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.
- If previous price is lower than current price, then set the values.
- U = close_now – close_previous
- D = 0
- While if the previous price is higher than current price, then set the values
- U = 0
- D = close_previous – close_now
- 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).
- Calculate the relative strength (RS)
- RS = EMA(U)/EMA(D)
- 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
from datetime import datetime
ticker = pdr.get_data_yahoo("TWTR", datetime(2020, 1, 1))
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 Close Volume Adj Close
Date
2020-01-02 32.500000 31.959999 32.310001 32.299999 10721100 32.299999
2020-01-03 32.099998 31.260000 31.709999 31.520000 14429500 31.520000
2020-01-06 31.709999 31.160000 31.230000 31.639999 12582500 31.639999
2020-01-07 32.700001 31.719999 31.799999 32.540001 13712900 32.540001
2020-01-08 33.400002 32.349998 32.349998 33.049999 14632400 33.049999
... ... ... ... ... ... ...
2021-10-08 64.339996 63.310001 64.250000 63.680000 8094900 63.680000
2021-10-11 63.509998 62.070000 62.990002 62.099998 9020400 62.099998
2021-10-12 62.799999 60.790001 61.680000 61.450001 9952100 61.450001
2021-10-13 62.740002 61.509998 61.959999 62.200001 9423500 62.200001
2021-10-14 63.779999 62.759998 63.009998 63.130001 3455733 63.130001
This tutorial was written 2020-08-18 (updated in 2021-10-14), and comparing with the RSI for twitter on Yahoo! Finance.
As you can see in the lower left corner, the RSI for the same ending day was 51.56 (it was measured in trading hours, so the end-of-day number is different), 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
from datetime import datetime
import matplotlib.pyplot as plt
ticker = pdr.get_data_yahoo("TWTR", datetime(2020, 1, 1))
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.
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i think there is a basic typo / error in the RSI formula:
instead of:
RSI = 100 – (100 / (1 – RSI))
it should be:
RSI = 100 – (100 / (1 + RSI))
Hi,
Yes, I can see that. Thank you for letting me know. I have updated it now.
Cheers,
Rune
Thanks Rune, fantastic job! I’d like to develop a Strategic (backtesting) where we buy in 30 and sell when go down 70, but I’m not getting😔. Could you give me the key to get it?
Hi Hugo,
Great question.
If I understand you want to create a “Signal” when to BUY, SELL, or KEEP.
This can be done as follows:
ticker['Signal'] = 'KEEP'ticker.loc[ticker['RSI'] > 70, 'Signal'] = 'SELL'
ticker.loc[ticker['RSI'] < 30, 'Signal'] = 'BUY'
Cheers,
Rune
Hello
I like your RSI calculations
Python has changed some syntax since 8 months ago
Is there a possibility that you can update the code?
I need everything but the chart calculation
How many rest charts can I do in one excel sheet. Thanks. harrycbailey@sbcglobal.net. Thx harry
Hi Harry,
I just went through all calculations with the newest pandas updates. I have also updated with the numbers from today.
It should be working.
If it does not work for your please provide the versions of Python, pandas, pandas_datareader and matplotlib.
Cheers, Rune
Hello Rune. I am still with you working hard trying to figure it out
my first mistake was not entering this code in Jupyter! (import pandas_datareader as pdr)
I have made progress!!. able to move past getting access to yahoo finance
Python version is 3.9
now having difficulity in this area
hpq [‘delta’] = delta = hpq [‘close’] .diff()
hpq [‘up’] = up = delta. clip (lower=0)
hpq [‘down’] = down = -1*delta.clip(upper=0)
and
Calculate the relative strength (RS)
RS = EMA(U)/EMA(D)
Then we end with the final calculation of the Relative Strength Index (RSI).
RSI = 100 – (100 / (1 + RSI))
I am i to assume the twitter example is the correct code?
Like your tutorials !!!!!!!
If you share what the errors you get – it will be easier to help.
First
i need to know how you reload the data. You dont explain how to do this.
Problem: my
Date delta heading UP, DOWN, RSI are the same values
(NaN) (NaN) (NaN) (NaN)
0.050001 0.050001 -0.0 100.0
0.160000 0.16000 -0.0 100.0
0.219999 0.219999 -0.0 100.
Second these are my steps i followed
hpq[‘delta’] = delta = hpq[‘Close’].diff()
hpq[‘up’] = up = delta.clip(lower=0)
hpq[‘down’] = 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
hpq [‘RSI’] = 100 – (100/(1 + rs))
hpq. head()
Thanks
I know i made a stupid mistake
I follow you comments and answer in the end.
GOT IF FINALLY I THINK
Okay.
