Pandas time series: Calculating stock price RSI (relative strength index)

Introduction

One of the most intriguing aspects of financial analysis is the diverse set of techniques and tools available to traders and analysts. Among these, the Relative Strength Index (RSI) stands out as a key momentum indicator in technical analysis, especially for stock prices. This tutorial will guide you on how to compute the RSI using Python’s Pandas library, a powerful tool for time series data manipulation and analysis. Whether you’re an experienced analyst or just starting, understanding how to calculate the RSI with Pandas can offer invaluable insights into market trends.

Prerequisites

• Access to a Python environment
• Installation of Pandas and Matplotlib libraries
• Basic familiarity with Python and financial market concepts

Understanding RSI in Stock Trading

The Relative Strength Index (RSI) is a momentum oscillator that measures the speed and change of price movements. The RSI oscillates between zero and 100. Traditionally, and according to Wilder, the RSI is considered overbought when above 70 and oversold when below 30. Signals can also be generated by looking for divergences, failure swings, and centerline crossovers. RSI can be used on any time frame, making it an incredibly versatile indicator.

Setting Up Your Environment

Before calculating the RSI, make sure you have Python and Pandas installed in your environment. You can install Pandas and Matplotlib, which will be used for data visualization, through pip:

pip install pandas matplotlib

Loading the Data

To begin, you need stock price data. For this tutorial, let’s assume you have a CSV file with daily closing prices. Use Pandas to read this data:

import pandas as pd

stock_data = pd.read_csv('path_to_your_csv.csv', parse_dates=['Date'], index_col='Date')
print(stock_data.head())

This code snippet will load your data, parse the ‘Date’ column as a datetime object, and set it as the index of your dataframe, facilitating time series operations.

Calculating Daily Returns

RSI calculation starts with the computation of daily price changes:

stock_data['Daily Returns'] = stock_data['Close'].pct_change()
print(stock_data[['Daily Returns']].head())

This computes the percentage change in daily closing prices, which is essential for determining price momentum.

Separating Gains and Losses

Next, separate the gains and losses from the daily returns as they are treated differently in the RSI calculation:

stock_data['Gain'] = stock_data['Daily Returns'].apply(lambda x: x if x > 0 else 0)
stock_data['Loss'] = stock_data['Daily Returns'].apply(lambda x: -x if x < 0 else 0)
print(stock_data[['Gain', 'Loss']].head())

This step is crucial for calculating the Average Gains and Average Losses needed for the RSI formula.

Calculating Average Gains and Losses

The next step in calculating the RSI is determining the average gains and losses over a specific period, typically 14 days:

window = 14

average_gain = stock_data['Gain'].rolling(window=window).mean()
average_loss = stock_data['Loss'].rolling(window=window).mean()

This code uses Pandas’ .rolling() method to compute the mean over a rolling window, effectively smoothing out short-term fluctuations and highlighting longer-term trends in gains and losses.

Calculating the Relative Strength (RS) and Relative Strength Index (RSI)

The RS is the ratio of average gain to average loss. The RSI is then derived from the RS:

RS = average_gain / average_loss
RSI = 100 - (100 / (1 + RS))

This formula transforms the RS ratio into an oscillating index between 0 and 100, providing a normalized measure of momentum.

Adding RSI to Your Dataframe and Visualizing

Integrating the RSI values into your stock data DataFrame and visualizing the results can offer tangible insights:

stock_data['RSI'] = RSI

import matplotlib.pyplot as plt

plt.figure(figsize=(10, 6))
plt.plot(stock_data.index, stock_data['RSI'])
plt.title('RSI over Time')
plt.ylabel('RSI')
plt.axhline(70, linestyle='--', alpha=0.5, color='red')
plt.axhline(30, linestyle='--', alpha=0.5, color='green')
plt.show()

Plot lines at 70 (overbought) and 30 (oversold) to easily identify potential buying or selling signals within your dataset.

Conclusion

This tutorial covered the basics of calculating the RSI using Pandas for stock price analysis. By following these steps, you can incorporate this powerful momentum indicator into your own financial analysis toolkit. Always remember that no single indicator should be used in isolation for trading decisions. Combining the RSI with other indicators and analysis techniques can help create a more robust trading strategy.

Whether you’re analyzing stocks, cryptocurrencies, or any other form of investment, understanding how to calculate and interpret the RSI is a valuable skill in the arsenal of any analyst or trader.