Ethereum: calculating macd from scratch in python

Calculate MACD from scratch in Python

Developed by J. Ross Cameron and George C. Lapp, the Multi-Exponential Moving Average Convergence Divergence (MACD) algorithm is a popular technical indicator used in various financial markets to analyze trends and momentum. In this article, we will explore how to calculate MACD values ​​from scratch in Python.

What is MACD?

The MACD calculation includes two main components:

• EMA (Exponential Moving Average) of the HMA (Heavenly Proportional Moving Average)

• Convergence Line (C-Line)

The MACD calculation formula can be divided into several steps:

HMA Calculation

To calculate the HMA, we need to perform an exponential smoothing calculation on the closing price of the asset.

import numpy as np
def hma(prices, alpha):
"""
Calculate the exponential moving average (EMA) of the price list.
Parameter:
prices(list): List of closing prices.
alpha(float): EMA smoothing factor.
Returns:
list: List of EMA values.
"""
n = len(prices)
hma_values ​​​​​​​​= []
i in range(n):
hma_values.append(alpha  prices[i] + (1 - alpha) hma_values[-1])
return np.array(hma_values)
def hma smoothed(prices, alpha, window_size):
"""
Calculate the exponential moving average (EMA) of the price list using the moving average.
Parameter:
prices(list): List of closing prices.
alpha(float): EMA smoothing factor.
window_size (int): The size of the moving average window.
Returns:
list: A list of EMA values.
"""
n = len (prices)
hma_values ​​​​​​​​= hma(prices, alpha)
hma_values ​​​​​​​​= np.convolve(hma_values, np.ones(window_size) / window_size, mode='same')
returns hma_values

MACD Calculation

MACD calculation includes the following steps:

• Calculate the EMA of the HMA.

• Calculate the C-line by averaging the two EMA values.

“` python

def macd(prices, window_size):

“””

Calculate the Multi-Exponentential Moving Average Convergence Divergence (MACD) algorithm.

Parameter:

prices (list): List of closing prices.

window_size (int): Size of the MACD signal.

Return value:

list: List of MACD values.

“””

hma_values ​​​​​​​​= hma_smoothed(prices, 3, window_size)

ema smoothed_hma_values ​​​​​​​​= hma_smoothed(hma_values, 12, window_size)

ema_values ​​​​​​​​= ema_smoothed(ema_smoothed_hma_values, 26, window_size)

macd_values ​​​​​​​​= []

for i in range(len(ema_values) – window_size):

macd_values.append((ema_values[i] – ema smoothed_hma_values[i]) / ema smoothed_hma_values[i + window_size -1])

return np.array(macd_values)

def ema smoothed(prices, alpha, window_size):

“””

Calculate the exponential moving average (EMA) of a list of prices using a moving average.

Parameters:

prices (list): List of closing prices.

alpha (float): EMA smoothing factor.

window_size (int): Size of the moving average window.

Returns:

list: List of EMA values.

“””

n = len (prices)

hma_values ​​​​​​​​= hma(prices, alpha)

hma_values ​​​​​​​​= np.convolve(hma_values, np.ones(windowsize) / windowsize, mode=’equal’)

hma_values ​​​​​​​​= np.roll(hma_values, -window_size)

returns hma_values

def ema_smoothed(ema_values, Alpha, WindowSize):

“””

Calculate the exponential moving average (EMA) of a list of EMA values ​​using a moving average.

Parameters:

ema_values ​​​​​​​​(list): List of EMA values.

Alpha (Float): EMA smoothing factor.

Window size (Int): Size of the moving average window.

Returns:

List: List of smoothed EMA values.

“””

n = length(mother_values)

hma_values ​​​​​​​​= hma_smoothed(ema_values, 3, window_size)

hma_values ​​​​​​​​​​= np.convolve(hma_values, np.