OK this is Embarrassing. when i thought I finally got it I had no errors. I tried again I have all of these errors. I also cut and pasted your code and I still had issues also i would like to insert
ticker[‘Signal’] = ‘KEEP’
ticker.loc[hpqr[‘RSI’] > 70, ‘Signal’] = ‘SELL’
ticker.loc[hpq[‘RSI’] < 30, 'Signal'] = 'BUY'
————————————————————————-
hpq = pdr.get_data_yahoo("hpq", dt.datetime(2018,1,2))
hpq.index = hpq.index.date
hpq.head()
ERROR IS
NameError Traceback (most recent call last)
in
—-> 1 hpq = pdr.get_data_yahoo(“hpq”, dt.datetime(2018,1,2))
2 hpq.index = hpq.index.date
3 hpq.head()
NameError: name ‘pdr’ is not defined
—————————————————–
delta = hpq[‘Close’].diff()
up = delta.clip(lower=0)
down = -1*delta.clip(upper=0)
ERROR IS
NameError Traceback (most recent call last)
in
—-> 1 delta = hpq[‘Close’].diff()
2 up = delta.clip(lower=0)
3 down = -1*delta.clip(upper=0)
NameError: name ‘hpq’ is not defined
————————————————————
ema_up = up.ewm(com=13, adjust=False).mean()
ema_down = down.ewm(com=13, adjust=False).mean()
ERROR IS
NameError Traceback (most recent call last)
in
—-> 1 ema_up = up.ewm(com=13, adjust=False).mean()
2 ema_down = down.ewm(com=13, adjust=False).mean()
NameError: name ‘up’ is not defined
—————————————————————-
rs = ema_up/ema_down
hpq[‘RSI’] = 100 -(100/(1+rs))
ERROR IS
NameError Traceback (most recent call last)
in
—-> 1 rs = ema_up/ema_down
2 hpq[‘RSI’] = 100 -(100/(1+rs))
NameError: name ’ema_up’ is not defined
—————————————————————
# Skip first 14 days to have real values
hpq = hpq.iloc[14:]
hpq.head()
ERROR IS
NameError Traceback (most recent call last)
in
1 # Skip first 14 days to have real values
—-> 2 hpq = hpq.iloc[14:]
3 hpq.head()
NameError: name ‘hpq’ is not defined
—————————————————————
# Create a Pandas Excel writer using XLsWriter
excel_file = ‘output.xlsx’
sheet_name = ‘hpq’
writer = pd.ExcelWriter(excel_file, engine=’xlsxwriter’)
hpq[14:].to_excel(writer, sheet_hpq_sheet_hpq)
ERROR IS
NameError Traceback (most recent call last)
in
2 excel_file = ‘output.xlsx’
3 sheet_name = ‘hpq’
—-> 4 writer = pd.ExcelWriter(excel_file, engine=’xlsxwriter’)
5 hpq[14:].to_excel(writer, sheet_hpq_sheet_hpq)
NameError: name ‘pd’ is not defined
——————————————————————————
# Access the XlsWriter wookbook and worksheet objects from dataframe
workbook = writer.book
worksheet = writer.sheets[sheet_hpq]
ERROR IS
NameError Traceback (most recent call last)
in
1 # Access the XlsWriter wookbook and worksheet objects from dataframe
—-> 2 workbook = writer.book
3 worksheet = writer.sheets[sheet_hpq]
NameError: name ‘writer’ is not defined
————————————————————
i discovered thes errors when I tried to go to excel
I am happy to dontate to your favorite charity
thanks
Hope you figure it out.
StoP!! I believe i worked the proper code out. no errors I am happy and I learned a lot Thank you!!!
I need to know where to insert this code please
hpq[‘Signal’] = ‘KEEP’
hpq.loc[ticker[‘RSI’] > 70, ‘Signal’] = ‘SELL’
hpq.loc[ticker[‘RSI’] > If i decide to run another stock, will the different stocks be on the same spreadsheet in different worksheets?
Thanks so much!!!
Yes you want to make a signal line:
hpq[‘Signal sell’] = ticker[‘RSI’] > 70
This will create true when sell
Similarly you can make one for buy.
I think this is the easiest way to do it.
this is the last part of the code
# Close and save the Excel file
writer.save()
I hit RUN and the excel chart did not open
I had a excel page open with HPQ as the name
If you want it to excel.
Assuming you have it in hpq.
hpq.to_excel(‘hpq.xlsx’)
This should be the last thing you do.
Then it writes an excel sheet hpq.xlsx in the current folder.
# Close and save the Excel file
writer.save()
another warning
:\Users\Harry C Bailey\anaconda3\lib\site-packages\xlsxwriter\workbook.py:336: UserWarning: Calling close() on already closed file.
warn(“Calling close() on already closed file.”)
Try to see previous comment.
I quit. I wil use excel
See how to do it in previous comment.
Dear Rune. If I calculate the RSI using these two scripts, it doesn’t give me the same result as yours.
Using the TA-lib Python wrapper:
import talib as ta
ta.RSI (df [‘close’], timeperiod = 13)
Using the pandas-ta library:
import pandas_ta as pta
pta.rsi (df [‘close’], length = 13)
What can be the cause? Thanks for your practical and pedagogical scripts.
That’s a very good question. I have followed the formula from investopedia and it firs the figures from Yahoo! Finance.
I would have to investigate it a bit further.
Hi, Rune. Thanks for the great work. I was just curious why for the RSI calculation you use n-1 (for n=14 days): ema_up = up.ewm(com=13, adjust=False).mean() but for your MACD calculation you use n (for n=12 days): exp1 = ticker.ewm(span=12, adjust=False).mean(). I thought they would both be the same since you use ewm.
Hi SSG,
Great questions.
There are different ways to set the decay in the EWM (see docs: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.ewm.html)
In this one I use the com (decay in terms of center of mass). The comparison with Yahoo! Finance is done with 14 days (you can specify the value as you please), but to use the com you need to set it one less (see formula for com in docs).
For the MACD I use the span, which works a bit different. Again, you can specify the values as you please – these are just common values.