# Technical Algorithmic Design Documentation -> Backtest Simulation

class AlgorithmicDesignDocumentation:
"""
Unified Algorithmic Design Documentation System

A comprehensive documentation framework that captures the complete backtesting
methodology through functional docstrings. This system provides a clean interface
for organizing and presenting algorithmic trading system documentation in Notion.

System Architecture:
1. Methodology Documentation
2. Implementation Details
3. Performance Analysis
4. System Integration
"""

def document_trading_system(self) -> dict:
    """
    Main Documentation Interface

    Creates a structured representation of the entire trading system documentation.
    This serves as the root page in Notion with hierarchical organization.

    Documentation Hierarchy:
    └── Trading System Overview
        ├── Data Processing Pipeline
        ├── Residuals Analysis Engine
        ├── Strategy Implementation
        └── Performance Analytics

    Returns:
        dict: Complete documentation structure
    """
    return {
        "title": "Cross-Sectional Trading System Documentation",
        "methodology": self._document_methodology(),
        "implementation": self._document_implementation(),
        "analysis": self._document_analysis()
    }

def _document_methodology(self) -> dict:
    """
    Core Methodology Documentation

    Details the theoretical framework and mathematical foundations
    of the trading system.

    Components:
    1. Residuals Calculation
       - NAV adjustments
       - Premium/discount metrics
       - Cross-sectional normalization

    2. Signal Generation
       - Statistical arbitrage approach
       - Time series momentum overlay
       - Risk-adjusted positioning

    3. Strategy Logic
       - Entry/exit rules
       - Position sizing
       - Risk management
    """
    return {
        "residuals_methodology": {
            "calculation": """
                Residuals are computed using a two-stage process:
                1. NAV-adjusted returns calculation
                2. Cross-sectional residuals estimation
                3. Cumulative residuals aggregation
            """,
            "adjustments": """
                NAV adjustments incorporate:
                - Premium/discount metrics
                - Leverage ratios
                - Market price yield impacts
            """,
            "signals": """
                Signal generation process:
                1. Raw residuals calculation
                2. Time series momentum overlay
                3. Cross-sectional ranking
            """
        },
        "strategy_rules": {
            "entry_conditions": [
                "Residual threshold breaches",
                "NAV deviation signals",
                "Volume conditions"
            ],
            "position_sizing": [
                "Risk-adjusted allocation",
                "Cross-sectional weights",
                "Leverage constraints"
            ],
            "risk_management": [
                "Stop-loss implementation",
                "Correlation controls",
                "Drawdown limits"
            ]
        }
    }

def _document_implementation(self) -> dict:
    """
    Implementation Documentation

    Details the technical implementation of the trading system.

    Structure:
    1. Data Processing
       - Input data handling
       - Feature engineering
       - Data validation

    2. Model Implementation
       - LightGBM configuration
       - Parameter optimization
       - Model validation

    3. Execution Framework
       - Signal processing
       - Order management
       - Risk controls
    """
    return {
        "data_processing": {
            "input_handling": """
                Data preprocessing pipeline:
                1. Time series alignment
                2. Missing data handling
                3. Feature engineering
            """,
            "validation": """
                Data validation checks:
                1. Data quality metrics
                2. Consistency checks
                3. Outlier detection
            """
        },
        "model_implementation": {
            "configuration": """
                LightGBM model setup:
                1. Feature selection
                2. Parameter optimization
                3. Cross-validation
            """,
            "validation": """
                Model validation process:
                1. Out-of-sample testing
                2. Performance metrics
                3. Robustness checks
            """
        }
    }

def _document_analysis(self) -> dict:
    """
    Performance Analysis Documentation

    Comprehensive framework for analyzing trading system performance.

    Components:
    1. Return Metrics
       - Absolute returns
       - Risk-adjusted returns
       - Attribution analysis

    2. Risk Metrics
       - Volatility analysis
       - Drawdown analysis
       - Correlation analysis

    3. System Metrics
       - Execution efficiency
       - Signal quality
       - Strategy capacity
    """
    return {
        "performance_metrics": {
            "returns": [
                "Cumulative returns",
                "Annualized returns",
                "Rolling returns"
            ],
            "risk_metrics": [
                "Volatility (various timeframes)",
                "Value at Risk (VaR)",
                "Expected Shortfall"
            ],
            "ratios": [
                "Sharpe Ratio",
                "Sortino Ratio",
                "Information Ratio"
            ]
        },
        "analysis_framework": {
            "methodology": """
                Performance analysis framework:
                1. Return decomposition
                2. Risk attribution
                3. Strategy analytics
            """,
            "visualization": """
                Visualization components:
                1. Performance charts
                2. Risk dashboards
                3. Attribution analysis
            """
        }
    }