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Visualizing information can provide invaluable insights that are not easily discernible from raw data alone. Among various types of data visualizations, bubble charts offer a unique way of representing relationships between multiple dimensions of data. In this article, we’ll explore how to create an interactive Python bubble chart to analyze the number of news articles published over time for a specific stock ticker.

By visualizing media coverage, we can uncover patterns that might correlate with stock price movements or corporate events. Media attention often mirrors and amplifies market sentiment, making this analysis valuable for investors and researchers alike.

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Introduction to the Concept

Bubble charts are well-suited for three-dimensional data representation:

• X-axis: The timeline (e.g., months or years).

• Y-axis: The frequency or count of published articles.

• Bubble Size: Highlights the magnitude of news coverage for each period.

This method of visualization helps us identify trends, outliers, or significant increases in news volume that may indicate major developments for a stock. Using Python, we can:

1. Fetch news data using the Financial Modeling Prep API.

2. Process the data to aggregate monthly article counts.

3. Create and enhance the bubble chart with interactive features.

By the end of this tutorial, you will have a script that not only visualizes news volume but also allows for intuitive exploration.

Prerequisites

Ensure the following Python libraries are installed in your environment:

• requests: For fetching data from APIs.

• pandas: For data manipulation and aggregation.

• matplotlib: For creating bubble charts.

• numpy: For numerical operations like normalization.

Install these packages using pip if necessary:

pip install requests pandas matplotlib numpy

Step 1: Fetching News Data

We will use the Financial Modeling Prep API to fetch news articles related to a given stock ticker. The API provides article publication dates and associated metadata.

import requests
import pandas as pd

# Initialize variables
ticker = "NVDA"  # Replace with your preferred stock ticker
base_url = "https://financialmodelingprep.com/api/v3/stock_news"
page = 0
max_pages = 1500  # Number of pages to fetch (adjust as needed)
news_data = []
api_key = "<your_api_key>"  # Replace with your API key

# Fetch articles from multiple pages
while page < max_pages:
    response = requests.get(f"{base_url}?tickers={ticker}&page={page}&apikey={api_key}")
    if response.status_code == 200:
        articles = response.json()
        if not articles:
            break  # Stop fetching if no articles are returned
        news_data.extend(articles)
        page += 1
    else:
        print(f"Failed to fetch data on page {page}. HTTP Status Code: {response.status_code}")
        break

# Convert the fetched data into a DataFrame
df = pd.DataFrame(news_data)

In this script, we iterate over multiple pages to collect all available articles, creating a DataFrame for processing. Be sure to replace <your_api_key> with your actual API key.

Step 2: Data Processing and Aggregation

Once we have the raw data, we process it to extract the publication dates and count the number of articles per month.

# Convert the 'publishedDate' column to datetime format
df['publishedDate'] = pd.to_datetime(df['publishedDate'])

# Extract the year and month from the publication date
df['YearMonth'] = df['publishedDate'].dt.to_period('M')

# Count the number of articles per month
article_counts = df['YearMonth'].value_counts().sort_index()

This step ensures that our data is grouped and summarized by month. By using the to_period('M') function, we efficiently bucket dates into monthly intervals.

Step 3: Creating the Bubble Chart

Bubble charts provide an intuitive and visually compelling way to display the aggregated data.

import matplotlib.pyplot as plt
import numpy as np

# Normalize article counts for color mapping
norm = plt.Normalize(article_counts.min(), article_counts.max())
colors = plt.cm.viridis(norm(article_counts.values))

# Plot the bubble chart
fig, ax = plt.subplots(figsize=(12, 8))
scatter = ax.scatter(
    article_counts.index.astype(str),  # Convert index to string for x-axis labels
    article_counts,  # Y-axis represents article counts
    s=article_counts * 100,  # Bubble size scales with article count
    c=colors,  # Color map for bubbles
    alpha=0.6,  # Transparency level
    cmap='viridis'  # Color map
)

# Add a color bar to indicate article volume
cbar = plt.colorbar(scatter, ax=ax)
cbar.set_label('Number of Articles')

# Customize the chart appearance
ax.set_xlabel('Year-Month')
ax.set_ylabel('Number of Articles')
ax.set_title(f'News Volume Over Time for {ticker}')
plt.xticks(rotation=45)
plt.grid(alpha=0.3)

Step 4: Adding Interactivity

Enhancing the chart with interactive elements provides an engaging experience for users. By hovering over bubbles, viewers can see detailed annotations for each data point.

# Add interactivity to display annotations
def update_annotation(ind):
    pos = scatter.get_offsets()[ind["ind"][0]]
    annot.xy = pos
    text = f"{article_counts.index[ind['ind'][0]]}: {article_counts.iloc[ind['ind'][0]]} articles"
    annot.set_text(text)
    annot.get_bbox_patch().set_facecolor('white')
    annot.get_bbox_patch().set_alpha(0.6)

annot = ax.annotate(
    "",
    xy=(0, 0),
    xytext=(15, 15),
    textcoords="offset points",
    bbox=dict(boxstyle="round", facecolor="white", edgecolor="gray"),
    arrowprops=dict(arrowstyle="->")
)
annot.set_visible(False)

# Connect hover functionality to the figure
def on_hover(event):
    if event.inaxes == ax:
        cont, ind = scatter.contains(event)
        if cont:
            update_annotation(ind)
            annot.set_visible(True)
            fig.canvas.draw_idle()
        else:
            annot.set_visible(False)
            fig.canvas.draw_idle()

fig.canvas.mpl_connect("motion_notify_event", on_hover)

Observations and Insights

When visualized for companies like NVIDIA (NVDA), Palantir (PLTR), or Hims & Hers Health (HIMS), the bubble chart reveals:

• Periods of heightened media coverage typically correlate with significant market events (e.g., product launches, earnings reports, regulatory changes).

• Sustained increases in news volume may signal heightened market attention or speculation.

• Comparing media coverage across different companies may reveal relative interest and sentiment trends.

Key Takeaways

• Media Impact: Increased news coverage often precedes stock price volatility, though this requires further analysis for confirmation.

• Pattern Detection: Visualization makes patterns in data obvious at a glance, facilitating more informed analysis.

• Interactive Features: Adding interactivity enables a richer user experience, allowing real-time exploration of the data.

This Python script serves as a starting point for integrating media sentiment analysis into your research or investment workflow.

Complete Source Code

Access the full source code on GitHub: fin_visualization/src/news_volume_visualization.py

Final Note

This visualization is for informational purposes only and does not constitute investment advice. Always perform your due diligence or consult a financial professional before making investment decisions.