diff --git a/DIRECTORY.md b/DIRECTORY.md
index 73f289bb..c4be82d0 100644
--- a/DIRECTORY.md
+++ b/DIRECTORY.md
@@ -250,6 +250,10 @@
       * [Test Non Overlapping Intervals](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/arrays/non_overlapping_intervals/test_non_overlapping_intervals.py)
     * Subarrays With Fixed Bounds
       * [Test Subarrays With Fixed Bounds](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/arrays/subarrays_with_fixed_bounds/test_subarrays_with_fixed_bounds.py)
+  * Circular Buffer
+    * [Circular Buffer](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/circular_buffer/circular_buffer.py)
+    * [Exceptions](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/circular_buffer/exceptions.py)
+    * [Test Circular Buffer](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/circular_buffer/test_circular_buffer.py)
   * Dicts
     * [Default Dicts](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/dicts/default_dicts.py)
     * [Ordered Dict](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/dicts/ordered_dict.py)
@@ -325,6 +329,12 @@
       * [Test Min Stack](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/stacks/minstack/test_min_stack.py)
     * [Test Stacks](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/stacks/test_stacks.py)
   * Streams
+    * Moving Average
+      * [Exponential Moving Average](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/streams/moving_average/exponential_moving_average.py)
+      * [Moving Average](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/streams/moving_average/moving_average.py)
+      * [Moving Average With Buffer](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/streams/moving_average/moving_average_with_buffer.py)
+      * [Test Moving Average](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/streams/moving_average/test_moving_average.py)
+      * [Weighted Moving Average](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/streams/moving_average/weighted_moving_average.py)
     * Stream Checker
       * [Test Stream Checker](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/streams/stream_checker/test_stream_checker.py)
   * Timemap
@@ -930,7 +940,6 @@
     * Linked List
       * [Test Reorder List](https://github.com/BrianLusina/PythonSnips/blob/master/tests/datastructures/linked_list/test_reorder_list.py)
     * [Test Build One 2 3](https://github.com/BrianLusina/PythonSnips/blob/master/tests/datastructures/test_build_one_2_3.py)
-    * [Test Circular Buffer](https://github.com/BrianLusina/PythonSnips/blob/master/tests/datastructures/test_circular_buffer.py)
     * [Test Consecutive](https://github.com/BrianLusina/PythonSnips/blob/master/tests/datastructures/test_consecutive.py)
     * [Test Data Reverse](https://github.com/BrianLusina/PythonSnips/blob/master/tests/datastructures/test_data_reverse.py)
     * [Test Dominator](https://github.com/BrianLusina/PythonSnips/blob/master/tests/datastructures/test_dominator.py)
diff --git a/datastructures/circular_buffer/__init__.py b/datastructures/circular_buffer/__init__.py
index 2d8537e7..fa64e9ed 100755
--- a/datastructures/circular_buffer/__init__.py
+++ b/datastructures/circular_buffer/__init__.py
@@ -1,45 +1,11 @@
-class BufferFullException(Exception):
-    pass
-
-
-class BufferEmptyException(Exception):
-    pass
-
-
-class CircularBuffer:
-    def __init__(self, size):
-        self.buffer = bytearray(size)
-        self.read_point = 0
-        self.write_point = 0
-
-    def _update_buffer(self, data):
-        """
-        Protected helper method for Python 2/3
-        """
-        try:
-            self.buffer[self.write_point] = data
-        except TypeError:
-            self.buffer[self.write_point] = ord(data)
-
-    def clear(self):
-        self.buffer = bytearray(len(self.buffer))
-
-    def write(self, data):
-        if all(self.buffer):
