Relative Strength Index (RSI) Calculator

This relative strength index (RSI) calculator allows you to compute the RSI of a stock based on the available data.

Simply input the details of the closing prices of a given stock, separated by line breaks, spaces, or commas, over the period of interest and click on the "Calculate" button.

What is the Relative Strength Index (RSI)?

The RSI was first introduced by J. Welles Wilder Jr. in the 1978 book "New Concepts in Technical Trading Systems." The RSI value provides valuable insights into the magnitude and direction of price movements and ranges from 0 to 100. Values above 50 indicate that prices are increasing, while those below 50 indicate that prices are falling. The RSI is calculated based on price versus time; as such, it does not consider the additional aspects that can influence the prices of stocks. However, despite this limitation, it can be a valuable tool to identify oversold and overbought market conditions.

To calculate the RSI from a set of closing prices (monthly, weekly, daily, hourly, etc.), the relative strength (RS) needs to be determined as follows:

RS = Average Gain of up periods during the specified time frame / Average Loss of down periods during the specified time frame

The percentage loss or gain is the degree of change that can be observed between the closing prices of different periods. Conventionally, average losses and gains in a given closing price are reported as a 14-period rolling average. In addition, losses are reported as positive, as opposed to negative, values.

Instead of using a technical indicator that spanned from zero to infinity, Wilder set an RSI that ranges from 0 to 100:

RSI = 100 − [100 / (1 + RS)]

As such, when the RS is 0, the maximum RSI is 0. When the RS is infinity, the RSI has a maximum value of 100. When an RSI exceeds 70, the market conditions are deemed to be overbought. The market is oversold when the RSI falls below 30.

Reference: Wilder, J. W., Jr. (1978). New concepts in technical trading systems. McLeansville, NC: Trend Research. pp 63 - 70

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