diff --git a/tksbrokerapi/TradeRoutines.py b/tksbrokerapi/TradeRoutines.py
index b46f062..203d27b 100644
--- a/tksbrokerapi/TradeRoutines.py
+++ b/tksbrokerapi/TradeRoutines.py
@@ -28,6 +28,7 @@
 
 from datetime import datetime, timedelta
 from dateutil.tz import tzutc
+import pandas as pd
 
 
 # --- Main constants:
@@ -262,3 +263,70 @@ def CalculateLotsForDeal(currentPrice: float, maxCost: float, volumeInLot: int =
         lots = 0
 
     return lots
+
+
+def HampelFilter(pdSeries: pd.Series, window: int = 5, sigma: float = 3, scaleFactor: float = 1.4826) -> pd.Series:
+    """
+    Outlier Detection with Hampel Filter. It can detect outliers based on a sliding window and counting difference
+    between median values and input values of series. The Hampel filter is often considered extremely effective in practice.
+
+    For each window we calculate the median and the standard deviation expressed as the median absolute deviation (MAD).
+    If the considered observation differs from the window median by more than x standard deviations, we treat it
+    as an outlier. Also used a constant scale factor which is dependent on the series distribution,
+    e.g. for Gaussian distribution it is approximately 1.4826.
+
+    References:
+    1. Lewinson Eryk, "Outlier Detection with Hampel Filter", September 26, 2019.
+       Link: https://towardsdatascience.com/outlier-detection-with-hampel-filter-85ddf523c73d
+    2. Liu, Hancong, Sirish Shah, and Wei Jiang, "On-line outlier detection and data cleaning". Computers and Chemical Engineering. Vol. 28, March 2004, pp. 1635–1647.
+       Link: https://sites.ualberta.ca/~slshah/files/on_line_outlier_det.pdf
+    3. Hampel F. R., "The influence curve and its role in robust estimation". Journal of the American Statistical Association, 69, 382–393, 1974.
+
+    Examples:
+    - `HampelFilter(pdSeries=pd.Series([1, 1, 1, 1, 1, 1]), window=3) -> pd.Series([False, False, False, False, False, False])`
+    - `HampelFilter(pdSeries=pd.Series([1, 1, 1, 2, 1, 1]), window=3) -> pd.Series([False, False, False, True, False, False])`
+    - `HampelFilter(pdSeries=pd.Series([0, 1, 1, 1, 1, 0]), window=3) -> pd.Series([True, False, False, False, False, True])`
+    - `HampelFilter(pdSeries=pd.Series([1])) -> pd.Series([False])`
+
+    :param pdSeries: Pandas Series object with numbers in which we identify outliers.
+    :param window: length of the sliding window (default: 5 points), 1 <= window <= len(pdSeries).
+    :param sigma: sigma is the number of standard deviations which identify the outlier (default: 3 sigmas), > 0.
+    :param scaleFactor: constant scale factor (default: 1.4826), > 0.
+    :return: Pandas Series object with True/False values. `True` mean that an outlier detected in that position of input series.
+             If an error occurred then empty series returned.
+    """
+    try:
+        if window < 1:
+            window = 1
+
+        if window > len(pdSeries):
+            window = len(pdSeries)
+
+        if sigma <= 0:
+            sigma = 3
+
+        if scaleFactor <= 0:
+            scaleFactor = 1.4826
+
+        # Extend data to avoid undetected anomaly in first and last elements by original algorithm:
+        pdSeries = pd.concat([pdSeries[: window], pdSeries, pdSeries[-window:]])
+
+        rollingMedian = pdSeries.rolling(
+            window=2 * window,
+            center=True,
+        ).median()
+
+        rollingMAD = scaleFactor * pdSeries.rolling(
+            window=2 * window,
+            center=True,
+        ).apply(lambda x: pd.Series.median(pd.Series.abs(x - pd.Series.median(x))))
+
+        delta = pd.Series.abs(pdSeries - rollingMedian)
+
+        new = delta > sigma * rollingMAD
+        new = new[window: -window]
+
+    except Exception:
+        new = pd.Series()
+
+    return new