https://github.com/ecemgunay/btc-anomaly-detection
Detecting anomalies in Bitcoin prices using Z-score and Python
https://github.com/ecemgunay/btc-anomaly-detection
anomaly-detection bitcoin data-science jupyter-notebook python z-score
Last synced: about 2 months ago
JSON representation
Detecting anomalies in Bitcoin prices using Z-score and Python
- Host: GitHub
- URL: https://github.com/ecemgunay/btc-anomaly-detection
- Owner: ecemgunay
- Created: 2025-06-07T19:18:36.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-07T19:46:05.000Z (about 1 year ago)
- Last Synced: 2025-06-07T20:25:07.853Z (about 1 year ago)
- Topics: anomaly-detection, bitcoin, data-science, jupyter-notebook, python, z-score
- Language: Jupyter Notebook
- Homepage:
- Size: 42 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 📊 BTC Price Anomaly Detection Using Z-Score
This project demonstrates a simple statistical approach to detect anomalies in Bitcoin (BTC) closing prices using the **Z-score** method. The analysis is performed in Python with `pandas`, `NumPy`, and `matplotlib`, and visualizes the abnormal price movements based on statistical thresholds.
---
## 🔍 Objective
To identify statistically significant deviations in daily BTC closing prices and visualize the results to gain quick insights about unusual market behavior.
---
## 📁 Project Structure
```
btc-anomaly-detection/
├── btc_anomaly_detection.ipynb # Jupyter notebook with full analysis
├── data/
│ └── btc_price_data.csv # Price data (simulated or real)
├── README.md # Project documentation
└── requirements.txt # Python dependencies (optional)
```
---
## 📊 Visualization Example
The chart below shows BTC price over time with red dots marking anomalies (Z-score > 2):
> _(You can include the chart here later as a PNG or let GitHub render the notebook.)_
---
## ⚙️ How It Works
1. Load BTC price data from CSV
2. Calculate Z-scores for each price
3. Mark all values where |Z-score| > 2 as anomalies
4. Plot and interpret
```python
df['Z-Score'] = (df['Price'] - df['Price'].mean()) / df['Price'].std()
df['Anomaly'] = np.abs(df['Z-Score']) > 2
```
---
## 📖 Related Blog Post
📎 You can read the full explanation on Medium here:
👉 [Coming soon – insert your Medium link here]
---
## 🧠 Key Takeaways
- Simple statistical tools like Z-score can help flag unusual market behavior.
- This approach is useful for **alert systems**, **risk monitoring**, and **data quality checks** in crypto platforms.
---
## 📦 Requirements
```text
pandas
numpy
matplotlib
```
> You can install dependencies with:
```bash
pip install -r requirements.txt
```
---
## 📝 License
This project is open source and available under the MIT License.