https://github.com/u-c4n/stock-predictor
Stock Market Analysis & Prediction Platform 🚀 A sophisticated stock market analysis platform combining historical data visualization with machine learning-based price predictions and comprehensive investment analytics. Built with Python, Streamlit, and advanced ML algorithms.
https://github.com/u-c4n/stock-predictor
Last synced: 6 months ago
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Stock Market Analysis & Prediction Platform 🚀 A sophisticated stock market analysis platform combining historical data visualization with machine learning-based price predictions and comprehensive investment analytics. Built with Python, Streamlit, and advanced ML algorithms.
- Host: GitHub
- URL: https://github.com/u-c4n/stock-predictor
- Owner: U-C4N
- Created: 2024-11-10T14:28:57.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-11-10T14:51:20.000Z (11 months ago)
- Last Synced: 2025-01-20T15:22:36.479Z (9 months ago)
- Language: Python
- Size: 16.6 KB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Stock Market Analysis & Prediction Platform 📈
## Overview
An advanced stock market analysis platform that combines historical data visualization with ML-based price predictions and investment analytics. The platform integrates multiple prediction models, technical indicators, and AI-powered recommendations.
## Features 🚀
- Multi-model ML price forecasting (Linear Regression, Random Forest, XGBoost, SVR)
- Interactive technical indicators (RSI, MACD, Bollinger Bands, etc.)
- Investment metrics analysis (Sharpe Ratio, Beta, Alpha)
- AI-powered trading recommendations in multiple languages
- Automated trading signals
- Real-time model performance comparison
## Installation 🛠️
```bash
# Clone the repository
git clone https://github.com/U-C4N/Stock-predictor.git
cd stock-market-analysis
# Install dependencies
pip install -r requirements.txt
# Set up environment variables
Create .env file with:
GEMINI_API_KEY=your_gemini_api_key
```
## Usage 💡
1. Run the application:
```bash
streamlit run main.py
```
2. Enter stock symbol (e.g., NVDA, AAPL)
3. Select time period and model
4. View analysis and predictions
## Dependencies 📚
- Python 3.11+
- Streamlit
- Pandas
- Scikit-learn
- XGBoost
- Plotly
- YFinance
- Google Gemini AI
## Technical Details 🔧
- Data processing: data_handler.py
- ML models: model.py
- Visualization: visualizations.py
- Investment analytics: utils.py
- AI analysis: ai_analyzer.py