-            raise BufferFullException
-        self._update_buffer(data)
-        self.write_point = (self.write_point + 1) % len(self.buffer)
-
-    def read(self):
-        if not any(self.buffer):
-            raise BufferEmptyException
-        data = chr(self.buffer[self.read_point])
-        self.buffer[self.read_point] = 0
-        self.read_point = (self.read_point + 1) % len(self.buffer)
-        return data
-
-    def overwrite(self, data):
-        self._update_buffer(data)
-        if all(self.buffer) and self.read_point == self.write_point:
-            self.read_point = (self.read_point + 1) % len(self.buffer)
-        self.write_point = (self.write_point + 1) % len(self.buffer)
+from datastructures.circular_buffer.circular_buffer import CircularBuffer
+from datastructures.circular_buffer.exceptions import (
+    BufferFullException,
+    BufferEmptyException,
+)
+
+__all__ = [
+    "CircularBuffer",
+    "BufferFullException",
+    "BufferEmptyException",
+]
diff --git a/datastructures/circular_buffer/circular_buffer.py b/datastructures/circular_buffer/circular_buffer.py
new file mode 100755
index 00000000..fc782f11
--- /dev/null
+++ b/datastructures/circular_buffer/circular_buffer.py
@@ -0,0 +1,43 @@
+from datastructures.circular_buffer.exceptions import (
+    BufferFullException,
+    BufferEmptyException,
+)
+
+
+class CircularBuffer:
+    def __init__(self, size):
+        self.buffer = bytearray(size)
+        self.read_point = 0
+        self.write_point = 0
+
+    def _update_buffer(self, data):
+        """
+        Protected helper method for Python 2/3
+        """
+        try:
+            self.buffer[self.write_point] = data
+        except TypeError:
+            self.buffer[self.write_point] = ord(data)
+
+    def clear(self):
+        self.buffer = bytearray(len(self.buffer))
+
+    def write(self, data):
+        if all(self.buffer):
+            raise BufferFullException
+        self._update_buffer(data)
+        self.write_point = (self.write_point + 1) % len(self.buffer)
+
+    def read(self):
+        if not any(self.buffer):
+            raise BufferEmptyException
+        data = chr(self.buffer[self.read_point])
+        self.buffer[self.read_point] = 0
+        self.read_point = (self.read_point + 1) % len(self.buffer)
+        return data
+
+    def overwrite(self, data):
+        self._update_buffer(data)
+        if all(self.buffer) and self.read_point == self.write_point:
+            self.read_point = (self.read_point + 1) % len(self.buffer)
+        self.write_point = (self.write_point + 1) % len(self.buffer)
diff --git a/datastructures/circular_buffer/exceptions.py b/datastructures/circular_buffer/exceptions.py
new file mode 100644
index 00000000..c3b8d396
--- /dev/null
+++ b/datastructures/circular_buffer/exceptions.py
@@ -0,0 +1,6 @@
+class BufferFullException(Exception):
+    pass
+
+
+class BufferEmptyException(Exception):
+    pass
diff --git a/tests/datastructures/test_circular_buffer.py b/datastructures/circular_buffer/test_circular_buffer.py
similarity index 100%
rename from tests/datastructures/test_circular_buffer.py
rename to datastructures/circular_buffer/test_circular_buffer.py
diff --git a/datastructures/streams/moving_average/README.md b/datastructures/streams/moving_average/README.md
new file mode 100644
index 00000000..d937a3e2
--- /dev/null
+++ b/datastructures/streams/moving_average/README.md
@@ -0,0 +1,62 @@
+# Moving Average from Data Stream
+
+Given a stream of integers and a window size, calculate the moving average of all integers in the sliding window. 
+Implement a class called MovingAverage that has the following methods:
+
+Constructor (int size): This constructor initializes the object with the specified window size.
+
+double next (int val): This method takes an integer value as input and returns the moving average of the last size 
+values from the stream.
