Quant Data Explorer

Overview

Quant Data Explorer is an open-source project for collecting, cleaning, and documenting financial datasets. It is designed for beginners who want to learn quantitative finance and basic data analysis.

Features

• Collect free financial datasets from public sources.
• Clean and standardize data for analysis.
• Create beginner-friendly Jupyter notebooks for visualization and simple calculations.
• Contribute your own datasets, cleaning scripts, or notebooks.

Roadmap

1. Collect and organize initial stock market datasets.
2. Add Jupyter notebook templates for basic analysis.
3. Improve dataset documentation.
4. Enable community contributions for new datasets and notebooks.

Getting Started

1. Fork the repository and clone it locally.
2. Follow the instructions in CONTRIBUTING.md.
3. Start by adding datasets, cleaning scripts, or beginner notebooks.

Vision

Build a community-driven hub of open financial data to help beginners learn and explore quantitative finance.

Contribution Tasks - Quant Data Explorer

This project is open to contributors of all levels. Below is a list of 25 tasks you can help with.

How to Claim a Task

1. Check if a task is already taken by looking at the Issues.
2. Claim a task by commenting on the Issue or assigning it to yourself.
3. Change the issue label to "In Progress" when you start.
4. Submit a Pull Request linked to the Issue when done.
5. Tasks inactive for 10 days may be reopened.

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Task List

1. Dataset Management

• Add new financial datasets
• Clean existing datasets
• Standardize data formats
• Add metadata for datasets
• Create automated scripts to download new datasets
• Maintain a changelog for dataset updates

2. Data Validation & Quality

• Write data validation scripts
• Build dataset quality checks
• Implement basic error handling for missing values
• Benchmark data loading performance

3. Helper Functions & Automation

• Write helper functions for loading datasets
• Add CSV/JSON export functions
• Create a simple search tool for datasets
• Improve folder structure for data organization

4. Analysis & Visualization

• Add example Jupyter notebooks for data analysis
• Create summary statistics scripts
• Build visualizations of key datasets
• Add data visualization templates

5. Documentation & Tutorials

• Document datasets in README
• Write README examples showing data usage
• Write beginner-friendly tutorials for dataset exploration
• Document workflow for contributing new data
• Add references to data sources
• Add contribution guidelines for new datasets

6. Testing & Maintenance

• Add unit tests for helper functions