Mapping Market Movements with and D3.js Treemap

Oct 6, 2023

In this tutorial, we'll walk through creating a Treemap visualization of the current stock market conditions using Polygon's Snapshot API and the D3.js Treemap facilitated by the python-client library. The Snapshot API enables us to gather the latest market data for all US-traded stocks, which we then represent as color-coded nested rectangles in the Treemap, offering an interactive and detailed view of the market's status at a specific point in time.

D3.js is a powerful JavaScript library known for creating detailed, interactive visualizations, and when paired with’s financial market APIs, it enables us to create clear and detailed views of market movements in real-time.

A visual snapshot of the market on October 5, 2023.

Polygon is a financial data platform that provides real-time and historical market data for Stocks, Options, Indices, Forex, and Crypto. Access to this data helps developers, investors, and financial institutions make informed decisions based on the latest market conditions.

Getting Started

Setting up and visualizing the stock market's current conditions is straightforward. All you'll need to do is clone the repository, secure an API key from, install the required Python library, launch the visualization server example, and then dive into the visualization through your web browser.

First, clone the repository to your local system:

git clone

If you haven't already, register with and obtain an API key. Opt for at least a Stocks Starter subscription so that you’ll have Snapshot support. Store this key as an environment variable:


Next, install the necessary Python client:

pip install -U polygon-api-client

Change your directory to the Treemap's location and start the server:

cd examples/tools/treemap

Once the server is running, open a web browser and navigate to:


That’s it. You'll now see a Treemap that organizes over 4,000+ US-traded stocks into 10 distinct categories. Use the dropdown box in the top left corner to select different categories and delve deeper into the data.

Building Groups for the Treemap

One of the main challenges in visualizing stock market data is organization. When presented with thousands of companies operating in various sectors, how do we group companies operating in similar areas together? Specifically, how can we ensure that companies from related sectors, like automobile manufacturers, are clustered together in our visualization?

A simple solution is to use the Standard Industrial Classification (SIC) system. The SIC system assigns a four-digit numerical code to each company, categorizing it based on its primary line of business. These codes are hierarchical: the first two digits provide a broad classification, and the subsequent digits offer more detailed categorizations, allowing for clear distinctions between businesses.

For the purposes of clarity in our Treemap, we'll focus on the top-level classifications, representing the following 10 SIC groups:

  • Agriculture, Forestry and Fishing
  • Construction
  • Finance, Insurance and Real Estate
  • Manufacturing
  • Mining
  • Public Administration
  • Retail Trade
  • Services
  • Transportation and Public Utilities
  • Wholesale Trade

To obtain the SIC code for any company, we utilize Polygon's Ticker Details API:

from polygon import RESTClient
client = RESTClient()
details = client.get_ticker_details("AAPL")
print(details[ticker], details['sic_code'], details['sic_description'], details[market_cap])

While the SIC system offers codes for a wide range of business categories, we've chosen to streamline our visualization by limiting ourselves to the top 10 groups. This ensures that our Treemap remains fairly user-friendly and easy to understand, without cluttering the interface with too much granularity.

A visual snapshot of the Retail Trade group on October 5, 2023.

For those interested in the underlying mechanics, the repository you cloned contains a script,
. This script retrieves a snapshot of all ticker symbols, processes each one to obtain its SIC code via the Ticker Details API, and then saves these classifications into a file named

The following function maps SIC codes to their respective industry groups:

def sic_code_to_group(sic_code):
    Maps a given SIC code to the industry group.
    sic_code = int(sic_code)
    if 100 <= sic_code <= 999:
        return "Agriculture, Forestry and Fishing"
    elif 1000 <= sic_code <= 1499:
        return "Mining"
    elif 1500 <= sic_code <= 1799:
        return "Construction"
    # Note: 1800-1999 not used
    elif 2000 <= sic_code <= 3999:
        return "Manufacturing"
    elif 4000 <= sic_code <= 4999:
        return "Transportation and Public Utilities"
    elif 5000 <= sic_code <= 5199:
        return "Wholesale Trade"
    elif 5200 <= sic_code <= 5999:
        return "Retail Trade"
    elif 6000 <= sic_code <= 6799:
        return "Finance, Insurance and Real Estate"
    elif 7000 <= sic_code <= 8999:
        return "Services"
    elif 9100 <= sic_code <= 9729:
        return "Public Administration"
    elif 9900 <= sic_code <= 9999:
        return "Nonclassifiable"
        return None

The logic of this SIC code-to-group enables us to transform a large dataset into a neatly structured visualization. This structured approach facilitates easy identification of market conditions, providing a snapshot of the market's overall health.

