README.md

---
title: FinanceAuger
emoji: 🌖
colorFrom: purple
colorTo: red
sdk: streamlit
sdk_version: 1.42.0
app_file: app.py
pinned: false
license: mit
short_description: Financial Data Simulation and Prediction Dashboard
---

# Market Data Simulation and Prediction Dashboard 📊

A powerful, interactive financial analysis tool that enables real-time comparison of multiple asset classes with advanced technical indicators and predictive analytics.

## 🚀 Features

- **Multi-Asset Analysis**
  - Stocks & ETFs
  - Cryptocurrencies
  - Commodities & Futures
  - Global Market Indices
  - Regional Market ETFs

- **Technical Indicators**
  - Bollinger Bands
  - Simple Moving Average (SMA)
  - Exponential Moving Average (EMA)
  - Moving Average Convergence Divergence (MACD)
  - Relative Strength Index (RSI)
  - Volume Weighted Average Price (VWAP)

- **Predictive Analytics**
  - Random Forest Price Prediction
  - Exponential Smoothing Forecasting
  - Monte Carlo Simulation
  - Pattern Detection
  - Breakout Prediction
  - Value at Risk (VaR) Analysis

- **Interactive Visualization**
  - Real-time data updates
  - Customizable time periods
  - Cross-asset comparison
  - Dynamic zooming and panning
  - Hover tooltips with precise values

## 🛠️ Tech Stack

- **Frontend**
  - Streamlit: Interactive web interface
  - Plotly: Advanced financial charts
  - Custom CSS: Enhanced UI/UX

- **Backend**
  - Python 3.13
  - yfinance: Real-time market data
  - pandas: Data manipulation
  - scikit-learn: Machine learning models
  - statsmodels: Time series analysis
  - ta: Technical analysis calculations

- **Configuration**
  - YAML: Flexible asset group configuration
  - Environment variables: Secure settings management

## 📚 Libraries & Dependencies

```
streamlit>=1.24.0
pandas>=2.0.0
yfinance>=0.2.0
plotly>=5.0.0
ta>=0.11.0
pyyaml>=6.0.0
scikit-learn>=1.6.1
statsmodels>=0.14.4
scipy>=1.11.0
```

## 🏗️ Architecture

- **Modular Design**
  - Separate configuration files for markets and project settings
  - Dedicated prediction models module
  - Extensible asset group system
  - Component-based visualization

- **Data Flow**
  1. User selects assets and indicators
  2. Real-time data fetching from Yahoo Finance
  3. Technical analysis calculations
  4. Dynamic chart generation
  5. Interactive user feedback

## 💡 Skills Demonstrated

- **Technical**
  - Financial data processing
  - Machine learning implementation
  - Real-time data visualization
  - Technical analysis implementation
  - Web application development
  - Configuration management

- **Financial**
  - Multi-asset analysis
  - Technical indicator implementation
  - Predictive modeling
  - Risk assessment
  - Market data interpretation
  - Cross-market correlation analysis

- **Design**
  - User interface design
  - Data visualization
  - User experience optimization
  - Interactive dashboard creation

## 🚦 Getting Started

1. Clone the repository
2. Install dependencies:
   ```bash
   pip install -r requirements.txt
   ```
3. Run the application:
   ```bash
   streamlit run main.py
   ```

## 🔄 Usage

1. Select asset groups from the sidebar
2. Choose specific tickers from each group
3. Add technical indicators as needed
4. Switch to Predictions & Risk tab for forecasting
5. Adjust prediction parameters and models
6. View raw data in the expandable section

## 📈 Prediction Models

- **Random Forest**
  - Machine learning model for price prediction
  - Captures non-linear market patterns
  - Provides feature importance analysis

- **Exponential Smoothing**
  - Time series forecasting
  - Handles trends and seasonality
  - Adaptive to market changes

- **Monte Carlo Simulation**
  - Simulates multiple price paths
  - Calculates confidence intervals
  - Helps assess potential outcomes

- **Pattern Detection**
  - Identifies trend changes
  - Spots support/resistance levels
  - Predicts potential breakouts

- **Risk Metrics**
  - Value at Risk (VaR) calculation
  - Volatility analysis
  - Trend strength indicators

## 🎯 Future Enhancements

- Additional technical indicators
- Custom indicator parameters
- Data export functionality
- Automated analysis reports
- Portfolio tracking
- Alert system for price movements

## 📝 License

MIT License - feel free to use and modify as needed.

## 👥 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.


Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference