{"id":30657346,"url":"https://github.com/rishi-gupta-data/stock-data-extraction","last_synced_at":"2026-05-15T22:40:04.540Z","repository":{"id":226790489,"uuid":"769638697","full_name":"Rishi-gupta-data/Stock-Data-extraction","owner":"Rishi-gupta-data","description":"# Stock-market Data Extraction using Python","archived":false,"fork":false,"pushed_at":"2025-08-24T06:44:18.000Z","size":516,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-08-24T13:45:02.394Z","etag":null,"topics":["data-extraction","data-science","python","stock-market"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Rishi-gupta-data.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2024-03-09T16:32:52.000Z","updated_at":"2025-08-24T06:44:21.000Z","dependencies_parsed_at":"2025-08-24T13:45:05.632Z","dependency_job_id":"1db3ac8a-047d-45d7-9a64-95b192fd575c","html_url":"https://github.com/Rishi-gupta-data/Stock-Data-extraction","commit_stats":null,"previous_names":["rishi-gupta-data/data-extraction","rishi-gupta-data/stock-data-extraction"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Rishi-gupta-data/Stock-Data-extraction","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Rishi-gupta-data%2FStock-Data-extraction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Rishi-gupta-data%2FStock-Data-extraction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Rishi-gupta-data%2FStock-Data-extraction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Rishi-gupta-data%2FStock-Data-extraction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Rishi-gupta-data","download_url":"https://codeload.github.com/Rishi-gupta-data/Stock-Data-extraction/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Rishi-gupta-data%2FStock-Data-extraction/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33082196,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-15T20:25:35.270Z","status":"ssl_error","status_checked_at":"2026-05-15T20:25:34.732Z","response_time":103,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["data-extraction","data-science","python","stock-market"],"created_at":"2025-08-31T11:13:14.529Z","updated_at":"2026-05-15T22:40:04.514Z","avatar_url":"https://github.com/Rishi-gupta-data.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 📈 Stock Market Data Extraction using Python  \n\nThis repository provides Python scripts and tools for extracting and analyzing **stock market data** from multiple sources (APIs \u0026 web scraping). It is designed for **data scientists, traders, and investors** who want to gather, clean, and analyze both **historical** and **real-time market data**, and explore predictive modeling for **future stock potential**.  \n\n---\n\n## 🚀 Features  \n\n- ✅ **Multiple Data Sources**: Yahoo Finance, Alpha Vantage, Quandl, and more.  \n- 📊 **Historical Data**: Stock prices, trading volumes, OHLC (Open-High-Low-Close).  \n- ⚡ **Real-Time Data**: Live updates for intraday trading strategies.  \n- 🔧 **Customizable**: Choose tickers, date ranges, frequency (daily/weekly/monthly).  \n- 📉 **Data Analysis**: Preprocessing, visualization, trend analysis.  \n- 🔮 **Future Insights**: Predict stock potential using statistical and ML models.  \n- 🛠️ **Easy-to-Use**: Python-based API with clean functions.  \n\n---\n\n## 📋 Software Requirement Specification (SRS)  \n\n### 1. Introduction  \n- **Purpose**: To build a stock market data extraction \u0026 analysis system for **historical, real-time, and predictive analysis**.  \n- **Users**: Traders, Data Scientists, Researchers, Students.  \n- **Scope**: Provide a Python toolkit to fetch, clean, and analyze stock data with future trend forecasting.  \n\n### 2. Functional Requirements  \n- Fetch stock data from APIs (Yahoo Finance, Alpha Vantage).  \n- Perform historical trend analysis.  \n- Support real-time data monitoring.  \n- Provide data visualization (candlesticks, line charts).  \n- Enable predictive analytics (ARIMA, LSTM, Prophet).  \n\n### 3. Non-Functional Requirements  \n- **Performance**: Handle multiple tickers efficiently.  \n- **Scalability**: Extendable to more APIs.  \n- **Usability**: Simple API calls.  \n- **Security**: Secure API key handling.  \n\n### 4. System Requirements  \n- **Python 3.8+**  \n- Libraries: `pandas`, `numpy`, `matplotlib`, `yfinance`, `alpha_vantage`, `requests`  \n- Internet connection (for live API fetches)  \n\n---\n\n## 🎯 Minimum Viable Product (MVP)  \n\nThe MVP focuses on:  \n1. Extracting **historical stock data** (daily/weekly/monthly).  \n2. Fetching **real-time intraday stock updates**.  \n3. **Data visualization** (price trends, candlestick charts).  \n4. Exporting stock data to **CSV/Excel**.  \n5. Running **basic forecasting models** (ARIMA or Prophet) for potential stock movement.  \n\nFuture versions will include **LSTM deep learning models**, portfolio optimization, and sentiment analysis.  \n\n---\n\n## 🔄 Workflow  \n\n### 📌 Stock Data Extraction \u0026 Forecasting Flow  \n\n```mermaid\ngraph TD\n    A[User Input] --\u003e B(Select Data Source)\n    B --\u003e C(API Call)\n    C --\u003e D(Data Extraction)\n    D --\u003e E(Data Cleaning)\n    E --\u003e F(Visualization)\n    F --\u003e G(Trend Analysis)\n    G --\u003e H(Forecasting)\n    H --\u003e I[Stock Potential]\n    \n    style A fill:#BBDEFB,stroke:#222,stroke-width:2px,color:#000,font-weight: bold;\n    style I fill:#C8E6C9,stroke:#222,stroke-width:2px,color:#000,font-weight: bold;\n    style B fill:#BBDEFB,stroke:#222,stroke-width:2px,color:#000,font-weight: bold;\n    style C fill:#BBDEFB,stroke:#222,stroke-width:2px,color:#000,font-weight: bold;\n        style D fill:#BBDEFB,stroke:#222,stroke-width:2px,color:#000,font-weight: bold;\n            style E fill:#BBDEFB,stroke:#222,stroke-width:2px,color:#000,font-weight: bold;\n                style F fill:#BBDEFB,stroke:#222,stroke-width:2px,color:#000,font-weight: bold;\n                    style G fill:#BBDEFB,stroke:#222,stroke-width:2px,color:#000,font-weight: bold;\n                        style H fill:#BBDEFB,stroke:#222,stroke-width:2px,color:#000,font-weight: bold;\n\n```\n\n---\n\n## 📊 Data Extraction \u0026 Prediction Flow  \n\n1. **Data Sources**  \n   - Yahoo Finance (`yfinance`) → Historical + real-time stock data.  \n   - Alpha Vantage → Fundamental + technical indicators.  \n   - Quandl → Economic and financial datasets.  \n\n2. **Data Preprocessing**  \n   - Cleaning missing values  \n   - Adjusting stock splits \u0026 dividends  \n   - Converting to desired frequency (daily/weekly/monthly)  \n\n3. **Visualization**  \n   - Line graphs for price trends  \n   - Candlestick charts for trading patterns  \n   - Volume analysis  \n\n4. **Prediction Models**  \n   - **Statistical**: ARIMA for short-term forecasting.  \n   - **ML/DL**: LSTM for long-term sequence modeling.  \n   - **Prophet**: Seasonality-aware forecasting.  \n\n5. **Output**  \n   - CSV/Excel exports  \n   - Graphs with predicted trends  \n   - Insights into **potential stock performance**  \n\n---\n\n## ⚙️ Installation \u0026 Usage  \n\n```bash\n# Clone the repository\ngit clone https://github.com/Rishi-gupta-data/Stock-Data-extraction.git\n\n# Navigate to project\ncd Stock-Market-Data-Extraction\n\n# Install dependencies\npip install -r requirements.txt\n```\n\n### Example Usage  \n\n```python\nimport yfinance as yf\n\n# Fetch historical data\ndata = yf.download(\"AAPL\", start=\"2020-01-01\", end=\"2023-01-01\")\nprint(data.head())\n\n# Fetch real-time stock info\nticker = yf.Ticker(\"AAPL\")\nprint(ticker.info)\n```\n\n---\n\n## 🤝 Contributing  \n\nContributions are welcome!  \n- Fork the repo  \n- Create a new branch  \n- Commit your changes  \n- Submit a pull request  \n\n---\n\n## 📜 License  \n\nThis project is licensed under the **MIT License** - see the [LICENSE](LICENSE) file for details.  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frishi-gupta-data%2Fstock-data-extraction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frishi-gupta-data%2Fstock-data-extraction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frishi-gupta-data%2Fstock-data-extraction/lists"}