Building a Financial Dashboard from Scratch: Python ETL, MySQL, & Power BI (Demo)

Описание: A Full-Stack Decision Support System for Dual-Mandate Investing (Financial + ESG) using Python, SQL, and Power BI.

This project was developed as the capstone for the CSCI 5050: Decision Support Systems course. Our goal was to apply data engineering principles to a real-world scenario, choosing the financial sector as our case study. We simulated the operations of "Apex Private Capital," a specialized investment firm managing a portfolio of S&P 500 companies. I intend to share the entire structure as an open-source project that anyone can use to kickstart a more robust system (see the last part of this readme for an idea of the "next steps").

The core challenge was to mimic a company that needed to move away from the industry standard of fragmented, static Excel spreadsheets. To achieve that, we architected a functional prototype of a modern decision support system. This project demonstrates how to build a robust, full-stack data pipeline—extracting raw financial and quantitative data, transforming it via Python, ensuring integrity with MySQL, and delivering actionable insights through Power BI.

The Business Case: "Dual-Mandate Investing"
The specific use case focuses on a "Dual-Mandate" strategy. We needed a tool that could reflect the stocks based on two conflicting criteria simultaneously:

Financial Value: Metrics like P/E Ratio and Dividend Yield (Value Investing).
Ethical Alignment: Metrics like ESG Risk Scores and Controversy Levels (Values-Based Investing).
While this implementation focuses on finance, the underlying architecture is domain-agnostic. It serves as a scalable template for any enterprise need involving complex data integration, historic tracking, and live reporting feeds.

Feel free to reach out if you have any question, suggestion, or would like to collaborate!

github.com/angomezu/Financial-Analytics-DSS