Wild Capital Data Visualization

January 4, 2024


The Wild Capital Data Visualization project is a comprehensive tool designed to visualize financial data, focusing on generating charts for social media. It encompasses two main components: data collection/cleaning and chart generation.

Project Components

  • Data Collection and Cleaning: This involves processing raw financial data to prepare it for visualization. The cleaned data is stored in a structured format and ready for analysis.
  • Chart Generation: This process utilizes the cleaned data to create insightful and visually appealing charts that can be shared on social media platforms.

Directory Structure

The project is organized into the following directories:

  • raw/: Contains raw data files (e.g., sp500_earnings_yield.csv, us10y_yield.csv).
  • charts.ipynb: Jupyter notebook for generating charts.
  • data.ipynb: Jupyter notebook for data collection and cleaning.
  • requirements.txt: Lists the Python package dependencies.

Additional directories created after running the notebooks:

  • chart/: Stores the output charts.
  • clean/: Contains cleaned data files.


  1. Requirements:
    • Python 3.x
    • Required libraries: pandas, matplotlib (detailed in requirements.txt)
  2. Installation:
    • Clone the repository to your local machine.
    • Install the required Python packages using:
    • pip install -r requirements.txt

Execution Instructions

  1. Data Collection and Cleaning:
    • Run the data.ipynb notebook to process the raw data and save the cleaned data in the clean/ directory.
  2. Chart Generation:
    • Run the charts.ipynb notebook to generate charts based on the cleaned data and save them in the chart/ directory.


The output charts, such as earnings_yield_cycle_plot.png, are stored in the chart/ directory, ready for use in social media posts and other presentations.