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| title: Advanced Stock Prediction Analysis with Amazon Chronos | |
| emoji: 🚀 | |
| colorFrom: blue | |
| colorTo: purple | |
| sdk: gradio | |
| sdk_version: 5.33.0 | |
| app_file: app.py | |
| pinned: true | |
| license: mit | |
| short_description: stock prediction with Amazon/Chronos | |
| tags: | |
| - mcp-server-track | |
| - finance | |
| - machine-learning | |
| - time-series | |
| - stock-prediction | |
| - chronos | |
| - market | |
| - yfinance | |
| - amazon | |
| - forecasting | |
| - ensemble | |
| # 🚀 Advanced Stock Prediction Analysis with Amazon Chronos | |
| A cutting-edge AI-powered stock prediction and analysis system with **580M+ parameters**, designed to analyze and predict stock prices across multiple timeframes. Equipped with **Amazon's Chronos foundation model** and **advanced ensemble methods**, it excels in both short-term trading and long-term investment analysis. | |
| ## 🌟 Key Features | |
| ### Market Status Monitoring | |
| - **Real-Time Market Status**: Check if markets are open or closed with a simple click | |
| - **Multi-Market Support**: Monitor US Stocks, European Markets, Asian Markets, Forex, Crypto, Futures, and Commodities | |
| - **Timezone-Aware**: Accurate status based on each market's local timezone | |
| - **Trading Hours**: Detailed information about market hours and next trading days | |
| - **24/7 Markets**: Support for continuous trading markets like Forex and Crypto | |
| - **User-Friendly Interface**: Simple dropdown menu and click-to-check functionality | |
| ### Core Prediction Engine | |
| - **Amazon Chronos Integration**: Uses the state-of-the-art Chronos T5 foundation model for probabilistic time series forecasting | |
| - **Multi-Timeframe Analysis**: Support for daily, hourly, and 15-minute timeframes | |
| - **Advanced Ensemble Methods**: Combines multiple algorithms including Random Forest, Gradient Boosting, SVR, and Neural Networks | |
| ### Enhanced Covariate Data | |
| - **Market Indices**: S&P 500, Dow Jones, NASDAQ, VIX, Treasury yields | |
| - **Sector ETFs**: Financial, Technology, Energy, Healthcare, and more | |
| - **Commodities**: Gold, Silver, Oil, Natural Gas, Corn, Soybeans | |
| - **Currencies**: EUR/USD, GBP/USD, JPY/USD, CHF/USD, CAD/USD | |
| - **Economic Indicators**: Inflation proxies, volatility indices, dollar strength | |
| ### Advanced Uncertainty Calculations | |
| - **Multiple Uncertainty Methods**: | |
| - Basic quantile-based uncertainty | |
| - Skewness-adjusted uncertainty | |
| - Volatility-scaled uncertainty | |
| - Market condition adjusted uncertainty | |
| - Time-decay uncertainty | |
| - Ensemble uncertainty (combines all methods) | |
| - **Regime-Aware Uncertainty**: Adjusts uncertainty based on market regime detection | |
| - **Confidence Intervals**: 95% confidence bands with multiple calculation methods | |
| ### Enhanced Volume Prediction | |
| - **Price-Volume Relationship Modeling**: Analyzes the relationship between price movements and volume | |
| - **Volume Momentum**: Incorporates volume momentum and trends | |
| - **Market Condition Adjustments**: Adjusts volume predictions based on market volatility | |
| - **Uncertainty Quantification**: Provides volume prediction uncertainty estimates | |
| ### Sentiment Analysis | |
| - **News Sentiment Scoring**: Analyzes news articles for sentiment polarity | |
| - **Confidence Levels**: Provides confidence scores for sentiment analysis | |
| - **Real-time Integration**: Incorporates sentiment data into prediction models | |
| ### Market Regime Detection | |
| - **Hidden Markov Models**: Detects bull, bear, and sideways market regimes | |
| - **Volatility Clustering**: Identifies periods of high and low volatility | |
| - **Regime-Aware Predictions**: Adjusts predictions based on current market regime | |
| ### Advanced Algorithms | |
| - **Multi-Algorithm Ensemble**: | |
| - Random Forest Regressor | |
| - Gradient Boosting Regressor | |
| - Ridge Regression | |
| - Lasso Regression | |
| - Support Vector Regression (SVR) | |
| - Multi-Layer Perceptron (MLP) | |
| - **Time Series Cross-Validation**: Uses expanding window validation for robust model evaluation | |
| - **Weighted Ensemble**: Combines predictions using uncertainty-weighted averaging | |
| ### Financial Smoothing | |
| - **Multiple Smoothing Methods**: | |
| - Exponential smoothing (trend following) | |
| - Moving average (noise reduction) | |
| - Kalman filter (adaptive smoothing) | |
| - Savitzky-Golay (preserves peaks/valleys) | |
| - Double exponential (trend + level) | |
| - Triple exponential (complex patterns) | |
| - Adaptive smoothing (volatility-based) | |
| ## 📊 Technical Indicators | |
