("att1"), functionB, Some(valueDoubler)), df).status ==
+          ConstraintStatus.Failure)
+
+        // Analysis should fail for a non existing column
+        assert(calculate(AnomalyBasedConstraint[NumMatches, Double, Double](
+          SampleAnalyzer("someMissingColumn"), functionA, Some(valueDoubler)), df).status ==
+          ConstraintStatus.Failure)
+      }
+
+    "get the analysis from the context, if provided" in withSparkSession { sparkSession =>
+      val df = getDfMissing(sparkSession)
+
+      val emptyResults = Map.empty[Analyzer[_, Metric[_]], Metric[_]]
+      val validResults = Map[Analyzer[_, Metric[_]], Metric[_]](
+        SampleAnalyzer("att1") -> SampleAnalyzer("att1").calculate(df),
+        SampleAnalyzer("someMissingColumn") -> SampleAnalyzer("someMissingColumn").calculate(df)
+      )
+
+      // Analysis result should equal to 1.0 for an existing column
+      val functionA = (metric: Double) => {
+        AnomalyDetectionAssertionResult(metric == 1.0,
+          AnomalyDetectionMetadata(AnomalyDetectionDataPoint(1.0, 1.0, confidence = 1.0)))
+      }
+      assert(AnomalyBasedConstraint[NumMatches, Double, Double](SampleAnalyzer("att1"), functionA)
+        .evaluate(validResults).status == ConstraintStatus.Success)
+
+      val functionB = (metric: Double) => {
+        AnomalyDetectionAssertionResult(metric != 1.0,
+          AnomalyDetectionMetadata(AnomalyDetectionDataPoint(1.0, 1.0, confidence = 1.0)))
+      }
+      assert(AnomalyBasedConstraint[NumMatches, Double, Double](SampleAnalyzer("att1"), functionB)
+        .evaluate(validResults).status == ConstraintStatus.Failure)
+
+      assert(AnomalyBasedConstraint[NumMatches, Double, Double](
+        SampleAnalyzer("someMissingColumn"), functionA)
+        .evaluate(validResults).status == ConstraintStatus.Failure)
+
+      // Although assertion would pass, since analysis result is missing,
+      // constraint fails with missing analysis message
+      AnomalyBasedConstraint[NumMatches, Double, Double](SampleAnalyzer("att1"), functionA)
+        .evaluate(emptyResults) match {
+        case result =>
+          assert(result.status == ConstraintStatus.Failure)
+          assert(result.message.contains("Missing Analysis, can't run the constraint!"))
+          assert(result.metric.isEmpty)
+      }
+    }
+
+    "execute value picker on the analysis result value retrieved from context, if provided" in
+      withSparkSession { sparkSession =>
+        val df = getDfMissing(sparkSession)
+        val validResults = Map[Analyzer[_, Metric[_]], Metric[_]](
+          SampleAnalyzer("att1") -> SampleAnalyzer("att1").calculate(df))
+
+        val functionA = (metric: Double) => {
+          AnomalyDetectionAssertionResult(metric == 2.0,
+            AnomalyDetectionMetadata(AnomalyDetectionDataPoint(2.0, 2.0, confidence = 1.0)))
+        }
+        assert(AnomalyBasedConstraint[NumMatches, Double, Double](
+          SampleAnalyzer("att1"), functionA, Some(valueDoubler))
+          .evaluate(validResults).status == ConstraintStatus.Success)
+      }
+
+
+    "fail on analysis if value picker is provided but fails" in withSparkSession { sparkSession =>
+      def problematicValuePicker(value: Double): Double = {
+        throw new RuntimeException("Something wrong with this picker")
+      }
+
+      val df = getDfMissing(sparkSession)
+
+      val emptyResults = Map.empty[Analyzer[_, Metric[_]], Metric[_]]
+      val validResults = Map[Analyzer[_, Metric[_]], Metric[_]](
+        SampleAnalyzer("att1") -> SampleAnalyzer("att1").calculate(df))
+
+      val functionA = (metric: Double) => {
+        AnomalyDetectionAssertionResult(metric == 1.0,
+          AnomalyDetectionMetadata(AnomalyDetectionDataPoint(1.0, 1.0, confidence = 1.0)))
+      }
+      val constraint = AnomalyBasedConstraint[NumMatches, Double, Double](
+        SampleAnalyzer("att1"), functionA, Some(problematicValuePicker))
+
+      calculate(constraint, df) match {
+        case result =>
+          assert(result.status == ConstraintStatus.Failure)
+          assert(result.message.get.contains("Can't retrieve the value to assert on"))
+          assert(result.metric.isDefined)
+      }
+
+      constraint.evaluate(validResults) match {
+        case result =>
+          assert(result.status == ConstraintStatus.Failure)
+          assert(result.message.isDefined)
+          assert(result.message.get.startsWith("Can't retrieve the value to assert on"))
+          assert(result.metric.isDefined)
+      }
+
+      constraint.evaluate(emptyResults) match {
+        case result =>
+          assert(result.status == ConstraintStatus.Failure)
+          assert(result.message.contains("Missing Analysis, can't run the constraint!"))
+          assert(result.metric.isEmpty)
+      }
+
+    }
+
+    "fail on failed assertion function with hint in exception message if provided" in
+      withSparkSession { sparkSession =>
+
+        val df = getDfMissing(sparkSession)
+
+        val assertionFunction = (metric: Double) => {
+          AnomalyDetectionAssertionResult(metric == 0.9,
+            AnomalyDetectionMetadata(AnomalyDetectionDataPoint(1.0, 1.0, confidence = 1.0)))
+        }
+        val failingConstraint = AnomalyBasedConstraint[NumMatches, Double, Double](
+          SampleAnalyzer("att1"), assertionFunction, hint = Some("Value should be like ...!"))
+
+        calculate(failingConstraint, df) match {
+          case result =>
+            assert(result.status == ConstraintStatus.Failure)
+            assert(result.message.isDefined)
+            assert(result.message.get == "Value: 1.0 does not meet the constraint requirement, " +
+              "check the anomaly detection metadata! Value should be like ...!")
+            assert(result.metric.isDefined)
+        }
+      }
+
+    "return failed constraint for a failing assertion" in withSparkSession { session =>
+      val msg = "-test-"
+      val exception = new RuntimeException(msg)
+      val df = getDfMissing(session)
+
+      def failingAssertion(value: Double): AnomalyDetectionAssertionResult = throw exception
+
+      val constraintResult = calculate(
+        AnomalyBasedConstraint[NumMatches, Double, Double](
+          SampleAnalyzer("att1"), failingAssertion), df
+      )
+
+      assert(constraintResult.status == ConstraintStatus.Failure)
+      assert(constraintResult.metric.isDefined)
+      assert(constraintResult.message.contains(s"Can't execute the assertion: $msg!"))
+    }
+
+  }
+
+}
diff --git a/src/test/scala/com/amazon/deequ/constraints/ConstraintUtils.scala b/src/test/scala/com/amazon/deequ/constraints/ConstraintUtils.scala
index 5782bc18c..228e41cd0 100644
--- a/src/test/scala/com/amazon/deequ/constraints/ConstraintUtils.scala
+++ b/src/test/scala/com/amazon/deequ/constraints/ConstraintUtils.scala
@@ -27,6 +27,9 @@ object ConstraintUtils {
         case c: Constraint => c
     }
 
