代码拉取完成,页面将自动刷新
"""
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.
"""
from collections.abc import Iterator
def exponential_moving_average(
stock_prices: Iterator[float], window_size: int
) -> 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.
"""
if window_size <= 0:
raise ValueError("window_size must be > 0")
# Calculating smoothing factor
alpha = 2 / (1 + window_size)
# Exponential average at timestamp t
moving_average = 0.0
for i, stock_price in enumerate(stock_prices):
if i <= window_size:
# Assigning simple moving average till the window_size for the first time
# is reached
moving_average = (moving_average + stock_price) * 0.5 if i else stock_price
else:
# Calculating exponential moving average based on current timestamp data
# point and previous exponential average value
moving_average = (alpha * stock_price) + ((1 - alpha) * moving_average)
yield moving_average
if __name__ == "__main__":
import doctest
doctest.testmod()
stock_prices = [2.0, 5, 3, 8.2, 6, 9, 10]
window_size = 3
result = tuple(exponential_moving_average(iter(stock_prices), window_size))
print(f"{stock_prices = }")
print(f"{window_size = }")
print(f"{result = }")
此处可能存在不合适展示的内容,页面不予展示。您可通过相关编辑功能自查并修改。
如您确认内容无涉及 不当用语 / 纯广告导流 / 暴力 / 低俗色情 / 侵权 / 盗版 / 虚假 / 无价值内容或违法国家有关法律法规的内容,可点击提交进行申诉,我们将尽快为您处理。