README.md

metadata

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:

   pip install -r requirements.txt
   

3. Run the application:

   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