Ethereum: calculating macd from scratch in python

MACD in Python calculated from scratch

The algorithm (Multi-Exponential Moving Average Convergence Divergence) developed by J. Ross Cameron and George C. Lapp is a popular technical indicator that is used in various financial markets to analyze the trend and dynamics. In this article we will examine how the MACD values ​​are calculated from scratch using Python.

What is MacD?

The MACD calculation includes two main components:

• The EMA (exponential sliding average) of the HMA (heavenly proportional moving average)

• The convergence line (C-line)

The formula for calculating MACD can be divided into several steps:

HMA calculation

In order to calculate the HMA, we have to carry out an exponential smoothing calculation for the closing course of the financial value.

`Python

Import nump as an NP

Def HMA (prices, alpha):

"" ""

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

Parameter:

Prices (list): List of final prices.

Alpha (Float): EMA smoothing factor.

Returns:

List: List of EMA values.

"" ""

n = len (prices)

Hma_values ​​= []

for i within reach (s):

Hma_values.append (alpha prices [i] + (1 - alpha) hma_values ​​[-1])

Return np.array (hma_values)

Def HMA smoothes (prices, alpha, Window_Size):

"" ""

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

Parameter:

Prices (list): List of final prices.

Alpha (Float): EMA smoothing factor.

Window_Size (Int): Size of the sliding average window.

Returns:

List: 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 = 'equal')))

Gives HMA_Values ​​back

MACD calculation

The MACD calculation includes the following steps:

• Calculate the EMA of the HMA.

• Calculate the C line by average the two EMA values.

“ Python

DEF MACD (prices, Window_Size):

“” “”

Calculate the multi-exponential gliding average convergence-diversity algorithm (MACD).

Parameter:

Prices (list): List of final prices.

Window_Size (Int): Size of the MacD signal.

Returns:

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 within reach (len (eMa_values) – Wester_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 smoothes (prices, alpha, Window_Size):

“” “”

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

Parameter:

Prices (list): List of final prices.

Alpha (Float): EMA smoothing factor.

Window_Size (Int): Size of the sliding average window.

Returns:

List: 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 = ‘equal’)))

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

Gives HMA_Values ​​back

DEF EMA_SMOOTED (EMA_Values, Alpha, Window_Size):

“” “”

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

Parameter:

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

Alpha (Float): EMA smoothing factor.

Window_Size (Int): Size of the sliding average window.

Returns:

List: List of the smoothed EMA values.

“” “”

n = len (ema_values)

Hma_values ​​= hma_smoothed (EMA_Values, 3, Window_Size)

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

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