💹 Bitcoin Market Sentiment & Trader Performance Analysis

🧭 Overview

This project explores how Bitcoin market sentiment correlates with historical trader performance. By merging emotional market indicators (Fear/Greed Index) with real trading data, it aims to uncover hidden dynamics that can drive data-informed trading strategies.

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📂 Project Structure

The repository is structured as follows:

├── data
│   ├── processed
│   └── raw
├── notebooks
│   ├── 1_data_preprocessing.ipynb
│   ├── 2_exploratory_data_analysis.ipynb
│   └── 3_trader_clustering.ipynb
├── reports
│   ├── figures
│   └── quick_report.json
├── src
│   ├── data_processing.py
│   └── feature_engineering.py
├── .gitignore
├── README.md
└── requirements.txt

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🚀 How to Run

1. Clone the repository:

   git clone https://github.com/your-username/your-repo-name.git
   cd your-repo-name

2. Install the dependencies:

   pip install -r requirements.txt

3. Run the notebooks in order:

   • notebooks/1_data_preprocessing.ipynb
   • notebooks/2_exploratory_data_analysis.ipynb
   • notebooks/3_trader_clustering.ipynb

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🎯 Objectives

• Analyze how market sentiment influences trader behavior and performance.
• Identify correlations between fear/greed cycles and profitability.
• Extract actionable insights for improved trading decision-making.

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⚙️ Tech Stack

Category	Tools & Libraries
Programming	Python
Data Handling	pandas, numpy
Visualization	matplotlib, seaborn, plotly
Modeling	scikit-learn, statsmodels
Environment	Jupyter Notebook

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📈 Expected Deliverables

✅ Correlation analysis between Fear/Greed and trading outcomes ✅ Interactive visualizations showing market emotion vs. profit trends ✅ Data-driven recommendations for traders to optimize strategies

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🤝 Contributions

Contributions, suggestions, and collaborations are welcome! Feel free to fork, open issues, or submit pull requests to enhance this project.

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📬 Contact

For discussions, ideas, or feedback, connect with Avijit Jana.

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