Visualizing the Market Strength Using a Gauge Chart: Python Tutorial

In this tutorial, we’ll explore how to visualize the RSI using a gauge chart in Python, leveraging the capabilities of the Plotly library, “Guage Chart” visually represents a single value within a given scale, resembling the dashboard gauge of a car. In the context of stock market indicators, such as the Relative Strength Index (RSI), it is used to depict how overbought or oversold a security is. The chart typically has a semicircular scale with a needle or pointer indicating the current value.

In this tutorial, we will be fetching the historical data from yfinance and computing the latest RSI value using the pandas_ta library. We will be creating the gauge chart quadrant values as follows

0-20 Levels – Extremely Oversold
20-40 – Over Sold
40-60 – Neutral
60-80 – Momentum Zone
80-100 – Extreme Overbought

Importing the Library and Computing the RSI Value

import yfinance as yf
import pandas as pd
import pandas_ta as ta
import plotly.graph_objects as go
import numpy as np

# Fetch data from Yahoo Finance
ticker = "^NSEI"  # Example ticker
data = yf.download(ticker, start="2023-01-01", end="2024-01-12")


# Calculate RSI using pandas_ta
data['RSI'] = ta.rsi(data['Close'], length=14)

# Latest RSI value
latest_rsi = data['RSI'].iloc[-1]

Plot the Guage Chart Showing RSI Strength Reading

For the visual representation, we’ll create a gauge chart using plotly.graph_objects. This type of chart will allow us to illustrate the RSI level like a car’s speedometer:

The below code snippet sets up the quadrant, colors, and text for the different RSI levels and calculates the angle of the needle based on the latest RSI value.

# Gauge chart setup

plot_bgcolor = "#def"
quadrant_colors = [plot_bgcolor, "#f25829", "#f2a529", "#eff229", "#85e043", "#2bad4e"] 
quadrant_text = ["", "<b>Extreme Overbought</b>", "<b>Momentum Zone</b>", "<b>Neutral</b>", "<b>Over Sold</b>", "<b>Extremely Oversold</b>"]
n_quadrants = len(quadrant_colors) - 1

min_value = 0
max_value = 100  # RSI ranges from 0 to 100
hand_length = np.sqrt(2) / 4
hand_angle =  360 * (-latest_rsi/2 - min_value) / (max_value - min_value) - 180

# Create gauge chart
fig = go.Figure(
    data=[
        go.Pie(
            values=[0.5] + (np.ones(n_quadrants) / 2 / n_quadrants).tolist(),
            rotation=90,
            hole=0.5,
            marker_colors=quadrant_colors,
            text=quadrant_text,
            textinfo="text",
            hoverinfo="skip",
            sort=False
        ),
    ],
    layout=go.Layout(
        showlegend=False,
        margin=dict(b=0,t=10,l=10,r=10),
        width=800,
        height=800,
        paper_bgcolor=plot_bgcolor,
        annotations=[
            go.layout.Annotation(
                text=f"<b>Nifty RSI Level:</b><br>{latest_rsi:.2f}",
                x=0.5, xanchor="center", xref="paper",
                y=0.25, yanchor="bottom", yref="paper",
                showarrow=False,
                font=dict(size=14)
            )
        ],
        shapes=[
            go.layout.Shape(
                type="circle",
                x0=0.48, x1=0.52,
                y0=0.48, y1=0.52,
                fillcolor="#333",
                line_color="#333",
            ),
            go.layout.Shape(
                type="line",
                x0=0.5, x1=0.5 + hand_length * np.cos(np.radians(hand_angle)),
                y0=0.5, y1=0.5 + hand_length * np.sin(np.radians(hand_angle)),
                line=dict(color="#333", width=4)
            )
        ]
    )
)
fig.show()

This visual tool can be particularly useful when presenting market data, offering a clear and immediate interpretation of the market’s momentum. By following this tutorial, you’ve learned how to fetch market data, calculate RSI, and create a dynamic gauge chart to visualize market strength. Happy trading, and may your analysis be as sharp as your visualizations!