Calculating the IBD RS Rating with Python

Unlocking Market Leadership: Identify Winning Stocks with Precision

Introduction

In the world of finance, identifying the best-performing stocks is crucial for successful investing. Among various metrics, the Relative Strength (RS) Rating stands out as a powerful tool for distinguishing the market leaders from the laggards. In this article, we will explore how to leverage Python to calculate the RS Rating, similar to the proprietary Investor’s Business Daily metric. By following this guide, you will gain the knowledge and skills to analyze stock performance and make informed investment decisions. So, let’s dive into the fascinating world of Python for finance and unlock the secrets of the RS Rating!

Section 1: Understanding the RS Rating

Before we dive into the Python code, let’s first grasp the essence of the RS Rating and why it is a vital metric for investors. The RS Rating evaluates a stock’s price performance over the past 52 weeks and compares it with the performance of other stocks. It provides a clear indication of whether a stock is a market leader or a laggard in terms of relative price performance. The RS Rating ranges from 1 (worst) to 99 (best). A rating of 99 indicates that the stock is outperforming 99% of all other stocks based on its relative share price performance over the last 52 weeks. As investors, we are looking for stocks with an RS Rating of 80 or higher, as they often demonstrate strong potential for launching significant price runs.

Section 2: Setting up the Environment

To begin our journey, we need to set up our Python environment by installing the necessary packages and importing the required modules. We will be using popular libraries such as yfinance, pandas, pandas-datareader, and yahoo-fin to collect and analyze financial data. These libraries provide powerful functionalities to retrieve historical stock data, calculate percentage changes, and perform various financial computations. Let’s install the required packages and import the modules to get started.

# Imports
import yfinance as yf
import pandas as pd
from pandas_datareader import data as pdr
from yahoo_fin import stock_info as si
import datetime
import time

# Override yfinance API
yf.pdr_override()

Section 3: Retrieving Stock Data

To calculate the RS Rating, we need access to historical price data for both individual stocks and the S&P 500 index. We will be working with the S&P 500 stocks as they represent a broad range of companies and are commonly used as a benchmark for the overall market. Let’s retrieve the tickers for all the S&P 500 stocks and format them for compatibility with Yahoo Finance.

# Get tickers for all S&P 500 stocks and replace "." with "-" for compatibility with Yahoo Finance
sp500_tickers = si.tickers_sp500()
sp500_tickers = [ticker.replace(".", "-") for ticker in sp500_tickers]

Next, we define the S&P 500 index as ^GSPC and specify the date range for which we want to collect the stock data. In this example, we consider the past year as the time frame.

# Define S&P 500 index
sp500_index = '^GSPC'

# Define date range for stock data
start_date = datetime.datetime.now() - datetime.timedelta(days=365)
end_date = datetime.date.today()

Section 4: Calculating Relative Returns

Now comes the exciting part: calculating the relative returns of each stock in comparison to the S&P 500 index. We start by creating an empty list, relative_returns, to store the relative returns for each stock.

# Create empty list to store relative returns for each stock
relative_returns = []

We retrieve the historical price data for the S&P 500 index using the pdr.get_data_yahoo() function from the pandas-datareader library. Then, we calculate the daily percent change for the S&P 500 index and cumulatively multiply these changes to obtain the overall percentage change.

# Retrieve historical price data for the S&P 500 index
sp500_df = pdr.get_data_yahoo(sp500_index, start_date, end_date)
sp500_df['Percent Change'] = sp500_df['Adj Close'].pct_change()
sp500_returns = sp500_df['Percent Change'].cumprod()
sp500_return = sp500_returns.iloc[-1]

Section 5: Iterating Through Stocks

To calculate the RS Rating for each stock, we iterate over the S&P 500 stocks and perform the following steps:

• Download historical data for each stock using pdr.get_data_yahoo() and save it as a CSV file for faster processing.
• Calculate the percent change column for each stock’s historical data.
• Compute the relative return, which is the weighted average of the most recent quarter and the previous 63 trading days.
• Store the relative return in the relative_returns list.

# Iterate over all S&P 500 stocks to calculate their relative returns
for ticker in sp500_tickers:
    # Download historical data as CSV for each stock to speed up the process
    stock_df = pdr.get_data_yahoo(ticker, start_date, end_date)
    stock_df.to_csv(f'{ticker}.csv')
    
    # Calculate percent change column
    stock_df['Percent Change'] = stock_df['Adj Close'].pct_change()
    
    # Calculate the relative return with double weight for the most recent quarter
    stock_returns = stock_df['Percent Change'].cumprod()
    stock_return = (stock_returns.iloc[-1] * 2 + stock_returns.iloc[-63]) / 3 # Double weight for the most recent quarter
    relative_return = round(stock_return / sp500_return, 2)
    relative_returns.append(relative_return)
    
    # Print relative return for each stock
    print(f'Ticker: {ticker}; Relative Return against S&P 500: {relative_return}\n')
    
    # Pause for 1 second to avoid overloading the server with requests
    time.sleep(1)

Section 6: Calculating RS Ratings

Now that we have collected the relative returns for all the S&P 500 stocks, we can create a dataframe to store the results and calculate the corresponding RS Ratings. We use the pd.DataFrame() function from the pandas library and combine the tickers and relative returns into columns.

