diff --git a/stock_market_cli.py b/stock_market_cli.py
index fc4f1ebef0c..9f5ad7a5161 100644
--- a/stock_market_cli.py
+++ b/stock_market_cli.py
@@ -65,11 +65,12 @@ def print_help(s_ticker, s_start, s_interval, b_is_market_open):
         print("   sma         simple moving average")
         print("   ema         exponential moving average")
         print("   macd        moving average convergence/divergence")
-        if s_interval != "1440min":
-            print("   vwap        volume weighted average price")
         print("   stoch       stochastic oscillator")
         print("   rsi         relative strength index")
         print("   adx         average directional movement index")
+        print("   cci         commodity channel index")
+        if s_interval != "1440min":
+            print("   vwap        volume weighted average price")
 
         print("\nPrediction:")
         print("   ma")
@@ -114,7 +115,7 @@ def main():
     # Add list of arguments that the main parser accepts
     main_parser.add_argument('cmd', choices=['quit', 'help', 'gainers' ,'view', 'load', 'clear', 
                                              'sma', 'ema', 'macd', 'vwap', 'stoch', 'rsi', 'adx',
-                                             'ratings'])
+                                             'cci', 'ratings'])
 
     # Print first welcome message and help
     print("\nWelcome to Didier's Stock Market Bot\n")
@@ -217,6 +218,11 @@ def main():
             smta.adx(l_args, s_ticker, s_interval, df_stock)
             continue
 
+        # ---------------------------------------------------- CCI ----------------------------------------------------
+        elif ns_known_args.cmd == 'cci':
+            smta.cci(l_args, s_ticker, s_interval, df_stock)
+            continue
+
         # --------------------------------------------------------------------------------------------------------------
         # ------------------------------------------------ PREDICTION --------------------------------------------------
         # --------------------------------------------------------------------------------------------------------------
diff --git a/stock_market_technical_analysis.py b/stock_market_technical_analysis.py
index c3c57c29ae9..1e13df3f3f6 100644
--- a/stock_market_technical_analysis.py
+++ b/stock_market_technical_analysis.py
@@ -265,3 +265,38 @@ def adx(l_args, s_ticker, s_interval, df_stock):
         return
 
 
+# ----------------------------------------------------- CCI -----------------------------------------------------
+def cci(l_args, s_ticker, s_interval, df_stock):
+    parser = argparse.ArgumentParser(prog='cci', 
+                                     description=""" The CCI is designed to detect beginning and ending market trends. 
+                                     The range of 100 to -100 is the normal trading range. CCI values outside of this 
+                                     range indicate overbought or oversold conditions. You can also look for price 
+                                     divergence in the CCI. If the price is making new highs, and the CCI is not, 
+                                     then a price correction is likely. """)
+
+    parser.add_argument('-p', "--timeperiod", action="store", dest="n_timeperiod", type=check_positive, default=60,
+                        help='Number of data points used to calculate each ADX value')
+
+    try:
+        (ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
+    except SystemExit:
+        print("")
+        return
+    
+    if l_unknown_args:
+        print(f"The following args couldn't be interpreted: {l_unknown_args}")
+
+    try:
+        # Daily
+        if s_interval == "1440min":
+            df_ta = ta.cci(high=df_stock['2. high'], low=df_stock['3. low'],
+                           close=df_stock['5. adjusted close'], time_period=ns_parser.n_timeperiod).dropna()
+        # Intraday 
+        else:
+            df_ta = ta.cci(high=df_stock['2. high'], low=df_stock['3. low'],
+                           close=df_stock['4. close'], time_period=ns_parser.n_timeperiod).dropna()
+
+        plot_ta(s_ticker, df_ta, f"{ns_parser.n_timeperiod} CCI")
+    except:
+        print("")
+        return
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