Whale Activity vs UNI Price

This mini-project investigates whether tracking whale activity (large transfers of UNI tokens) can reveal useful signals about price movements and volatility.

The core question was:
Do whales move the market, or do they just stir the waters?

---

Project Overview

• Data Collected:

  • Whale transfer volumes (USD equivalent).
  • UNI token historical price (daily close).

• Methodology:

  1. Whale Transfer Volume (USD):

     • Aggregated daily sum of UNI transfers above a “whale threshold.”
     • Provides a proxy for big-money activity in the ecosystem.

  2. UNI Price:

     • Daily closing price of UNI.
     • Used to compare whale movements with market direction.

  3. Volatility (7-day rolling standard deviation of returns):

     • Measured daily UNI log returns.
     • Applied a 7-day rolling window to capture “turbulence.”
     • Helps test if whale spikes correlate with noisier price action.

  4. Overlay Visualization:

     • Whale transfer volumes plotted as bars.
     • UNI price plotted as a line on secondary axis.
     • Makes it easy to visually identify coincidences.

---

Key Findings

• Price Direction:
  Whale transfers alone do not consistently predict whether UNI price goes up or down afterward.

• Volatility Signal:
  Spikes in whale transfers often coincide with short-term jumps in volatility, i.e., more erratic price swings (both directions).

• Interpretation:
  Think of whale transfers as shockwaves: they don’t tell you which way the ship will move, but they tell you the waters won’t stay calm.

---

So What?

For analysts and traders:

• Don’t treat whale moves as buy/sell signals.
• Instead, treat them as risk alerts: brace for turbulence, hedge exposure, or scale risk accordingly.
• The real edge lies not in predicting price direction, but in anticipating when markets may stop behaving “normally.”

---

Future Improvements

• Analyze at hourly granularity instead of daily to capture faster reactions.
• Expand analysis across multiple tokens (cross-token whale impact).
• Add machine learning models to test predictive power on volatility regimes.
• Build real-time monitoring + alerting system for whale spikes.

---

📂 Repository Structure

├── data/              # Raw and processed datasets
├── notebooks/         # Jupyter notebooks with analysis
├── charts/            # Visualizations (PNG outputs)
├── src/               # Supporting Python scripts
└── README.md          # Project documentation

---

Quick Preview

Example chart from the project:

---

⚙️ How to Run

1. Clone this repository

   git clone https://github.com/your-username/whale-uni-analysis.git
   cd whale-uni-analysis

2. **Set up a virtual environment & install dependencies**

   ```bash
   python -m venv .venv
   source .venv/bin/activate   # on macOS/Linux
   .venv\Scripts\activate      # on Windows

   pip install -r requirements.txt
   ```

3. **Run the notebooks**
   Open JupyterLab or Jupyter Notebook and run the files in `/notebooks/`.

   ```bash
   jupyter lab
   ```

4. **Reproduce the charts**
   Executing the notebooks will generate updated visualizations in the `/charts/` folder.

---

## 📝 License

MIT License. Free to use, modify, and share.

```
```