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SP500-Data-Filter

This Python project processes historical S&P 500 market data from a CSV file. Each row is parsed into structured objects, filtered by closing price, and the results can be displayed as a formatted table in the console or stored in a SQLite database, where the program creates the database and tables, inserts the original data, and generates a filtered table.

Note: This project was created for academic and learning purposes. It is not intended for production use.

Installation

  1. Ensure Python 3.12 is installed on your system.
  2. Download or clone the repository and navigate to the project directory.
  3. No external dependencies are required — the project uses only Python standard libraries (csv, sqlite3, unittest, and io).

Usage

Make sure the dataset file (sp500_data.csv) is located in the project folder, then run the program using the main Python file.

You will be prompted with the following menu:

S&P 500 Data Filter
--------------------
1 - Print filtered CSV data
2 - Create SQL tables and insert data
3 - Exit

Key Functionality

  • Option 1 – Data Filtering (Console Output)

    • Reads CSV data
    • Filters rows where close > 6800
    • Displays formatted results in a table

  • Option 2 – Database Creation & Storage

    • Creates a SQLite database: sp500_database.db
    • Builds two tables:
      • original_data (raw dataset)
      • filtered_data (filtered subset)
    • Inserts and filters data using SQL queries

Features

  • CSV parsing with handling for empty rows
  • Object-oriented design using SP500Entry
  • Filtering based on financial thresholds
  • SQLite database integration
  • Automated table creation and data insertion
  • Formatted console output
  • Unit testing

Project Structure

SP500-Data-Filter/
├── main.py
├── sp500_data.csv
├── sp500_data_class.py
├── test_database.py
├── test_data_processing.py
└── sp500_database.db (generated at runtime)

Skills Demonstrated

  • Python programming
  • File handling and CSV parsing
  • Database management with SQLite
  • SQL queries and data transformation
  • Implementation of unit tests

Future Improvements

  • Add data visualization (matplotlib)

License

MIT