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Ice 7 scatterplot

✓ Published0🌍 Public
UUday Kiran
Last edited Apr 1, 2025
Created on Apr 1, 2025

This scatter plot analyzes stock trading data by mapping opening prices on the x-axis and closing prices on the y-axis. Each point is colored green for gains and red for losses, with a dashed reference line showing where prices are equal. The visualization uses D3.js v7 with the `d3.scaleLinear`, `d3.extent`, `d3.axisBottom`, and `d3.axisLeft` APIs, loading data from a GitHub Gist CSV file. Interactive tooltips display date, open, close, and price change details on hover.

AI-generated description

Reusable Scatter Plot for Trading Data

This project demonstrates a reusable D3.js scatter plot component applied to stock trading data. The visualization helps analyze the relationship between opening and closing prices of stocks.

Visualization Description

The scatter plot displays:

  • X-axis: Opening prices of stocks
  • Y-axis: Closing prices of stocks
  • Point colors: Green for days where stocks gained value, red for days where stocks lost value
  • Diagonal reference line: Shows where opening price equals closing price

Dataset Analysis

The trading dataset used in this visualization contains stock price information over multiple days. The scatter plot reveals:

  1. Strong correlation: There is a strong positive correlation between opening and closing prices, as expected in financial markets.

  2. Price movement patterns: Points above the diagonal line represent days where the stock closed higher than it opened (gains), while points below represent losses.

  3. Distribution: The cluster of points shows the price range where most of the trading activity occurs.

  4. Outliers: Any points far from the main cluster may represent unusual trading days with significant price movements.

Why Build a Reusable Scatter Plot?

Building a reusable scatter plot for trading data offers several benefits:

  1. Flexibility: By parameterizing aspects like axes, colors, and interactions, the visualization can be adapted to different trading datasets or different financial metrics.

  2. Consistency: Using the same component across multiple visualizations ensures consistent styling and behavior.

  3. Maintainability: Changes to the core visualization logic can be made in one place and applied everywhere.

  4. Efficiency: Creating new visualizations becomes faster as the component can be reused with different configurations.

  5. Analysis: The same visualization structure can be applied to different time periods or different stocks for comparative analysis.

Usage

The scatter plot component can be customized through various getter/setter methods:

MIT Licensed

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