diff --git a/NuRadioReco/modules/channelCWNotchFilter.py b/NuRadioReco/modules/channelCWNotchFilter.py
new file mode 100644
index 000000000..7dff3f21c
--- /dev/null
+++ b/NuRadioReco/modules/channelCWNotchFilter.py
@@ -0,0 +1,295 @@
+import logging
+logger = logging.getLogger("NuRadioReco.channelCWNotchFilter")
+import time
+import numpy as np
+from scipy import signal
+from NuRadioReco.utilities import units
+from NuRadioReco.utilities import fft
+
+"""
+Contains module to filter continuous wave out of the signal using notch filters
+on peaks in frequency spectrum
+"""
+
+
+def find_frequency_peaks_from_trace(trace : np.ndarray, fs : float, threshold : float = 4):
+    """
+    Function fo find the frequency peaks in the real fourier transform of the input trace.
+
+    Parameters
+    ----------
+    trace : np.ndarray
+        Waveform 
+    fs : float
+        Sampling frequency, (input should be taking from the channel object)
+    threshold : float, default = 4
+        Threshold for peak definition. A peak is defined as a point in the frequency spectrum
+        that exceeds threshold * rms(real fourier transform)
+    
+    Returns
+    -------
+    freq_peaks : np.ndarray
+        Frequencies at which a peak was found
+    """
+    freq = np.fft.rfftfreq(len(trace), d=1/fs)
+    ft = fft.time2freq(trace, fs)
+    
+    freq_peaks = find_frequency_peaks(freq, ft, fs=fs, threshold=threshold)
+
+    return freq_peaks
+
+
+def find_frequency_peaks(freq: np.ndarray, spectrum : np.ndarray, threshold : float = 4):
+    """
+    Function fo find the frequency peaks in the real fourier transform of the input trace.
+
+    Parameters
+    ----------
+    freq : np.ndarray
+        Frequencies of a NuRadio time trace
+    spectrum : np.ndarray
+        Spectrum of a NuRadio time trace
+    threshold : float, default = 4
+        Threshold for peak definition. A peak is defined as a point in the frequency spectrum
+        that exceeds threshold * rms(real fourier transform)
+    
+    Returns
+    -------
+    freq : np.ndarray
+        Frequencies at which a peak was found
+    """
+    
+    rms = np.sqrt(np.mean(np.abs(spectrum)**2))
+    peak_idxs = np.where(np.abs(spectrum) > threshold * rms)[0]
+
+    return freq[peak_idxs]
+
+
+def get_filter(freq : int, fs, quality_factor=1e3, cache=None):
+    """
+    Function to get single notch filter for a given frequency.
+    
+    Parameters
+    ----------
+    freq : np.ndarray
+        Frequency
+    fs : float,
+        sampling frequency in MHz
+    quality_factor : int, default = 1000
+        quality factor of the notch filter, defined as the ratio f0/bw, where f0 is the centre frequency
+        and bw the bandwidth of the filter at (f0,-3 dB)
+    cache : dict, default = None,
+        Optional caching dictionary. The function will check whether the frequency to be filtered
+        is in the dictionary values and will otherwise add it
+        !!! Note this does not cache the quality factor information
+    
+    Returns
+    -------
+    filter : list, shape (6)
+        second order IIR notch filter at frequency freq
+    """
+    if cache is not None:
+        if freq in cache.keys():
+            return cache[freq]
+    filter = signal.iirnotch(freq, quality_factor, fs=fs)
+    if cache is not None:
+        # Check to avoid cache dictionary overflowing the memory,
+        # set to roughly stay below 6 MB (every filter is 6 floats + freq = 7 floats ~ 56B)
+        if len(cache.keys()) < 1e5:
+            cache[freq] = filter
+    return filter
+
+
+def filter_cws(trace : np.ndarray, freq : np.ndarray, spectrum : np.ndarray, fs : float, quality_factor=1e3, threshold=4,
+               cache : dict = None,
+               filters : list = None):
+    """
+    Function that applies a notch filter at the frequency peaks of a given time trace
+    using the scipy library
+
+    Parameters
+    ----------
+    trace : np.ndarray
+        waveform (shape: [2048])
+    freq : np.ndarray
+        Frequency of the trace's real fourier transform
+    spectrum:
+        the trace's real fourier transform
+    fs : float
+        sampling frequency in MHz
+    quality_factor : int, default = 1000
+        quality factor of the notch filter, defined as the ratio f0/bw, where f0 is the centre frequency
+        and bw the bandwidth of the filter at (f0,-3 dB)
+    threshold : int, default = 4
+        threshold for peak definition. A peak is defined as a point in the frequency spectrum
+        that exceeds threshold * rms(real fourier transform)
+    cache : dict, default = None,
+        Optional caching dictionary. The function will check whether the frequency to be filtered
+        is in the dictionary values and will otherwise add it
+        !!! Note this assumes the quality_factor is the same for all notch filters!!!
