How To Compute Moving Average Convergence Divergence (MACD) using Python?

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import pandas as pd


def compute_macd(data, short_window=12, long_window=26, signal_window=9):
    # Calculate short and long moving averages
    short_ema = data.ewm(span=short_window, adjust=False).mean()
    long_ema = data.ewm(span=long_window, adjust=False).mean()
    
    # Compute MACD line
    macd_line = short_ema - long_ema
    
    # Compute signal line
    signal_line = macd_line.ewm(span=signal_window, adjust=False).mean()
    
    # Compute MACD histogram
    macd_histogram = macd_line - signal_line
    
    return macd_line, signal_line, macd_histogram


# Example usage
# Assuming 'data' is a pandas DataFrame with a 'Close' column containing closing prices
# Replace 'data' with your actual data
macd_line, signal_line, macd_histogram = compute_macd(data['Close'])


# Print MACD line, Signal line, and MACD histogram
print("MACD Line:")
print(macd_line)
print("\nSignal Line:")
print(signal_line)
print("\nMACD Histogram:")
print(macd_histogram)

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import pandas as pd
import numpy as np

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def calculate_macd(data, short_period=12, long_period=26, signal_period=9):
    data['EMA_short'] = data['Close'].ewm(span=short_period, adjust=False).mean()
    data['EMA_long'] = data['Close'].ewm(span=long_period, adjust=False).mean()
    data['MACD'] = data['EMA_short'] - data['EMA_long']
    data['Signal'] = data['MACD'].ewm(span=signal_period, adjust=False).mean()
    data['Histogram'] = data['MACD'] - data['Signal']
    return data

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data = pd.read_csv('path/to/data.csv')

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data = calculate_macd(data)

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print(data[['Close', 'MACD', 'Signal', 'Histogram']])