| ### Price-Based Indicators | |
| - **RSI (Relative Strength Index)**: Momentum oscillator with regime-adjusted thresholds | |
| - **MACD (Moving Average Convergence Divergence)**: Trend-following momentum indicator | |
| - **Bollinger Bands**: Volatility indicator with position analysis | |
| - **Moving Averages**: SMA 20, SMA 50 with crossover analysis | |
| ### Volume-Based Indicators | |
| - **Volume-Price Trend**: Analyzes the relationship between volume and price movements | |
| - **Volume Momentum**: Tracks volume changes over time | |
| - **Volume Volatility**: Measures volume stability | |
| - **Volume Ratio**: Compares current volume to historical averages | |
| ### Risk Metrics | |
| - **Sharpe Ratio**: Risk-adjusted return measure | |
| - **Value at Risk (VaR)**: Maximum expected loss at given confidence level | |
| - **Maximum Drawdown**: Largest peak-to-trough decline | |
| - **Beta**: Market correlation measure | |
| - **Volatility**: Historical and implied volatility measures | |
| ## 🛠️ Installation | |
| 1. **Install Dependencies**: | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| 2. **Key Dependencies**: | |
| - `chronos-forecasting>=1.0.0`: Amazon's Chronos foundation model | |
| - `torch>=2.1.2`: PyTorch for deep learning | |
| - `yfinance>=0.2.0`: Yahoo Finance data | |
| - `scikit-learn>=1.3.0`: Machine learning algorithms | |
| - `plotly>=5.0.0`: Interactive visualizations | |
| - `gradio>=4.0.0`: Web interface | |
| - `textblob>=0.17.1`: Sentiment analysis | |
| - `arch>=6.2.0`: GARCH modeling | |
| - `hmmlearn>=0.3.0`: Hidden Markov Models | |
| ## 🚀 Usage | |
| ### Web Interface | |
| ```bash | |
| python app.py | |
| ``` | |
| The application provides a comprehensive web interface with three main tabs: | |
| 1. **Daily Analysis**: Long-term investment analysis (up to 365 days) | |
| 2. **Hourly Analysis**: Medium-term trading analysis (up to 7 days) | |
| 3. **15-Minute Analysis**: Short-term scalping analysis (up to 3 days) | |
| ### Market Status Check | |
| The application includes a simple market status monitoring feature: | |
| 1. **Quick Market Status Check**: Located at the top of the interface | |
| 2. **Market Selection**: Dropdown menu with all supported markets: | |
| - US Stock Market (NYSE, NASDAQ, AMEX) | |
| - European Markets (London, Frankfurt, Paris) | |
| - Asian Markets (Tokyo, Hong Kong, Shanghai) | |
| - Forex Market (24/7 Global Currency Exchange) | |
| - Cryptocurrency Market (24/7 Bitcoin, Ethereum, Altcoins) | |
| - US Futures Market (24/7 CME, ICE, CBOT) | |
| - Commodities Market (24/7 Gold, Silver, Oil, Natural Gas) | |
| 3. **One-Click Check**: Click "Check Market Status" to get real-time information | |
| 4. **Detailed Information**: Shows current status, trading hours, next trading day, and time until open/close | |
| **Example Output:** | |
| ``` | |
| 🟢 US Stock Market Status: OPEN | |
| Current Status: US Stock Market is currently open | |
| Market Details: | |
| - Type: Stocks | |
| - Symbol: ^GSPC | |
| - Current Time: 14:30:00 EDT | |
| - Last Updated: 2024-01-15 14:30:00 EDT | |
| Trading Information: | |
| - Next Trading Day: 2024-01-16 | |
| - Time Until Open: N/A (Market is open) | |
| - Time Until Close: 1h 30m | |
| Market Description: NYSE, NASDAQ, AMEX | |
| ``` | |
| ### Advanced Settings | |
| - **Ensemble Methods**: Enable/disable multi-algorithm ensemble | |
| - **Regime Detection**: Enable/disable market regime detection | |
| - **Stress Testing**: Enable/disable scenario analysis | |
| - **Enhanced Covariate Data**: Include market indices, sectors, commodities | |
| - **Sentiment Analysis**: Include news sentiment analysis | |
| - **Smoothing**: Choose from multiple smoothing algorithms | |
| ### Ensemble Weights | |
| Configure the weights for different prediction methods: | |
| - **Chronos Weight**: Weight for Amazon Chronos predictions | |
| - **Technical Weight**: Weight for technical analysis | |
| - **Statistical Weight**: Weight for statistical models | |
| ## 📈 Prediction Features | |
| ### Enhanced Uncertainty Quantification | |
| The system provides multiple uncertainty calculation methods: | |
| 1. **Basic Uncertainty**: Standard quantile-based uncertainty | |
| 2. **Skewness-Adjusted**: Accounts for asymmetric return distributions | |
| 3. **Volatility-Scaled**: Scales uncertainty based on historical volatility | |
| 4. **Market-Adjusted**: Adjusts uncertainty based on VIX and market conditions | |
| 5. **Time-Decay**: Uncertainty increases with prediction horizon | |
| 6. **Ensemble Uncertainty**: Combines all methods for robust estimates | |
| ### Volume Prediction Improvements | |
| - **Price-Volume Relationship**: Models the relationship between price movements and volume | |