+
+## Constraints
+
+- 1 <= size <= 100
+- -10^3 <= val <= 10^3
+- At most 10^2 calls will be made to next
+
+## Examples
+
+![Example 1](./images/examples/moving_average_example_1.png)
+
+## Solution
+
+The algorithm calculates the moving average of the most recent values within a fixed-size window. It employs a queue to
+store these values and maintains a running sum for efficient computation. Each time a new value is added to the window,
+it is appended to the queue, and the running sum is updated accordingly. If the queue exceeds the specified size (i.e.,
+the window is full), the oldest value is removed from the queue, and the sum is adjusted by subtracting this value. The
+moving average is then calculated by dividing the running sum by the number of values in the queue. This approach ensures
+that the moving average is updated, achieving constant time complexity for each operation.
+
+1. **Constructor**: The constructor initializes the following variables:
+   - _size_: This represents the window size, the maximum number of recent values for calculating the moving average. 
+   - _queue_: A queue data structure stores the most recent values up to the specified window size. The queue supports 
+     efficient operations for adding new values to the end and removing the oldest value from the front, essential for 
+     maintaining the sliding window.
+   - _window_sum_: This keeps a running sum of the values currently in the window. This allows the moving average to be 
+     calculated efficiently without the need to sum all values repeatedly.
+
+2. The **next** method: The next method calculates the moving average by following these steps:
+   - Enqueue the new value (val) to queue and add it to window_sum. This effectively extends the current sliding window 
+     to include the new value.
+   - If the number of elements in queue exceeds size (indicating the window is full), remove the oldest value from queue
+     and update window_sum by subtracting this value. This ensures the queue size stays within the specified window size. 
+   - Compute the moving average as window_sum / len(queue). Use floating-point division to ensure the result is a float.
+
+### Time Complexity
+
+1. **Constructor**: Initializing the MovingAverage instance with a given size takes O(1) time. This is because the operation
+   involves only setting the size, initializing an empty queue, and initializing the sum.
+2. **Next method**: 
+   - Appending a value: Adding a value to the queue takes O(1) time as operations like append and popleft are optimized 
+    for constant time execution in a queue.
+   - Removing the oldest value: If the window is full, removing the oldest value also takes O(1) time, as popleft is a 
+     constant time operation in a queue.
+   - Calculating the moving average: This step involves dividing window_sum by the number of elements in the queue, 
+     which is an O(1) operation.
+
+Thus the time complexity of the next method is O(1).
+
+### Space Complexity
+
+The queue holds up to _size_ elements at any given time (i.e., the number of elements in the sliding window). Therefore, 
+the space complexity of the queue is O(size), where size is the maximum window size.
diff --git a/datastructures/streams/moving_average/__init__.py b/datastructures/streams/moving_average/__init__.py
new file mode 100644
index 00000000..152d17e4
--- /dev/null
+++ b/datastructures/streams/moving_average/__init__.py
@@ -0,0 +1,6 @@
+from datastructures.streams.moving_average.moving_average_with_buffer import (
+    MovingAverageWithBuffer,
+)
+from datastructures.streams.moving_average.moving_average import MovingAverage
+
+__all__ = ["MovingAverage", "MovingAverageWithBuffer"]
diff --git a/datastructures/streams/moving_average/exponential_moving_average.py b/datastructures/streams/moving_average/exponential_moving_average.py
new file mode 100644
index 00000000..d80b9a00
--- /dev/null
+++ b/datastructures/streams/moving_average/exponential_moving_average.py
@@ -0,0 +1,28 @@
+class ExponentialMovingAverage:
+    """
+    The Exponential Moving Average (EMA) is widely used in finance and signal processing because it reacts faster to
+    recent price changes than a simple moving average.
+
+    The beauty of the EMA is its efficiency: it does not require a buffer or a window of previous values. You only need
+    to store the previous EMA result. This makes the time and space complexity both O(1).
+
+    Why use EMA?
+    1. Reduced Lag: Because it weights the most recent data most heavily, it catches trend reversals much sooner than an SMA.
+    2. Memory Efficiency: You don't need to store a list of numbers; you only need to store one variable (self.ema).
