diff --git a/example/Example-Analysis.py b/example/Example-Analysis.py
index ba0f4682..b9fa5ef3 100644
--- a/example/Example-Analysis.py
+++ b/example/Example-Analysis.py
@@ -309,3 +309,46 @@
 # <codecell>
 
 print(pf.data.loc[pf.data.index.year == 2017].head(3))
+
+# <markdowncell>
+
+# ## Momentum Indicators
+# `FinQuant` provides a module `finquant.momentum_indicators` to compute and
+# visualize a number of momentum indicators. Currently RSI (Relative Strength Index)
+# and MACD (Moving Average Convergence Divergence) indicators are available.
+# See below.
+
+# <codecell>
+# plot the RSI (Relative Strength Index) for disney stock proces
+from finquant.momentum_indicators import relative_strength_index as rsi
+
+# get stock data for disney
+dis = pf.get_stock("WIKI/DIS").data.copy(deep=True)
+
+# plot RSI - by default this plots RSI against the price in two graphs
+rsi(dis)
+plt.show()
+
+# plot RSI with custom arguments
+rsi(dis, oversold = 20, overbought = 80)
+plt.show()
+
+# plot RSI standalone graph
+rsi(dis, oversold = 20, overbought = 80, standalone=True)
+plt.show()
+
+# <codecell>
+# plot MACD for disney stock proces
+from finquant.momentum_indicators import macd
+
+# plot MACD - by default this plots RSI against the price in two graphs
+macd(dis)
+plt.show()
+
+# plot MACD using custom arguments
+macd(dis, longer_ema_window = 30, shorter_ema_window = 15, signal_ema_window = 10)
+plt.show()
+
+# plot MACD standalone graph
+macd(standlone = True)
+plt.show()
diff --git a/tests/test_momentum_indicators.py b/tests/test_momentum_indicators.py
new file mode 100644
index 00000000..f3b091f6
--- /dev/null
+++ b/tests/test_momentum_indicators.py
@@ -0,0 +1,110 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+from finquant.momentum_indicators import (
+    relative_strength_index as rsi,
+    macd,
+)
+
+plt.switch_backend("Agg")
+
+def test_rsi():
+    x = np.sin(np.linspace(1, 10, 100))
+    xlabel_orig = "Date"
+    ylabel_orig = "Price"
+    df = pd.DataFrame({"Stock": x}, index=np.linspace(1, 10, 100))
+    df.index.name = "Date"
+    rsi(df)
+    # get data from axis object
+    ax = plt.gca()
+    # ax.lines[0] is the data we passed to plot_bollinger_band    
+    line1 = ax.lines[0]
+    stock_plot = line1.get_xydata()
+    xlabel_plot = ax.get_xlabel()
+    ylabel_plot = ax.get_ylabel()
+    # tests
+    assert (df['Stock'].index.values == stock_plot[:, 0]).all()
+    assert (df["Stock"].values == stock_plot[:, 1]).all()
+    assert xlabel_orig == xlabel_plot
+    assert ylabel_orig == ylabel_plot
+    
+def test_rsi_standalone():
+    x = np.sin(np.linspace(1, 10, 100))
+    xlabel_orig = "Date"
+    ylabel_orig = "RSI"
+    labels_orig = ['rsi']
+    title_orig = 'RSI Plot'
+    df = pd.DataFrame({"Stock": x}, index=np.linspace(1, 10, 100))
+    df.index.name = "Date"
+    rsi(df, standalone=True)
+    # get data from axis object
+    ax = plt.gca()
+    # ax.lines[2] is the RSI data
+    line1 = ax.lines[2]
+    rsi_plot = line1.get_xydata()
+    xlabel_plot = ax.get_xlabel()
+    ylabel_plot = ax.get_ylabel()
+    print (xlabel_plot, ylabel_plot)    
+    # tests
+    assert (df['rsi'].index.values == rsi_plot[:, 0]).all()
+    # for comparing values, we need to remove nan
+    a, b = df['rsi'].values, rsi_plot[:, 1]
+    a, b = map(lambda x: x[~np.isnan(x)], (a, b))
+    assert (a == b).all()
+    labels_plot = ax.get_legend_handles_labels()[1]
+    title_plot = ax.get_title()
+    assert labels_plot == labels_orig
+    assert xlabel_plot == xlabel_orig
+    assert ylabel_plot == ylabel_orig    
+    assert title_plot == title_orig
+
+def test_macd():
+    x = np.sin(np.linspace(1, 10, 100))
+    xlabel_orig = "Date"
+    ylabel_orig = "Price"
+    df = pd.DataFrame({"Stock": x}, index=np.linspace(1, 10, 100))
+    df.index.name = "Date"
+    macd(df)
+    # get data from axis object
+    ax = plt.gca()
+    # ax.lines[0] is the data we passed to plot_bollinger_band    
+    line1 = ax.lines[0]
+    stock_plot = line1.get_xydata()
+    xlabel_plot = ax.get_xlabel()
+    ylabel_plot = ax.get_ylabel()
+    # tests
+    assert (df['Stock'].index.values == stock_plot[:, 0]).all()
+    assert (df["Stock"].values == stock_plot[:, 1]).all()
+    assert xlabel_orig == xlabel_plot
+    assert ylabel_orig == ylabel_plot
+    
+def test_macd_standalone():
+    labels_orig = ['MACD', 'diff', 'SIGNAL']    
+    x = np.sin(np.linspace(1, 10, 100))
+    xlabel_orig = "Date"
+    ylabel_orig = "MACD"
+    df = pd.DataFrame({"Stock": x}, index=np.linspace(1, 10, 100))
+    df.index.name = "Date"
+    macd(df, standalone=True)
+    # get data from axis object
+    ax = plt.gca()
+    labels_plot = ax.get_legend_handles_labels()[1]
+    xlabel_plot = ax.get_xlabel()
+    ylabel_plot = ax.get_ylabel()
+    assert labels_plot == labels_orig
+    assert xlabel_plot == xlabel_orig
+    assert ylabel_plot == ylabel_orig    
+    # ax.lines[0] is macd data
+    # ax.lines[1] is diff data
+    # ax.lines[2] is macd_s data
+    # tests
+    for i, key in ((0, 'macd'), (1, 'diff'), (2, 'macd_s')):
+        line = ax.lines[i]
+        data_plot = line.get_xydata()
+        # tests
+        assert (df[key].index.values == data_plot[:, 0]).all()
+        # for comparing values, we need to remove nan
+        a, b = df[key].values, data_plot[:, 1]
+        a, b = map(lambda x: x[~np.isnan(x)], (a, b))
+        assert (a == b).all()