+    filters : NoneType or list, default = None
+        Optional list to which the filters used in this function can be appended for future reference
+
+    Returns
+    -------
+    trace : np.ndarray
+        CW-filtered trace
+    """
+    freqs = find_frequency_peaks(freq, spectrum, threshold=threshold)
+
+    if len(freqs):
+        # the array is reshaped to (nr_of_filters, nr_of_coefficients), since iirnotch is a second order IIR,
+        # the nr_of_coefficients will be 6: 3 for the numerator and 3 for the denumerator, in that order
+        notch_filters = np.array([get_filter(freq, fs, quality_factor, cache=cache) for freq in freqs]).reshape(-1, 6)
+        if filters is not None:
+            filters.append(notch_filters)
+        logging.debug(f"Shape of notch filters for one channel is: {notch_filters.shape}")
+        trace_notched = signal.sosfiltfilt(notch_filters, trace, padtype = None)
+        return trace_notched
+    else:
+        # append empty list when filters is specified to ensure
+        # filters list is shape 24 when looping over channels
+        if filters is not None:
+            filters.append([])
+    
+    return trace
+
+
+def plot_trace(channel, ax, fs=3.2e9*units.Hz, label=None, plot_kwargs=dict()):
+    """
+    Function to plot trace of given channel
+    
+    Parameters
+    ----------
+    channel : NuRadio channel class
+        channel from which to get trace
+    ax : matplotlib.axes
+        ax on which to plot
+    fs : float, default = 3.2 Hz
+        sampling frequency
+    label : string
+        plotlabel
+    plot_kwargs : dict
+        options for plotting
+    """
+    times = np.arange(2048)/fs / units.ns
+    trace = channel.get_trace()
+
+    legendloc = 2
+
+    ax.plot(times, trace, label=label, **plot_kwargs)
+    ax.set_xlabel("time / ns")
+    ax.set_ylabel("trace / V")
+    ax.legend(loc=legendloc)
+
+
+def plot_ft(channel, ax, label=None, plot_kwargs=dict()):
+    """
+    Function to plot real frequency spectrum of given channel
+    
+    Parameters
+    ----------
+    channel : NuRadio channel class
+        channel from which to get trace
+    ax : matplotlib.axes
+        ax on which to plot
+    label : string
+        plotlabel
+    plot_kwargs : dict
+        options for plotting
+    """
+    freqs = channel.get_frequencies()
+    spec = channel.get_frequency_spectrum()
+
+    legendloc = 2
+
+    ax.plot(freqs, np.abs(spec), label=label, **plot_kwargs)
+    ax.set_xlabel("freq / GHz")
+    ax.set_ylabel("amplitude / V/GHz")
+    ax.legend(loc = legendloc)
+
+
+class channelCWNotchFilter():
+    """ Continuous wave (CW) filter module. Uses notch filters from the scipy library """
+    
+    def __init__(self):
+        pass
+
+    def begin(self, quality_factor=1e3, threshold=4, save_filters=False):
+        self.quality_factor = quality_factor
+        self.threshold = threshold
+        self.filters = [] if save_filters else None
+        # dictionary to cache known notch filters at specific frequencies
+        self.filter_cache = {}
+
+    def run(self, event, station, det):
+        for channel in station.iter_channels():
+            fs = channel.get_sampling_rate()
+            freq =  channel.get_frequencies()
+            spectrum = channel.get_frequency_spectrum()
+            trace = channel.get_trace()
+            trace_fil = filter_cws(
