diff --git a/cluster_experiments/washover.py b/cluster_experiments/washover.py
index fdd9ca18..523c9188 100644
--- a/cluster_experiments/washover.py
+++ b/cluster_experiments/washover.py
@@ -242,5 +242,124 @@ def from_config(cls, config) -> "Washover":
         return cls(washover_time_delta=washover_time_delta)
 
 
+class TwoEventsWashover(Washover):
+    """
+    For each record in our experiment, two events' timestamps,
+    login_timestamp and logout_timestamp.
+     We want to apply washover such that if there is a change in treatment
+    between login_timestamp and logout_timestamp.
+    Ex:
+    Calendar:
+        +-----------+-------+
+        | treatment | time  |
+        +-----------+-------+
+        | A         | 10:00 |
+        | B         | 11:00 |
+        | B         | 12:00 |
+        +-----------+-------+
+    Record data:
+
+        +----+------------+----------+
+        | id | start_time | end_time |
+        +----+------------+----------+
+        |  1 | 10:50      | 10:59    |
+        |  2 | 10:51      | 11:01    |
+        |  3 | 11:01      | 11:05    |
+        |  4 | 11:01      | 12:01    |
+        |  5 | 12:01      | 12:05    |
+        +----+------------+----------+
+
+    This washover will drop record number 2.
+    """
+
+    def __init__(
+        self,
+        calendar_df: pd.DataFrame,
+        time_column_calendar: str = "start_time",
+        treatment_column_calendar: str = "treatment",
+    ):
+        """
+        Args:
+            calendar_df (pd.DataFrame): df with the calendar of treatments.
+            time_column_calendar (str): Name of the time column in the calendar df.
+            treatment_column_calendar (str): Name of the treatment column in the calendar df.
+            record_df (pd.DataFrame): Record dataframe.
+            start_time_column (str): Name of the start time column in the record dataframe.
+            end_time_column (str): Name of the end time column in the record dataframe.
+
+        """
+        self.calendar_df = calendar_df
+        self.time_column_calendar = time_column_calendar
+        self.treatment_column_calendar = treatment_column_calendar
+
+    def _validate_columns(
+        self, record_df: pd.DataFrame, start_time_column: str, end_time_column: str
+    ):
+        if self.time_column_calendar not in self.calendar_df.columns:
+            raise ValueError(
+                f"{self.time_column_calendar = } is not in the calendar dataframe columns and/or not specified as an input."
+            )
+        if self.treatment_column_calendar not in self.calendar_df.columns:
+            raise ValueError(
+                f"{self.treatment_column_calendar = } is not in the calendar dataframe columns and/or not specified as an input."
+            )
+        if start_time_column not in record_df.columns:
+            raise ValueError(
+                f"{start_time_column = } is not in the record dataframe columns and/or not specified as an input."
+            )
+        if end_time_column not in record_df.columns:
+            raise ValueError(
+                f"{end_time_column = } is not in the record dataframe columns and/or not specified as an input."
+            )
+
+    def washover(
+        self,
+        record_df: pd.DataFrame,
+        start_time_column: str = "start_time",
+        end_time_column: str = "end_time",
+    ):
+        """
+        Return the Dataframe after applying the washover.
+        """
+        self._validate_columns(record_df, start_time_column, end_time_column)
+        self.calendar_df[self.time_column_calendar] = pd.to_datetime(
+            self.calendar_df[self.time_column_calendar],
+        ).dt.time
+        record_df[start_time_column] = pd.to_datetime(record_df[start_time_column])
+        record_df[end_time_column] = pd.to_datetime(record_df[end_time_column])
+        record_df["start_time_floor"] = (
+            record_df[start_time_column].dt.floor("h").dt.time
+        )
+        record_df["end_time_floor"] = record_df[end_time_column].dt.floor("h").dt.time
+        record_df[end_time_column] = record_df[end_time_column].dt.time
+        record_df[start_time_column] = record_df[start_time_column].dt.time
+        record_df = (
+            record_df.merge(
+                self.calendar_df,
+                how="left",
+                left_on="start_time_floor",
+                right_on=self.time_column_calendar,
+                suffixes=("", "_start"),
+            )
+            .drop(columns=["start_time_start"])
+            .merge(
+                self.calendar_df,
+                how="left",
+                left_on="end_time_floor",
+                right_on=self.time_column_calendar,
+                suffixes=("", "_end"),
+            )
+            .drop(
+                columns=[
+                    self.time_column_calendar + "_end",
+                ]
+            )
+        )
+        record_df = record_df.query("treatment == treatment_end").drop(
+            columns=["treatment_end"]
+        )
+        return record_df
+
+
 # This is kept in here because of circular imports, need to rethink this
 washover_mapping = {"": EmptyWashover, "constant_washover": ConstantWashover}