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[![GitHub stars](https://img.shields.io/github/stars/The-Swarm-Corporation/Legal-Swarm-Template?style=social)](https://github.com/The-Swarm-Corporation/Legal-Swarm-Template)
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[![Swarms Framework](https://img.shields.io/badge/Built%20with-Swarms-blue)](https://github.com/kyegomez/swarms)
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# ATLAS: Advanced Time-series Learning and Analysis System
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ATLAS is a sophisticated real-time risk analysis system designed for institutional-grade market risk assessment. Built with high-frequency trading (HFT) capabilities and advanced machine learning techniques, ATLAS provides continuous volatility predictions and risk metrics using both historical patterns and real-time market data.
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### Core Capabilities
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#### 1. Multi-horizon Risk Analysis
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- Real-time volatility predictions (5-day, 21-day, 63-day horizons)
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- Adaptive regime detection and risk adjustment
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- Multiple volatility estimation methods:
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- Close-to-close volatility
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- Parkinson estimator (high-low range)
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- Garman-Klass estimator (OHLC)
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#### 2. Feature Engineering
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- Market Microstructure Features:
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- Order flow imbalance
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- Price impact measurements
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- Volume-price relationships
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- Technical Indicators:
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- RSI (Relative Strength Index)
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- MACD (Moving Average Convergence Divergence)
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- ATR (Average True Range)
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- Custom momentum indicators
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- Volatility Features:
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- Multi-timeframe realized volatility
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- Implied vs. realized volatility spread
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- Volatility regime indicators
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#### 3. Model Architecture
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- Primary Model: LightGBM Regressor
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- Optimized hyperparameters for financial time series
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- Early stopping and validation
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- Feature importance tracking
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- Time Series Handling:
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- Look-ahead bias prevention
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- Time series cross-validation
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- Gap-aware training
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#### 4. Real-time Processing
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- Data Collection:
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- 1-minute interval updates
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- Efficient data queueing system
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- Robust error handling
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- Live Predictions:
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- Continuous model updating
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- Anomaly detection
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- Prediction confidence scoring
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### Performance Metrics
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1. Prediction Accuracy:
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- R² score on validation sets
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- Mean Absolute Percentage Error (MAPE)
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- Directional accuracy
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2. Risk Metrics:
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- Value at Risk (VaR)
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- Conditional VaR (CVaR)
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- Maximum drawdown
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- Sharpe ratio
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- Calmar ratio
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### Key Features
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1. Robustness:
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- Fallback mechanisms for data sources
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- Automatic error recovery
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- Cache management
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- Thread-safe operations
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2. Scalability:
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- Parallel processing capabilities
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- Efficient memory management
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- Optimized numerical computations
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3. Monitoring:
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- Comprehensive logging system
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- Performance tracking
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- Feature importance analysis
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- Model drift detection
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### Use Cases
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1. Portfolio Risk Management:
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- Real-time portfolio risk assessment
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- VaR calculations
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- Risk factor decomposition
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2. Trading Systems:
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- Volatility regime detection
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- Risk-adjusted position sizing
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- Market stress indicators
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3. Risk Reporting:
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- Automated risk reports
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- Real-time alerts
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- Performance attribution
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### Implementation Requirements
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1. Technical Stack:
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```python
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numpy>=1.24.0
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pandas>=2.0.0
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lightgbm>=4.1.0
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scikit-learn>=1.3.0
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yfinance>=0.2.31
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talib>=0.4.28
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numba>=0.58.0
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loguru>=0.7.0
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```
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2. System Requirements:
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- Multi-core CPU for parallel processing
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- Minimum 16GB RAM recommended
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- Stable internet connection for real-time data
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- Redis server (optional, for caching)
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## 🚀 Quick Start
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### Future Development Roadmap
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```bash
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# Clone the repository
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git clone https://github.com/The-Swarm-Corporation/Swarms-Example-1-Click-Template.git
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1. Enhanced Capabilities:
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- Multi-asset correlation analysis
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- Alternative data integration
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- Deep learning models for regime detection
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- Options-implied volatility incorporation
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2. System Improvements:
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- Distributed computing support
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- GPU acceleration
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- Advanced backtesting framework
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- Real-time visualization dashboard
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### Model Limitations
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1. Data Dependencies:
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- Reliance on quality of market data
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- Potential gaps in high-frequency data
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- Market hours constraints
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2. Model Constraints:
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- Regime change adaptation lag
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- Black swan event handling
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- Market microstructure noise
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### Performance Monitoring
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The system includes continuous monitoring of:
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1. Prediction accuracy vs. realized volatility
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2. Feature importance stability
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3. Model drift indicators
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4. System resource utilization
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5. Data quality metrics
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### Conclusion
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ATLAS represents a production-ready risk analysis system suitable for institutional use. Its combination of robust engineering, sophisticated modeling, and real-time capabilities makes it particularly valuable for active risk management and trading applications.
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The system's modular design allows for easy extension and customization, while its focus on reliability and accuracy makes it suitable for mission-critical applications in financial risk management.
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# Install requirements
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pip3 install -r requirements.txt
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# Set your task in the .env file or pass it in the yaml file on the bottom `task:`
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export WORKSPACE_DIR="agent_workspace"
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export GROQ_API_KEY=""
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# Run the swarm
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python3 main.py
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```
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## 🛠 Built With

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