Building the Treemap

In this section, we'll outline the process of transforming raw market data into a visual treemap representation. We will begin by fetching market data, organizing it based on our previously defined SIC groupings, and then leveraging the D3.js library to visualize this data. Specifically, the size of each section in the treemap corresponds to a company's market_cap, while its color represents the stock's percentage change.

We begin by obtaining a snapshot of the market from using the Snapshot API. This snapshot gives us the latest information for each stock, including the ticker name and its percentage change for the day.

from polygon import RESTClient
client = RESTClient()  # POLYGON_API_KEY environment variable is used
snapshots = client.get_snapshot_all("stocks")
pct_changes = {
    snapshot.ticker: round(snapshot.todays_change_percent, 2)
    for snapshot in snapshots

We already solved a significant challenge in grouping companies from similar sectors by their top-level SIC codes. Using our previously constructed SIC mappings in the

file, we're able to achieve this. Larger companies, denoted by a bigger market_cap, will be represented with bigger rectangles in the treemap. This structure keeps our Treemap visualization user-friendly manner, and hopefully easy to understand what’s happening at a quick glance.

To transform our snapshot data into a structured format suitable for visualization, we use:

with open("sic_code_groups.json", "r") as file:
    sic_code_groups = json.load(file)

data = defaultdict(lambda: defaultdict(list))

for sic_code, group_data in sic_code_groups.items():
    parent = group_data["sic_description"]
    for company in group_data["companies"]:
        ticker = company["ticker"]
        weight = company["weight"]
        pct_change = pct_changes.get(ticker, 0.0)
            {"name": ticker, "weight": weight, "change": pct_change}

data = dict(data)
output = {"name": "root", "children": []}
for parent, children in data.items():
    parent_dict = {"name": parent, "children": []}
    for child, companies in children.items():
        total_change = sum(company["change"] for company in companies)
        avg_change = total_change / len(companies) if companies else 0
        avg_change = round(avg_change, 2)
        child_dict = {
            "name": child,
            "change": avg_change,
            "children": companies,

With our data structured, the next step is to visually represent it as a D3.js Treemap. The logic we'll explore here offers a top-level view of how this is built. But, to really see how it works it is worth exploring the full code provided in the example repository.

A visual snapshot of the Transportation and Public Utilities group on October 5, 2023.

script has predefined HTML and Javascript code that serves the Treemap visualization and also acts as a data provider, retrieving and processing the snapshot data in real-time. A neat feature of the visualization is an interactive dropdown selector in the top left corner. By using this, you can filter the treemap to focus on specific industry categories, offering a much more detailed view into specific sectors.

d3.json("/data").then(function(data) {

    const select ='body').insert('select', 'script')
        .attr('id', 'categorySelector')
        .style('position', 'absolute')
        .style('z-index', '10');

    select.append('option').text('All').attr('value', 'all');

    data.children.forEach(child => {

    drawTreemap(data); // Draw the initial treemap with all categories"#categorySelector").on("change", function() {
	    console.log("Dropdown Changed!");
	    const selectedCategory ='value');
	    drawTreemap(data, selectedCategory);

}).catch(function(error) {

Finally, to make this visualization accessible, a simple HTTP server is set up through Python. Running the tool and accessing http://localhost:8889 on a browser will present the treemap. This visualization fetches and displays data in real-time every time it's loaded, ensuring users always get the freshest snapshot of the market.

Next Steps

Understanding market movements can be complex given the large amount of data. Yet, by combining the capabilities of with the interactive visualization of D3.js, we managed to represent a snapshot of the market in an easy to understand way. Whether you're a developer, a financial analyst, or someone with a keen interest in the stock market, having tools like this Treemap at your disposal can greatly enhance your ability to quickly grasp market conditions. If you've been thinking about building a similar visualization tool, we hope this tutorial offers a path forward.

Happy exploring!

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