-    analysisBasedConstraint.asInstanceOf[AnalysisBasedConstraint[_, _, _]].calculateAndEvaluate(df)
+    analysisBasedConstraint match {
+      case analysis: AnalysisBasedConstraint[_, _, _] => analysis.calculateAndEvaluate(df)
+      case anomaly: AnomalyBasedConstraint[_, _, _] => anomaly.calculateAndEvaluate(df)
+    }
   }
 }
diff --git a/src/test/scala/com/amazon/deequ/constraints/ConstraintsTest.scala b/src/test/scala/com/amazon/deequ/constraints/ConstraintsTest.scala
index e4a8ba898..19712c196 100644
--- a/src/test/scala/com/amazon/deequ/constraints/ConstraintsTest.scala
+++ b/src/test/scala/com/amazon/deequ/constraints/ConstraintsTest.scala
@@ -25,6 +25,7 @@ import org.apache.spark.sql.Row
 import org.apache.spark.sql.types.{DoubleType, StringType}
 import Constraint._
 import com.amazon.deequ.SparkContextSpec
+import com.amazon.deequ.anomalydetection.{AnomalyDetectionAssertionResult, AnomalyDetectionDataPoint, AnomalyDetectionMetadata}
 
 class ConstraintsTest extends WordSpec with Matchers with SparkContextSpec with FixtureSupport {
 
@@ -163,15 +164,32 @@ class ConstraintsTest extends WordSpec with Matchers with SparkContextSpec with
   "Anomaly constraint" should {
     "assert on anomaly analyzer values" in withSparkSession { sparkSession =>
       val df = getDfMissing(sparkSession)
+
       assert(calculate(Constraint.anomalyConstraint[NumMatchesAndCount](
-        Completeness("att1"), _ > 0.4), df).status == ConstraintStatus.Success)
+        Completeness("att1"), (metric: Double) => {
+          AnomalyDetectionAssertionResult(metric > 0.4,
+            AnomalyDetectionMetadata(AnomalyDetectionDataPoint(1.0, 1.0, confidence = 1.0)))
+        } ), df)
+        .status == ConstraintStatus.Success)
       assert(calculate(Constraint.anomalyConstraint[NumMatchesAndCount](
-        Completeness("att1"), _ < 0.4), df).status == ConstraintStatus.Failure)
+        Completeness("att1"), (metric: Double) => {
+          AnomalyDetectionAssertionResult(metric < 0.4,
+            AnomalyDetectionMetadata(AnomalyDetectionDataPoint(1.0, 1.0, confidence = 1.0)))
+        }), df)
+        .status == ConstraintStatus.Failure)
 
       assert(calculate(Constraint.anomalyConstraint[NumMatchesAndCount](
-        Completeness("att2"), _ > 0.7), df).status == ConstraintStatus.Success)
+        Completeness("att2"), (metric: Double) => {
+          AnomalyDetectionAssertionResult(metric > 0.7,
+            AnomalyDetectionMetadata(AnomalyDetectionDataPoint(1.0, 1.0, confidence = 1.0)))
+        }), df)
+        .status == ConstraintStatus.Success)
       assert(calculate(Constraint.anomalyConstraint[NumMatchesAndCount](
-        Completeness("att2"), _ < 0.7), df).status == ConstraintStatus.Failure)
+        Completeness("att2"), (metric: Double) => {
+          AnomalyDetectionAssertionResult(metric < 0.7,
+            AnomalyDetectionMetadata(AnomalyDetectionDataPoint(1.0, 1.0, confidence = 1.0)))
+        }), df)
+        .status == ConstraintStatus.Failure)
     }
   }
 }