financial.exponential_moving_average
====================================

.. py:module:: financial.exponential_moving_average

.. autoapi-nested-parse::

   Calculate the exponential moving average (EMA) on the series of stock prices.
   Wikipedia Reference: https://en.wikipedia.org/wiki/Exponential_smoothing
   https://www.investopedia.com/terms/e/ema.asp#toc-what-is-an-exponential
   -moving-average-ema

   Exponential moving average is used in finance to analyze changes stock prices.
   EMA is used in conjunction with Simple moving average (SMA), EMA reacts to the
   changes in the value quicker than SMA, which is one of the advantages of using EMA.



Attributes
----------

.. autoapisummary::

   financial.exponential_moving_average.stock_prices


Functions
---------

.. autoapisummary::

   financial.exponential_moving_average.exponential_moving_average


Module Contents
---------------

.. py:function:: exponential_moving_average(stock_prices: collections.abc.Iterator[float], window_size: int) -> collections.abc.Iterator[float]

   Yields exponential moving averages of the given stock prices.
   >>> tuple(exponential_moving_average(iter([2, 5, 3, 8.2, 6, 9, 10]), 3))
   (2, 3.5, 3.25, 5.725, 5.8625, 7.43125, 8.715625)

   :param stock_prices: A stream of stock prices
   :param window_size: The number of stock prices that will trigger a new calculation
                       of the exponential average (window_size > 0)
   :return: Yields a sequence of exponential moving averages

   Formula:

   st = alpha * xt + (1 - alpha) * st_prev

   Where,
   st : Exponential moving average at timestamp t
   xt : stock price in from the stock prices at timestamp t
   st_prev : Exponential moving average at timestamp t-1
   alpha : 2/(1 + window_size) - smoothing factor

   Exponential moving average (EMA) is a rule of thumb technique for
   smoothing time series data using an exponential window function.


.. py:data:: stock_prices
   :value: [2.0, 5, 3, 8.2, 6, 9, 10]