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Pandas: Calculate the Relative Strength Index (RSI) on a Stock

What is the Relative Strength Index?

The Relative Strength Index (RSI) on a stock is a technical indicator.

The relative strength index (RSI) is a momentum indicator used in technical analysis that measures the magnitude of recent price changes to evaluate overbought or oversold conditions in the price of a stock or other asset. 

https://www.investopedia.com/terms/r/rsi.asp

A technical indicator is a mathematical calculation based on past prices and volumes of a stock. The RSI has a value between 0 and 100. It is said to be overbought if above 70, and oversold if below 30.

Step 1: How to calculate the RSI

To be quite honest, I found the description on investopedia.org a bit confusing. Therefore I went for the Wikipedia description of it. It is done is a couple of steps, so let us do the same.

  1. If previous price is lower than current price, then set the values.
    • U = close_now – close_previous
    • D = 0
  2. While if the previous price is higher than current price, then set the values
    • U = 0
    • D = close_previous – close_now
  3. Calculate the Smoothed or modified moving average (SMMA) or the exponential moving average (EMA) of D and U. To be aligned with the Yahoo! Finance, I have chosen to use the (EMA).
  4. Calculate the relative strength (RS)
    • RS = EMA(U)/EMA(D)
  5. Then we end with the final calculation of the Relative Strength Index (RSI).
    • RSI = 100 – (100 / (1 – RSI))

Notice that the U are the price difference if positive otherwise 0, while D is the absolute value of the the price difference if negative.

Step 2: Get a stock and calculate the RSI

We will use the Pandas-datareader to get some time series data of a stock. If you are new to using Pandas-datareader we advice you to read this tutorial.

In this tutorial we will use Twitter as an examples, which has the TWTR ticker. It you want to do it on some other stock, then you can look up the ticker on Yahoo! Finance here.

Then below we have the following calculations.

import pandas_datareader as pdr
import datetime as dt


ticker = pdr.get_data_yahoo("TWTR", dt.datetime(2020,1,1), dt.datetime.now())

delta = ticker['Close'].diff()
up = delta.clip(lower=0)
down = -1*delta.clip(upper=0)
ema_up = up.ewm(com=13, adjust=False).mean()
ema_down = down.ewm(com=13, adjust=False).mean()
rs = ema_up/ema_down

print(ticker)

To have a naming that is close to the definition and also aligned with Python, we use up for U and down for D.

This results in the following output.

                 High        Low       Open  ...    Volume  Adj Close        RSI
Date                                         ...                                
2020-01-02  32.500000  31.959999  32.310001  ...  10721100  32.299999        NaN
2020-01-03  32.099998  31.260000  31.709999  ...  14429500  31.520000   0.000000
2020-01-06  31.709999  31.160000  31.230000  ...  12582500  31.639999   1.169582
2020-01-07  32.700001  31.719999  31.799999  ...  13712900  32.540001   9.699977
2020-01-08  33.400002  32.349998  32.349998  ...  14632400  33.049999  14.218360
...               ...        ...        ...  ...       ...        ...        ...
2020-08-11  39.000000  36.709999  37.590000  ...  20486000  37.279999  58.645030
2020-08-12  38.000000  36.820000  37.500000  ...  11013300  37.439999  59.532873
2020-08-13  38.270000  37.369999  37.430000  ...  13259400  37.820000  61.639293
2020-08-14  37.959999  37.279999  37.740002  ...  10377300  37.900002  62.086731
2020-08-17  38.090000  37.270000  37.950001  ...  10186900  37.970001  62.498897

This tutorial was written 2020-08-18, and comparing with the RSI for twitter on Yahoo! Finance.

From Yahoo! Finance on Twitter with RSI

As you can see in the lower left corner, the RSI for the same ending day was 62.50, which fits the calculated value. Further checks reveal that they also fit the values of Yahoo.

Step 3: Visualize the RSI with the daily stock price

We will use the matplotlib library to visualize the RSI with the stock price. In this tutorial we will have two rows of graphs by using the subplots function. The function returns an array of axis (along with a figure, which we will not use).

The axis can be parsed to the Pandas DataFrame plot function.

import pandas_datareader as pdr
import datetime as dt
import matplotlib.pyplot as plt


ticker = pdr.get_data_yahoo("TWTR", dt.datetime(2019,1,1), dt.datetime.now())

delta = ticker['Close'].diff()
up = delta.clip(lower=0)
down = -1*delta.clip(upper=0)
ema_up = up.ewm(com=13, adjust=False).mean()
ema_down = down.ewm(com=13, adjust=False).mean()
rs = ema_up/ema_down

ticker['RSI'] = 100 - (100/(1 + rs))

# Skip first 14 days to have real values
ticker = ticker.iloc[14:]

print(ticker)
fig, (ax1, ax2) = plt.subplots(2)
ax1.get_xaxis().set_visible(False)
fig.suptitle('Twitter')

ticker['Close'].plot(ax=ax1)
ax1.set_ylabel('Price ($)')
ticker['RSI'].plot(ax=ax2)
ax2.set_ylim(0,100)
ax2.axhline(30, color='r', linestyle='--')
ax2.axhline(70, color='r', linestyle='--')
ax2.set_ylabel('RSI')

plt.show()

Also, we we remove the x-axis of the first graph (ax1). Adjust the y-axis of the second graph (ax2). Also, we have set two horizontal lines to indicate overbought and oversold at 70 and 30, respectively. Notice, that Yahoo! Finance use 80 and 20 as indicators by default.

The resulting output.