# Create dataframe with relative returns and corresponding RS ratings
rs_df = pd.DataFrame(list(zip(sp500_tickers, relative_returns)), columns=['Ticker', 'Relative Return'])

To calculate the RS Ratings, we leverage the rank() and pct_change() functions provided by pandas. These functions rank the relative returns in percentage terms, providing a standardized RS Rating on a scale of 1 to 100.

rs_df['RS_Rating'] = rs_df
['Relative Return'].rank(pct=True) * 100

Section 7: Unleashing the Power of RS Ratings

Finally, it’s time to unleash the power of RS Ratings! We can now analyze the dataframe to identify the top-performing stocks based on their RS Ratings. By sorting the dataframe in descending order of RS Ratings, we can easily spot the market leaders.

# Print RS Ratings for all stocks
print(rs_df)

Conclusion

In this article, we delved into the fascinating world of Python for finance and explored how to calculate the Relative Strength (RS) Rating using Python. By leveraging powerful libraries such as pandas, pandas-datareader, and yahoo-fin, we were able to retrieve historical price data, calculate relative returns, and generate RS Ratings for a comprehensive analysis of stock performance. Armed with this knowledge, you can now identify top-performing stocks and make informed investment decisions. So go ahead, unlock the secrets of the market, and embark on a successful journey in the realm of finance!

Remember, the RS Rating is just one of many tools in the arsenal of a savvy investor. Further enhancements to this code could involve incorporating additional financial indicators and expanding the analysis to other market indices. By continuously refining your Python skills and exploring advanced techniques, you can gain a competitive edge in the ever-evolving world of finance.

Here’s the full code for the IBD RS Calculation Python program. Feel free to check it out in your own coding environment!

# Imports
import yfinance as yf
import pandas as pd
from pandas_datareader import data as pdr
from yahoo_fin import stock_info as si
import datetime
import time

# Override yfinance API
yf.pdr_override()

# Get tickers for all S&P 500 stocks and replace "." with "-" for compatibility with Yahoo Finance
sp500_tickers = si.tickers_sp500()
sp500_tickers = [ticker.replace(".", "-") for ticker in sp500_tickers]

# Define S&P 500 index
sp500_index = '^GSPC'

# Define date range for stock data
start_date = datetime.datetime.now() - datetime.timedelta(days=365)
end_date = datetime.date.today()

# Create empty list to store relative returns for each stock
relative_returns = []

# Retrieve historical price data for the S&P 500 index
sp500_df = pdr.get_data_yahoo(sp500_index, start_date, end_date)
sp500_df['Percent Change'] = sp500_df['Adj Close'].pct_change()
sp500_returns = sp500_df['Percent Change'].cumprod()
sp500_return = sp500_returns.iloc[-1]

# Iterate over all S&P 500 stocks to calculate their relative returns relative to the S&P 500
for ticker in sp500_tickers:
    # Download historical data as CSV for each stock to speed up the process
    stock_df = pdr.get_data_yahoo(ticker, start_date, end_date)
    stock_df.to_csv(f'{ticker}.csv')

    # Calculate percent change column
    stock_df['Percent Change'] = stock_df['Adj Close'].pct_change()

    # Calculate the relative return with double weight for the most recent quarter
    stock_returns = stock_df['Percent Change'].cumprod()
    stock_return = (stock_returns.iloc[-1] * 2 + stock_returns.iloc[-63]) / 3  # Double weight for the most recent quarter

    relative_return = round(stock_return / sp500_return, 2)
    relative_returns.append(relative_return)

    # Print relative return for each stock
    print(f'Ticker: {ticker}; Relative Return against S&P 500: {relative_return}\n')

    # Pause for 1 second to avoid overloading the server with requests
    time.sleep(1)

# Create dataframe with relative returns and corresponding Relative Strength (RS) rating
rs_df = pd.DataFrame(list(zip(sp500_tickers, relative_returns)), columns=['Ticker', 'Relative Return'])
rs_df['RS_Rating'] = rs_df.relative_return.rank(pct=True) * 100

# Print RS ratings for all stocks
print(rs_df)

Happy investing!

Disclaimer: The material in this article is purely educational and should not be taken as professional investment advice. Invest at your own discretion.

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