Commodity Channel Index ( CCI ) Using Python

The CCI compares the current price to an average price over a period of time. The indicator fluctuates above or below zero, moving into positive or negative territory. While most values, approximately 75%, fall between -100 and +100, about 25% of the values fall outside this range, indicating a lot of weakness or strength in the price movement.

CCI is calculated with the following formula:

(Typical Price - Simple Moving Average) / (0.015 x Mean Deviation)

When the CCI is above +100, this means the price is well above the average price as measured by the indicator. When the indicator is below -100, the price is well below the average price.

A basic CCI strategy is used to track the CCI for movement above +100, which generates buy signals, and movements below -100, which generates sell or short trade signals.

Investors may only want to take the buy signals, exit when the sell signals occur, and then re-invest when the buy signal occurs again.

Here I've used Python to build a CCI indicator. 

# Load the necessary packages and modules

from pandas_datareader import data as pdr

import matplotlib.pyplot as plt

import yfinance

import pandas as pd

# Commodity Channel Index 

def CCI(data, ndays): 

    TP = (data['High'] + data['Low'] + data['Close']) / 3 

    CCI = pd.Series((TP - TP.rolling(ndays).mean()) / (0.015 * TP.rolling(ndays).std()),

                    name = 'CCI') 

    data = data.join(CCI) 

    return data

# Retrieve the Nifty data from Yahoo finance:

data = pdr.get_data_yahoo("^NSEI", start="2018-01-01", end="2021-01-24") 

data = pd.DataFrame(data)

# Compute the Commodity Channel Index(CCI) for NIFTY based on the 20-day Moving average

n = 20

NIFTY_CCI = CCI(data, n)  

CCI = NIFTY_CCI['CCI']

# Plotting the Price Series chart and the Commodity Channel index below

fig = plt.figure(figsize=(10,8))

ax = fig.add_subplot(2, 1, 1)

ax.set_xticklabels([])

plt.plot(data['Close'],lw=1)

plt.title('NSE Price Chart')

plt.ylabel('Close Price')

plt.grid(True)

bx = fig.add_subplot(2, 1, 2)

plt.plot(CCI,'k',lw=0.75,linestyle='-',label='CCI')

plt.legend(loc=2,prop={'size':9.5})

plt.ylabel('CCI values')

plt.grid(True)