https://github.com/rishi-gupta-data/stock-data-extraction
# Stock-market Data Extraction using Python
https://github.com/rishi-gupta-data/stock-data-extraction
data-extraction data-science python stock-market
Last synced: about 2 months ago
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# Stock-market Data Extraction using Python
- Host: GitHub
- URL: https://github.com/rishi-gupta-data/stock-data-extraction
- Owner: Rishi-gupta-data
- License: mit
- Created: 2024-03-09T16:32:52.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-08-24T06:44:18.000Z (11 months ago)
- Last Synced: 2025-08-24T13:45:02.394Z (11 months ago)
- Topics: data-extraction, data-science, python, stock-market
- Language: Jupyter Notebook
- Homepage:
- Size: 504 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 📈 Stock Market Data Extraction using Python
This repository provides Python scripts and tools for extracting and analyzing **stock market data** from multiple sources (APIs & 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**.
---
## 🚀 Features
- ✅ **Multiple Data Sources**: Yahoo Finance, Alpha Vantage, Quandl, and more.
- 📊 **Historical Data**: Stock prices, trading volumes, OHLC (Open-High-Low-Close).
- ⚡ **Real-Time Data**: Live updates for intraday trading strategies.
- 🔧 **Customizable**: Choose tickers, date ranges, frequency (daily/weekly/monthly).
- 📉 **Data Analysis**: Preprocessing, visualization, trend analysis.
- 🔮 **Future Insights**: Predict stock potential using statistical and ML models.
- 🛠️ **Easy-to-Use**: Python-based API with clean functions.
---
## 📋 Software Requirement Specification (SRS)
### 1. Introduction
- **Purpose**: To build a stock market data extraction & analysis system for **historical, real-time, and predictive analysis**.
- **Users**: Traders, Data Scientists, Researchers, Students.
- **Scope**: Provide a Python toolkit to fetch, clean, and analyze stock data with future trend forecasting.
### 2. Functional Requirements
- Fetch stock data from APIs (Yahoo Finance, Alpha Vantage).
- Perform historical trend analysis.
- Support real-time data monitoring.
- Provide data visualization (candlesticks, line charts).
- Enable predictive analytics (ARIMA, LSTM, Prophet).
### 3. Non-Functional Requirements
- **Performance**: Handle multiple tickers efficiently.
- **Scalability**: Extendable to more APIs.
- **Usability**: Simple API calls.
- **Security**: Secure API key handling.
### 4. System Requirements
- **Python 3.8+**
- Libraries: `pandas`, `numpy`, `matplotlib`, `yfinance`, `alpha_vantage`, `requests`
- Internet connection (for live API fetches)
---
## 🎯 Minimum Viable Product (MVP)
The MVP focuses on:
1. Extracting **historical stock data** (daily/weekly/monthly).
2. Fetching **real-time intraday stock updates**.
3. **Data visualization** (price trends, candlestick charts).
4. Exporting stock data to **CSV/Excel**.
5. Running **basic forecasting models** (ARIMA or Prophet) for potential stock movement.
Future versions will include **LSTM deep learning models**, portfolio optimization, and sentiment analysis.
---
## 🔄 Workflow
### 📌 Stock Data Extraction & Forecasting Flow
```mermaid
graph TD
A[User Input] --> B(Select Data Source)
B --> C(API Call)
C --> D(Data Extraction)
D --> E(Data Cleaning)
E --> F(Visualization)
F --> G(Trend Analysis)
G --> H(Forecasting)
H --> I[Stock Potential]
style A fill:#BBDEFB,stroke:#222,stroke-width:2px,color:#000,font-weight: bold;
style I fill:#C8E6C9,stroke:#222,stroke-width:2px,color:#000,font-weight: bold;
style B fill:#BBDEFB,stroke:#222,stroke-width:2px,color:#000,font-weight: bold;
style C fill:#BBDEFB,stroke:#222,stroke-width:2px,color:#000,font-weight: bold;
style D fill:#BBDEFB,stroke:#222,stroke-width:2px,color:#000,font-weight: bold;
style E fill:#BBDEFB,stroke:#222,stroke-width:2px,color:#000,font-weight: bold;
style F fill:#BBDEFB,stroke:#222,stroke-width:2px,color:#000,font-weight: bold;
style G fill:#BBDEFB,stroke:#222,stroke-width:2px,color:#000,font-weight: bold;
style H fill:#BBDEFB,stroke:#222,stroke-width:2px,color:#000,font-weight: bold;
```
---
## 📊 Data Extraction & Prediction Flow
1. **Data Sources**
- Yahoo Finance (`yfinance`) → Historical + real-time stock data.
- Alpha Vantage → Fundamental + technical indicators.
- Quandl → Economic and financial datasets.
2. **Data Preprocessing**
- Cleaning missing values
- Adjusting stock splits & dividends
- Converting to desired frequency (daily/weekly/monthly)
3. **Visualization**
- Line graphs for price trends
- Candlestick charts for trading patterns
- Volume analysis
4. **Prediction Models**
- **Statistical**: ARIMA for short-term forecasting.
- **ML/DL**: LSTM for long-term sequence modeling.
- **Prophet**: Seasonality-aware forecasting.
5. **Output**
- CSV/Excel exports
- Graphs with predicted trends
- Insights into **potential stock performance**
---
## ⚙️ Installation & Usage
```bash
# Clone the repository
git clone https://github.com/Rishi-gupta-data/Stock-Data-extraction.git
# Navigate to project
cd Stock-Market-Data-Extraction
# Install dependencies
pip install -r requirements.txt
```
### Example Usage
```python
import yfinance as yf
# Fetch historical data
data = yf.download("AAPL", start="2020-01-01", end="2023-01-01")
print(data.head())
# Fetch real-time stock info
ticker = yf.Ticker("AAPL")
print(ticker.info)
```
---
## 🤝 Contributing
Contributions are welcome!
- Fork the repo
- Create a new branch
- Commit your changes
- Submit a pull request
---
## 📜 License
This project is licensed under the **MIT License** - see the [LICENSE](LICENSE) file for details.