iTrade - Stock Market Analysis Tool

iTrade is a Python-based command-line tool designed to help novice traders make informed decisions in the stock market. The application analyzes a database of approximately 500 stocks and provides recommendations based on user preferences, including performance metrics, industry sectors, company age, and ESG (Environmental, Social, and Governance) scores.

Features

• Performance-based Filtering: Get the top performing stocks based on your specified criteria
• Industry Selection: Filter stocks by specific industries including:
  • Agriculture
  • Clothing
  • Construction
  • Electronics
  • Energy
  • Entertainment
  • Mining
• Historical Context: Filter companies based on their establishment year (1800-2020)
• ESG Scoring: Consider environmental, social, and governance factors with customizable minimum scores (0-10)
• Smart Defaults: Automatic adjustment of criteria if initial filters are too strict
• Clear Data Presentation: Results displayed in an easy-to-read tabular format

Requirements

• Python 3.x
• CSV module (built-in)
• Operating System: Windows/Mac/Linux

Installation

1. Clone this repository:

git clone https://github.com/jasp-nerd/computational-thinking-project
cd computational-thinking-project

2. Ensure you have the stocks.csv file in the root directory with the following columns:
   • ID
   • Performance
   • Industry
   • FoundationYear
   • Environment
   • Social
   • Governance

Usage

Run the program using Python:

python iTrade.py

Follow the interactive prompts to:

1. Specify the number of stocks you want to see (1-100)
2. Choose whether to filter by industry
3. Set a maximum establishment year (optional)
4. Define minimum ESG criteria (optional)

Example Output

ID    |  Performance    |  Industry        |  Foundation Year   |  Environment    |  Social    |  Governance    |
------|-----------------|------------------|--------------------|-----------------|------------|----------------|
AAPL  |  25.4          |  Electronics     |  1976             |  8              |  7         |  9             |

Acknowledgments

• Vrije Universiteit Amsterdam
• Computational Thinking Course
• Project completed: December 2024