| - **Momentum Effects**: Incorporates volume momentum and trends | |
| - **Market Condition Adjustments**: Adjusts predictions based on market volatility | |
| - **Uncertainty Quantification**: Provides confidence intervals for volume predictions | |
| ### Covariate Integration | |
| The system automatically collects and integrates: | |
| - **Market Indices**: S&P 500, Dow Jones, NASDAQ, VIX | |
| - **Sector Performance**: Financial, Technology, Energy, Healthcare ETFs | |
| - **Economic Indicators**: Treasury yields, dollar index, commodity prices | |
| - **Global Markets**: International indices and currencies | |
| ## 🔬 Advanced Features | |
| ### Regime Detection | |
| Uses Hidden Markov Models to detect market regimes: | |
| - **Bull Market**: High returns, low volatility | |
| - **Bear Market**: Low returns, high volatility | |
| - **Sideways Market**: Low returns, low volatility | |
| ### Stress Testing | |
| Performs scenario analysis under various market conditions: | |
| - **Market Crash**: -20% market decline | |
| - **Volatility Spike**: 50% increase in VIX | |
| - **Interest Rate Shock**: 100 basis point rate increase | |
| - **Sector Rotation**: Major sector performance shifts | |
| ### Sentiment Analysis | |
| - **News Sentiment**: Analyzes recent news articles for sentiment | |
| - **Confidence Scoring**: Provides confidence levels for sentiment analysis | |
| - **Integration**: Incorporates sentiment into prediction models | |
| ## 📊 Output Metrics | |
| ### Trading Signals | |
| - **RSI Signals**: Oversold/Overbought with confidence levels | |
| - **MACD Signals**: Buy/Sell with strength indicators | |
| - **Bollinger Bands**: Position within bands with breakout signals | |
| - **SMA Signals**: Trend following with crossover analysis | |
| ### Risk Metrics | |
| - **Sharpe Ratio**: Risk-adjusted return measure | |
| - **VaR**: Value at Risk at 95% confidence | |
| - **Maximum Drawdown**: Largest historical decline | |
| - **Beta**: Market correlation coefficient | |
| - **Volatility**: Historical and implied volatility | |
| ### Enhanced Features | |
| - **Covariate Data Usage**: Indicates which external data was used | |
| - **Sentiment Analysis**: News sentiment scores and confidence | |
| - **Advanced Uncertainty Methods**: List of uncertainty calculation methods used | |
| - **Regime-Aware Uncertainty**: Whether regime detection was applied | |
| - **Enhanced Volume Prediction**: Whether advanced volume modeling was used | |
| ## 🎯 Use Cases | |
| ### Long-Term Investment (Daily Analysis) | |
| - Portfolio management and asset allocation | |
| - Strategic investment decisions | |
| - Risk management and hedging | |
| - Sector rotation strategies | |
| ### Medium-Term Trading (Hourly Analysis) | |
| - Swing trading strategies | |
| - Position sizing and timing | |
| - Intraday volatility analysis | |
| - Momentum-based trading | |
| ### Short-Term Trading (15-Minute Analysis) | |
| - Scalping strategies | |
| - High-frequency trading | |
| - Micro-pattern recognition | |
| - Ultra-short-term momentum | |
| ## 🔧 Configuration | |
| ### Model Parameters | |
| - **Chronos Model**: `amazon/chronos-t5-large` (default) | |
| - **Context Window**: 64 time steps | |
| - **Prediction Length**: Configurable up to model limits | |
| - **Quantile Levels**: [0.1, 0.5, 0.9] for uncertainty estimation | |
| ### Data Sources | |
| - **Primary**: Yahoo Finance (yfinance) | |
| - **Covariates**: Market indices, ETFs, commodities, currencies | |
| - **Sentiment**: News articles via yfinance | |
| - **Economic Data**: Treasury yields, VIX, dollar index | |
| ## 📝 Notes | |
| - **Market Hours**: Hourly and 15-minute data only available during market hours | |
| - **Data Limitations**: Yahoo Finance has specific limits for intraday data | |
| - **Model Performance**: Chronos performs best with sufficient historical data | |
| - **Uncertainty**: All predictions include comprehensive uncertainty estimates | |
| - **Ensemble Weights**: Should sum to 1.0 for optimal performance | |
| ## 🤝 Contributing | |
| This system is designed to be extensible. Key areas for enhancement: | |
| - Additional covariate data sources | |
| - New uncertainty calculation methods | |
| - Advanced sentiment analysis techniques | |
| - Custom technical indicators | |
| - Alternative ensemble methods | |
| ## 📄 License | |
| This project is licensed under the Apache-2.0 License. | |
| ## 🙏 Acknowledgments | |
| - **Amazon Chronos**: Foundation model for time series forecasting | |
| - **Yahoo Finance**: Market data provider | |
| - **Gradio**: Web interface framework | |
| - **Plotly**: Interactive visualizations | |
| - **Scikit-learn**: Machine learning algorithms | |