📌 Introduction This project analyzes stock market data for six major stocks (AAPL, AMZN, NVDA, TSLA, GOOG, SPY) using Python and Power BI. It explores trends, volatility, and correlations.

📌 Technologies Used Python (Pandas, Matplotlib, Seaborn, yfinance, talib) Power BI (DAX, variety of visualizations ) GitHub Jupyter Notebook

📌 Dataset The data consists of historical stock closing prices and volume from Yahoo Finance for the last 5 years.

🐍Python ✔️ Data Cleaning & Preprocessing. ✔️ Calculation of Key Metrics (MA, RSI, Volatility, Bollinger Bands, Daily Returns). ✔️ Statistical Correlation Analysis. ✔️ Visualization of Key Trends.

📌 Power BI Reports ✔️ YoY Growth with Matrix and Ribbon Chart. ✔️ Cluster Column Chart for Volume comparison. ✔️ Cluster Bar Chart for Volatility comparison. ✔️ Correlation matrix with conditinal formatting. ✔️ Table with Peak Prices. ✔️ Slicers for time interactivity.

📌 Key Findings 📊 Tesla had the highest volatility. 📉 Amazon had the most oversold positions. 🔥 NVDA stock increased 10X in 5 years! 📈 SPY had the lowest volatility but was the best benchmark for comparison as an ETF of the S&P 500.

📌 Future Improvements 🚀 Automate data fetching with APIs 📈 Add machine learning predictions 📊 Expand analysis with more stocks 🐊 Risk Management Analysis

📌 Contact 📧 Email: ilias.analytics@gmail.com 🔗 LinkedIn: https://www.linkedin.com/in/ilias-roufogalis-320025347/ 💻 GitHub: https://github.com/LiakosData

📓 I have adjusted the project's layout and style and the new version is at my Portfolio GitHub Page : https://liakosdata.github.io/Portfolio/