{"id":28604553,"url":"https://github.com/ecemgunay/btc-anomaly-detection","last_synced_at":"2026-05-09T09:19:52.888Z","repository":{"id":297838953,"uuid":"998057012","full_name":"ecemgunay/btc-anomaly-detection","owner":"ecemgunay","description":"Detecting anomalies in Bitcoin prices using Z-score and Python","archived":false,"fork":false,"pushed_at":"2025-06-07T19:46:05.000Z","size":43,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-06-07T20:25:07.853Z","etag":null,"topics":["anomaly-detection","bitcoin","data-science","jupyter-notebook","python","z-score"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ecemgunay.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-06-07T19:18:36.000Z","updated_at":"2025-06-07T19:46:09.000Z","dependencies_parsed_at":"2025-06-07T20:36:59.492Z","dependency_job_id":null,"html_url":"https://github.com/ecemgunay/btc-anomaly-detection","commit_stats":null,"previous_names":["ecemgunay/btc-anomaly-detection"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ecemgunay/btc-anomaly-detection","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ecemgunay%2Fbtc-anomaly-detection","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ecemgunay%2Fbtc-anomaly-detection/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ecemgunay%2Fbtc-anomaly-detection/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ecemgunay%2Fbtc-anomaly-detection/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ecemgunay","download_url":"https://codeload.github.com/ecemgunay/btc-anomaly-detection/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ecemgunay%2Fbtc-anomaly-detection/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":259311151,"owners_count":22838644,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["anomaly-detection","bitcoin","data-science","jupyter-notebook","python","z-score"],"created_at":"2025-06-11T18:01:01.496Z","updated_at":"2026-05-09T09:19:47.863Z","avatar_url":"https://github.com/ecemgunay.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 📊 BTC Price Anomaly Detection Using Z-Score\n\n![Anomaly Detection Chart](./btc_anomaly_chart.jpg)\n\nThis 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.\n\n---\n\n## 🔍 Objective\n\nTo identify statistically significant deviations in daily BTC closing prices and visualize the results to gain quick insights about unusual market behavior.\n\n---\n\n## 📁 Project Structure\n\n```\nbtc-anomaly-detection/\n├── btc_anomaly_detection.ipynb   # Jupyter notebook with full analysis\n├── data/\n│   └── btc_price_data.csv        # Price data (simulated or real)\n├── README.md                     # Project documentation\n└── requirements.txt              # Python dependencies (optional)\n```\n\n---\n\n## 📊 Visualization Example\n\nThe chart below shows BTC price over time with red dots marking anomalies (Z-score \u003e 2):\n\n\u003e _(You can include the chart here later as a PNG or let GitHub render the notebook.)_\n\n---\n\n## ⚙️ How It Works\n\n1. Load BTC price data from CSV\n2. Calculate Z-scores for each price\n3. Mark all values where |Z-score| \u003e 2 as anomalies\n4. Plot and interpret\n\n```python\ndf['Z-Score'] = (df['Price'] - df['Price'].mean()) / df['Price'].std()\ndf['Anomaly'] = np.abs(df['Z-Score']) \u003e 2\n```\n\n---\n\n## 📖 Related Blog Post\n\n📎 You can read the full explanation on Medium here:  \n👉 [Coming soon – insert your Medium link here]\n\n---\n\n## 🧠 Key Takeaways\n\n- Simple statistical tools like Z-score can help flag unusual market behavior.\n- This approach is useful for **alert systems**, **risk monitoring**, and **data quality checks** in crypto platforms.\n\n---\n\n## 📦 Requirements\n\n```text\npandas\nnumpy\nmatplotlib\n```\n\n\u003e You can install dependencies with:\n```bash\npip install -r requirements.txt\n```\n\n---\n\n## 📝 License\n\nThis project is open source and available under the MIT License.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fecemgunay%2Fbtc-anomaly-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fecemgunay%2Fbtc-anomaly-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fecemgunay%2Fbtc-anomaly-detection/lists"}