This is an end-to-end analysis of S&P 500 stock prices from 2014–2017, covering 505 companies. Ge...This is an end-to-end analysis of S&P 500 stock prices from 2014–2017, covering 505 companies. Ge...
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This is an end-to-end analysis of S&P 500 stock prices from 2014–2017, covering 505 companies.
Getting here wasn’t a straight line. I hit MySQL connection errors, forgotten passwords, password mismatches, and slicers that refused to filter my charts. Every error message became a small lesson in how SQLAlchemy, MySQL services, and Power BI relationships actually work under the hood. I didn’t just copy steps — I troubleshot my way through each one until I understood why it worked.
The pipeline I built:
🔹 Python (Pandas + SQLAlchemy) to load 497K rows into MySQL 🔹 SQL for data cleaning, quality checks, and analysis 🔹 Power BI for an interactive dashboard with 5 visuals and 3 slicers
Here’s what the data revealed:
📌 The single highest trading volume day was August 24, 2015 — “Black Monday” — when a global market sell-off triggered 4.6 billion shares traded in one day, led by Bank of America and Apple.
📌 Wednesday consistently sees the highest trading volume, Monday the lowest — markets ramp up activity as the week goes on.
📌 Amazon’s most volatile day was June 9, 2017, with an $85.99 swing between its daily high and low.
📌 And the standout investment story: if you’d put money into Nvidia (NVDA) on January 2, 2014 and held until December 29, 2017, you’d have seen a +1,120% return.
This project pushed my SQL and Power BI skills further than any tutorial could — because real data (and real errors) force you to actually understand the tools, not just follow steps.
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Creatives on Contra have earned over $150M and we are just getting started