+    3. Smoothness: It creates a smooth curve that isn't as sensitive to an old "outlier" dropping out of the window (a common issue with SMA).
+    """
+
+    def __init__(self, size: int):
+        self.size = size
+        self.alpha = 2 / (size + 1)
+        self.ema = None  # Initialized with the first value received
+
+    def next(self, val: int) -> float:
+        if self.ema is None:
+            # The first value acts as the starting point
+            self.ema = float(val)
+        else:
+            # Apply the EMA formula
+            self.ema = (val * self.alpha) + (self.ema * (1 - self.alpha))
+
+        return self.ema
diff --git a/datastructures/streams/moving_average/images/examples/moving_average_example_1.png b/datastructures/streams/moving_average/images/examples/moving_average_example_1.png
new file mode 100644
index 00000000..c27f7ae4
Binary files /dev/null and b/datastructures/streams/moving_average/images/examples/moving_average_example_1.png differ
diff --git a/datastructures/streams/moving_average/moving_average.py b/datastructures/streams/moving_average/moving_average.py
new file mode 100644
index 00000000..c4103890
--- /dev/null
+++ b/datastructures/streams/moving_average/moving_average.py
@@ -0,0 +1,34 @@
+from typing import Deque
+from collections import deque
+
+
+class MovingAverage:
+    def __init__(self, size: int):
+        """
+        Initializes the moving average object
+        Args:
+            size (int): The size of the moving average
+        """
+        self.queue: Deque[int] = deque()
+        self.size: int = size
+        self.window_sum: float = 0.0
+
+    def next(self, val: int) -> float:
+        """
+        Adds a value to the stream and returns the moving average of the stream
+        Args:
+            val (int): The value to add to the stream
+        Returns:
+            float: The moving average of the stream
+        """
+        if len(self.queue) == self.size:
+            # remove oldest value
+            oldest_value = self.queue.popleft()
+            self.window_sum -= oldest_value
+
+        # add new value to queue
+        self.queue.append(val)
+        self.window_sum += val
+
+        # calculate average
+        return self.window_sum / len(self.queue)
diff --git a/datastructures/streams/moving_average/moving_average_with_buffer.py b/datastructures/streams/moving_average/moving_average_with_buffer.py
new file mode 100644
index 00000000..83af742f
--- /dev/null
+++ b/datastructures/streams/moving_average/moving_average_with_buffer.py
@@ -0,0 +1,38 @@
+class MovingAverageWithBuffer:
+    """
+    Using a Circular Buffer (implemented with a fixed-size list) is an excellent way to optimize memory. Instead of
+    dynamically resizing or shifting elements, we use a fixed array and a pointer that "wraps around" using the modulo
+    operator (index(modsize)).
+
+    This approach is often preferred in embedded systems or high-performance scenarios because it avoids the overhead of
+     frequent memory allocations.
+
+     The expression self.head = (self.head + 1) % self.size ensures that if our size is 3, the pointer sequence will be:
+     0 → 1 → 2 → 0 → 1...
+
+     This effectively "recycles" the array positions, making it behave like a continuous loop.
