How to Calculate RSI based on SMMA in Python

To calculate the Relative Strength Index (RSI) based on the Smoothed Moving Average (SMMA) in Python, you can use the following formula:

RSI = 100 – (100 / (1 + RS))

where RS is the average of the up periods divided by the average of the down periods.

Now, let’s dive into the details of how to calculate RSI based on SMMA in Python.

Relative Strength Index (RSI) is a popular technical indicator used by traders to identify overbought or oversold conditions in the market. It is calculated based on the average gain and average loss over a specified period.

Smoothed Moving Average (SMMA) is a type of moving average that assigns more weight to the recent data points, making it more responsive to price changes.

To calculate RSI based on SMMA in Python, you can follow these steps:

1. Import the necessary libraries:

import pandas as pd
import numpy as np

2. Define a function to calculate SMMA:

def smma(data, period):
return data.ewm(span=period, adjust=False).mean()

3. Define a function to calculate RSI based on SMMA:

def rsi_smma(data, period):
    delta = data.diff()
    gain = (delta.where(delta > 0, 0)).ewm(span=period, adjust=False).mean()
    loss = (-delta.where(delta < 0, 0)).ewm(span=period, adjust=False).mean()
    rs = gain / loss
    rsi = 100 - (100 / (1 + rs))
    return rsi

4. Apply the function to your data:

# Load your data into a DataFrame
data = pd.read_csv('your_data.csv')

# Calculate RSI based on SMMA with a period of 14
data['RSI'] = rsi_smma(data['Close'], 14)

# Print the RSI values
print(data['RSI'])

By following these steps, you can calculate RSI based on SMMA in Python and use it to make more informed trading decisions. Remember to adjust the period parameter based on your trading strategy and the timeframe of your data. Happy coding!”