📈 Sector Seasonality Analysis in Financial Markets

(Data Science Portfolio Project – Finance Analytics)

This project analyzes historical sector performance across months and seasons using Python.
The goal is to identify whether certain market sectors show recurring seasonal patterns that could inform investment strategies, portfolio allocation, and risk management.

📊 Data Source

• Data obtained from Yahoo Finance by scraping historical sector ETF performance.
• Data collection was handled as part of a group project.
• This repository focuses on my contribution:
  • Exploratory seasonality analysis
  • Visualization of trends
  • Interpretation of results
• Cleaned CSV files are included in the Data/ folder for reproducibility.

📁 Project Structure

.
├── PROJECT_sector_seasonality_analysis.ipynb # Main notebook with full analysis
├── Data/ # Cleaned input data (CSV format)
├── requirements.txt # Dependencies for reproducibility
└── README.md # Project description and usage guide

🎯 Objectives

• Examine monthly and seasonal sector performance.
• Detect recurring calendar effects across sectors.
• Compare cyclical vs defensive sectors.
• Communicate insights with clear visualizations and metrics.

🧪 Methodology

1. Data Preprocessing

   • Import cleaned sector returns from Yahoo Finance.
   • Aggregate by month and season.
   • Normalize returns for comparability.

2. Seasonality Analysis

   • Compute average monthly returns per sector.
   • Detect recurring overperformance/underperformance patterns.
   • Compare across multiple years.

3. Visualization

   • Heatmaps of monthly sector returns.
   • Bar and line charts comparing sectors.
   • Highlight top/bottom months for each sector.

📈 Key Results

• Certain sectors exhibit consistent seasonal outperformance (e.g., technology in Q4).
• Defensive sectors (e.g., utilities, consumer staples) show lower seasonal variation.
• Cyclical sectors (e.g., energy, industrials) demonstrate stronger calendar effects.
• Seasonality insights may support timing and allocation strategies.

(Detailed plots and charts are available in the notebook.)

🚀 How to Run

1. Clone the repository:

   git clone https://github.com/<your-username>/sector-seasonality-analysis.git
   cd sector-seasonality-analysis

2. Install dependencies:

   pip install -r requirements.txt
   

3. Launch the notebook:

   jupyter notebook PROJECT_sector_seasonality_analysis.ipynb
   

📦 Requirements

• Python 3.11+
• pandas
• numpy
• matplotlib
• seaborn
• jupyter

Full dependencies are listed in requirements.txt.

📄 License

This project is licensed under the MIT License – see the LICENSE file for details.

🧭 Roadmap

• v1.1.0: Add interactive visualizations (Plotly, Dash, or Power BI)
• v2.0.0: Extend with ML forecasting (Prophet/ARIMA) and automated sector rotation strategy

👥 Credits

• Analysis and interpretation by Máté Lukács
• Data collected from Yahoo Finance as part of a group project