+    """
+
+    def __init__(self, size: int):
+        # Pre-allocate a list of zeros
+        self.size = size
+        self.buffer = [0] * size
+        self.head = 0  # Pointer to the next position to overwrite
+        self.count = 0  # Track how many elements we've actually added
+        self.current_sum = 0.0
+
+    def next(self, val: int) -> float:
+        # If the buffer is full, subtract the value we are about to overwrite
+        if self.count == self.size:
+            self.current_sum -= self.buffer[self.head]
+        else:
+            self.count += 1
+
+        # Overwrite the old value at the head pointer
+        self.buffer[self.head] = val
+        self.current_sum += val
+
+        # Move the pointer to the next index, wrapping around if at the end
+        self.head = (self.head + 1) % self.size
+
+        return self.current_sum / self.count
diff --git a/datastructures/streams/moving_average/test_moving_average.py b/datastructures/streams/moving_average/test_moving_average.py
new file mode 100644
index 00000000..3d54bf77
--- /dev/null
+++ b/datastructures/streams/moving_average/test_moving_average.py
@@ -0,0 +1,40 @@
+import unittest
+from typing import List, Tuple
+from parameterized import parameterized
+from datastructures.streams.moving_average import MovingAverage
+from datastructures.streams.moving_average.moving_average_with_buffer import (
+    MovingAverageWithBuffer,
+)
+
+
+TEST_CASES = [
+    (3, [(1, 1.00000), (10, 5.50000), (3, 4.66667), (5, 6.00000)]),
+    (2, [(7, 7.00000), (14, 10.50000), (21, 17.50000), (28, 24.50000), (35, 31.50000)]),
+    (3, [(5, 5.00000), (10, 7.50000), (15, 10.00000), (20, 15.00000)]),
+    (10, [(1, 1.00000), (2, 1.50000)]),
+    (100, [(-100, -100.00000)]),
+]
+
+
+class MyTestCase(unittest.TestCase):
+    @parameterized.expand(TEST_CASES)
+    def test_moving_average(self, size: int, data_to_expected: List[Tuple[int, float]]):
+        moving_average = MovingAverage(size)
+        for data, expected in data_to_expected:
+            actual = moving_average.next(data)
+            round(actual, 5)
+            self.assertEqual(expected, round(actual, 5))
+
+    @parameterized.expand(TEST_CASES)
+    def test_moving_average_with_buffer(
+        self, size: int, data_to_expected: List[Tuple[int, float]]
+    ):
+        moving_average = MovingAverageWithBuffer(size)
+        for data, expected in data_to_expected:
+            actual = moving_average.next(data)
+            round(actual, 5)
+            self.assertEqual(expected, round(actual, 5))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/datastructures/streams/moving_average/weighted_moving_average.py b/datastructures/streams/moving_average/weighted_moving_average.py
new file mode 100644
index 00000000..a33dff68
--- /dev/null
+++ b/datastructures/streams/moving_average/weighted_moving_average.py
@@ -0,0 +1,46 @@
+class WeightedMovingAverage:
+    """
+    Weight Assignment: The most recent value always gets the maximum weight (self.size), and weights
+    decrease as we look further back in time.
+
+    The Denominator: Unlike the simple moving average where you divide by the count, here you divide by the sum of the
+    weights applied.
+
+    For a full window of size 3, weights are 3, 2, and 1. Sum = 6.
+    Before the window is full, we use a current_denominator to ensure accuracy.
+
+    The WMA is slightly more expensive (O(size) per next() call) because we have to re-sum the weighted values each time.
+    If you need O(1) performance for a weighted average, you might look into an Exponential Moving Average (EMA), which
+    uses a smoothing factor (α) to give more weight to recent data without needing to store the full history.
+    """
+
+    def __init__(self, size: int):
+        self.size = size
+        self.buffer = [0] * size
+        self.head = 0
+        self.count = 0
+        # The denominator is the sum of weights: 1 + 2 + ... + size
+        # Formula: (n * (n + 1)) / 2
+        self.denominator = (size * (size + 1)) / 2
+
+    def next(self, val: int) -> float:
+        # 1. Update the buffer
+        self.buffer[self.head] = val
+        self.head = (self.head + 1) % self.size
+        if self.count < self.size:
+            self.count += 1
+
+        # 2. Calculate Weighted Sum
+        weighted_sum = 0.0
+        current_denominator = 0
+
+        # Iterate backward from the most recent element
+        for i in range(self.count):
+            # Find index of elements from newest to oldest
+            # (self.head - 1 - i) handles the circular wrap-around
+            idx = (self.head - 1 - i) % self.size
+            weight = self.size - i
+            weighted_sum += self.buffer[idx] * weight
+            current_denominator += weight
+
+        return weighted_sum / current_denominator