What is the procedure for incorporating RSI Divergence in Python?

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To incorporate RSI divergence in Python, you can follow the below steps:

1. Import the necessary libraries like pandas, numpy, matplotlib and talib.

2. Load the data from your chosen source using pandas.

3. Calculate the RSI values using talib.

4. Create a new column in the dataframe to store the RSI values.

5. Use matplotlib to plot the RSI values to visualize the trend.

6. Apply a divergence formula to detect divergences between the price and RSI using numpy.

7. Identify the divergences and create a new column in the dataframe to store the signals.

8. Plot the signals on the chart using matplotlib.

9. Use the signals to take trading decisions.

Here is some sample code to help you get started:

# Importing the necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import talib as ta

# Loading the data
data = pd.read_csv('stock_data.csv')

# Calculating the RSI values
data['RSI'] = ta.RSI(data['Close'], timeperiod=14)

# Plotting the RSI values
plt.plot(data['RSI'])

# Calculating the divergence
divergence = np.diff(data['RSI'])

# Identifying the divergences
data['Divergence'] = np.where(divergence != np.roll(divergence, 1), 1, 0)

# Plotting the signals
plt.plot(data.loc[data['Divergence'] == 1].index, data['RSI'][data['Divergence'] == 1], 'bo')

# Taking trading decisions based on the signals

Note: This is just an example code and may not work for all scenarios. It is important to test and customize the code as per your requirements.