+                trace, freq, spectrum, fs, quality_factor=self.quality_factor, threshold=self.threshold, 
+                cache=self.filter_cache, filters=self.filters)
+            
+            channel.set_trace(trace_fil, fs)
+
+
+# Standard test for people playing around with module settings, applies the module as one would in a data reading pipeline
+# using one event in RNO_G_DATA (choose station and run) as a test
+if __name__ == "__main__":
+    import os
+    import logging
+    import argparse
+    import matplotlib.pyplot as plt
+   
+    from NuRadioReco.modules.io.RNO_G.readRNOGDataMattak import readRNOGData
+
+    parser = argparse.ArgumentParser(prog="%(prog)s", usage="cw filter test")
+    parser.add_argument("--station", type=int, default=24)
+    parser.add_argument("--channel", type = int, default = 0)
+    parser.add_argument("--run", type=int, default=1)
+
+    parser.add_argument("--quality_factor", type=int, default=1e3)
+    parser.add_argument("--threshold", type=int, default=4)
+    parser.add_argument("--fs", type=float, default=3.2e9 * units.Hz)
+    
+    parser.add_argument("--save_dir", type=str, default=None,
+                        help="Directory where to save plot produced by the test.\
+                                If None, saves to NuRadioReco test directory")
+
+    args = parser.parse_args()
+    
+    data_dir = os.environ["RNO_G_DATA"]
+    rnog_reader = readRNOGData(log_level = logging.DEBUG)
+
+    root_dirs = f"{data_dir}/station{args.station}/run{args.run}"
+    rnog_reader.begin(root_dirs,
+                      # linear voltage calibration
+                      convert_to_voltage=True,
+                      mattak_kwargs=dict(backend="uproot"))
+
+    channelCWNotchFilter = channelCWNotchFilter()
+    channelCWNotchFilter.begin(quality_factor=args.quality_factor, threshold=args.threshold)
+
+    for event in rnog_reader.run():
+        station_id = event.get_station_ids()[0]
+        station = event.get_station(station_id)
+
+        fig, axs = plt.subplots(1, 2, figsize=(14, 6))
+        plot_trace(station.get_channel(args.channel), axs[0], label="before")
+        plot_ft(station.get_channel(args.channel), axs[1], label="before")
+        t0 = time.time()
+        channelCWNotchFilter.run(event, station, det=0)
+        logger.debug(f"Filter took {time.time() - t0} s to run.")
+        plot_trace(station.get_channel(args.channel), axs[0], label="after")
+        plot_ft(station.get_channel(args.channel), axs[1], label="after")
+        
+        if args.save_dir is None:
+            fig_dir = os.path.abspath(f"{__file__}/../../test")
+        else:
+            fig_dir = args.save_dir
+
+
+        fig.savefig(f"{fig_dir}/test_cw_filter", bbox_inches="tight")
+        break   
diff --git a/changelog.txt b/changelog.txt
index 9b42724d8..5688a21fe 100644
--- a/changelog.txt
+++ b/changelog.txt
@@ -57,6 +57,7 @@ for the phasing_mode == "slice"
 - Improved logging: created NuRadioLogger class which is now used by default, the NRR and NRMC loggers
 are created automatically when importing the packages, and a new STATUS logging level was added.
 - Updating the ray_tracing_2D class in order to be able to use Numba optimization
+- Add new cw filter module to NuRadioReco using notch filters on peaks in the frequency spectrum
 
 bugfixes:
 - Fixed bug in get_travel_time in directRayTracing propagation module