Python For Trading

Implementing the Relative Strength Index (RSI) in Python

Written by timelessalpha

1. Introduction to the Relative Strength Index (RSI)

The Relative Strength Index (RSI) is a momentum oscillator that measures the speed and change of price movements. It is used to identify overbought or oversold conditions in a market, which can signal potential trend reversals. The RSI ranges from 0 to 100 and is typically used with a 14-period time frame.

  • Overbought Condition: RSI above 70 suggests that the asset is overbought and might be due for a correction.
  • Oversold Condition: RSI below 30 indicates that the asset is oversold and could be poised for a rebound.

The formula to calculate the RSI is: RSI=100−(1001+RS)RSI = 100 – \left( \frac{100}{1 + RS} \right)

Where RSRS (Relative Strength) is the average of the upward price changes divided by the average of the downward price changes over the chosen period (usually 14 periods).

2. Step-by-Step Implementation of RSI

2.1 Calculating the RSI

The RSI is calculated in a few steps:

  1. Calculate the daily price changes.
  2. Separate the price changes into gains and losses.
  3. Calculate the average gain and average loss over the specified period.
  4. Calculate the Relative Strength (RS), which is the ratio of average gain to average loss.
  5. Use the RS value to calculate the RSI.

2.2 Python Code to Calculate RSI

import pandas as pd
import numpy as np
import yfinance as yf

# Function to calculate RSI
def calculate_rsi(data, window=14):
    # Calculate price changes
    delta = data['Close'].diff()
    
    # Calculate gains and losses
    gain = delta.where(delta > 0, 0)
    loss = -delta.where(delta < 0, 0)
    
    # Calculate average gains and losses
    avg_gain = gain.rolling(window=window, min_periods=1).mean()
    avg_loss = loss.rolling(window=window, min_periods=1).mean()
    
    # Calculate relative strength (RS)
    rs = avg_gain / avg_loss
    
    # Calculate RSI
    rsi = 100 - (100 / (1 + rs))
    return rsi

# Example usage: Fetching Apple stock data
data = yf.download('AAPL', start='2023-01-01', end='2023-12-31')

# Calculate the RSI for a 14-day period
data['RSI'] = calculate_rsi(data)

# Display the first few rows
print(data[['Close', 'RSI']].head())

2.3 Explanation of the Code

  • The delta represents the daily price changes.
  • We use where() to separate the gains and losses, replacing negative values with 0 for gains and positive values with 0 for losses.
  • The rolling().mean() function calculates the average gain and loss over the 14-day period.
  • The Relative Strength (RS) is simply the ratio of average gain to average loss.
  • Finally, the RSI formula is applied to calculate the RSI values.

3. Visualizing the RSI on a Chart

The RSI is often displayed as a line chart with horizontal levels at 30 and 70 to indicate oversold and overbought conditions, respectively.

3.1 Plotting the RSI with Price Chart

import matplotlib.pyplot as plt

# Create a figure with two subplots: one for price, one for RSI
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))

# Plot the price chart
ax1.plot(data['Close'], label='Close Price', color='blue')
ax1.set_title('Apple Stock Price')
ax1.legend()

# Plot the RSI
ax2.plot(data['RSI'], label='RSI', color='green')
ax2.axhline(70, color='red', linestyle='--', label='Overbought (70)')
ax2.axhline(30, color='blue', linestyle='--', label='Oversold (30)')
ax2.set_title('RSI with Overbought and Oversold Levels')
ax2.legend()

plt.tight_layout()
plt.show()

3.2 Interpreting the RSI Chart

  • When the RSI crosses above 70, it suggests that the asset is becoming overbought, and a potential reversal or correction could be coming.
  • When the RSI crosses below 30, it signals that the asset is oversold and may be due for a rebound.

4. Using RSI to Identify Overbought and Oversold Conditions

Traders commonly use the RSI to spot potential buy and sell opportunities:

  • Buy Signal: When the RSI falls below 30 (oversold) and then crosses back above 30, it can be seen as a potential buying opportunity.
  • Sell Signal: When the RSI rises above 70 (overbought) and then crosses back below 70, it can be seen as a potential selling opportunity.

4.1 Example of Buy/Sell Signals Based on RSI

# Generate Buy/Sell signals based on RSI
data['Signal'] = 0  # Default to no signal
data['Signal'][data['RSI'] < 30] = 1  # Buy signal when RSI is below 30
data['Signal'][data['RSI'] > 70] = -1  # Sell signal when RSI is above 70

# Display the signals
print(data[['Close', 'RSI', 'Signal']].tail())

In this example:

  • A Buy Signal is generated when the RSI drops below 30 and then crosses back above 30.
  • A Sell Signal is generated when the RSI rises above 70 and then crosses back below 70.

5. Conclusion

The Relative Strength Index (RSI) is a powerful tool for identifying overbought and oversold conditions in the market. In this guide, we:

  • Walked through how to calculate the RSI using Python.
  • Visualized the RSI alongside the stock price to identify key trends.
  • Implemented a simple strategy based on RSI crossovers with overbought and oversold levels.

*Disclaimer: The content in this post is for informational purposes only. The views expressed are those of the author and may not reflect those of any affiliated organizations. No guarantees are made regarding the accuracy or reliability of the information. Use at your own risk.

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