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Margin Risk Management System

Overview

The Margin Risk Management System is a comprehensive margin analysis and risk management platform designed for financial institutions handling complex portfolios containing both equity and options positions. It provides automated PDF processing, sophisticated risk calculations, and real-time monitoring capabilities.

Key Features

📄 Automated Document Processing

  • OCR Technology: Advanced text extraction from Goldman Sachs customer margin reports
  • Intelligent Parsing: Automatic detection and classification of equity vs. options positions
  • Multi-page Support: Handles complex reports across multiple pages
  • Data Validation: Built-in quality checks and error detection

📊 Position Analysis

  • Real-time Portfolio Valuation: Live market value calculations
  • Risk Metrics: Comprehensive risk assessment including concentration analysis
  • Greeks Calculations: Delta, gamma, theta, vega for options positions
  • Margin Requirements: Reg T, FINRA, and house requirement calculations

🎯 Risk Dashboard

  • Portfolio Overview: High-level risk metrics and alerts
  • Concentration Monitoring: Position-level and sector-level concentration limits
  • Options Analytics: Time decay analysis, moneyness calculations
  • Historical Tracking: Time-series risk metric evolution

Architecture Components

MarginIQ Application

The primary Streamlit-based interface providing:

📤 Upload & Process     - PDF ingestion and OCR processing
📊 Position Analysis    - Detailed position-level analytics  
📈 Risk Dashboard       - Portfolio risk monitoring
🔍 Data QA             - Data quality validation
📈 Stock QA            - Equity-specific quality checks
📞 Options QA          - Options-specific validation
🎯 Risk Control        - Real-time risk management

Core Processing Engine

  • Margin Extractor: OCR-based PDF processing engine
  • Position Classifier: Equity vs. options detection logic
  • Risk Calculator: Portfolio-level risk metric computation
  • Data Validator: Quality assurance and error detection

Storage Architecture

  • Job-Based System: Each processing job creates separate data files
  • Parquet Format: Efficient columnar storage for analytical workloads
  • Metadata Tracking: Complete audit trail and processing history

Use Cases

📋 Daily Operations

Morning Risk Assessment 

# Upload overnight margin reports
# Review position changes
# Validate data quality
# Generate risk alerts

Intraday Monitoring - Real-time position tracking - Concentration limit monitoring
- Options time decay analysis - Margin requirement updates

End-of-Day Reporting - Portfolio risk summary - Regulatory reporting prep - Performance attribution - Risk limit compliance

🔬 Analysis Workflows

New Position Analysis 1. Upload current margin report 2. Review position classifications 3. Analyze concentration impact 4. Assess options greeks exposure 5. Generate risk recommendations

Portfolio Optimization 1. Historical risk analysis 2. Scenario modeling 3. Concentration rebalancing 4. Options strategy evaluation

⚠️ Risk Management

Concentration Monitoring - Position-level concentration limits - Sector diversification requirements - Single-name exposure tracking - Correlation analysis

Options Risk Management - Time decay (theta) monitoring - Volatility exposure (vega) tracking - Delta hedging requirements - Expiration management

Best Practices

🎯 Data Quality

Quality Assurance Workflow

  1. Always run Data QA after processing new reports
  2. Validate option classifications using Options QA page
  3. Cross-check equity positions with Stock QA tools
  4. Review processing logs for extraction errors

📊 Analysis Standards

Position Classification Rules 

# Options Detection Logic
if 'CALL' in symbol or 'PUT' in symbol:
    asset_class = 'option'
elif option_price is not None:
    asset_class = 'option'
else:
    asset_class = 'equity'

Risk Calculation Methodology - Market values include appropriate multipliers (100x for options) - Margin requirements follow Reg T guidelines - Concentration percentages based on absolute market values - Greeks calculated using industry-standard models

🔒 Security & Compliance

Data Handling - No sensitive data in logs or error messages - Secure PDF processing with automatic cleanup - Audit trail for all processing activities - Job-based data isolation

Regulatory Compliance - Reg T margin requirement calculations - FINRA position reporting formats - Risk limit monitoring and alerting - Historical data retention policies

Integration Points

📡 Data Sources

  • Goldman Sachs customer margin reports
  • Real-time market data feeds
  • Reference data for symbol mapping
  • Historical pricing information

🔄 Output Formats

  • Interactive Streamlit dashboards
  • Parquet files for analytical processing
  • CSV exports for reporting systems
  • JSON APIs for system integration

🔗 System Dependencies

  • OCR Engine: Tesseract for text extraction
  • Data Processing: Pandas, NumPy for calculations
  • Visualization: Plotly, Streamlit for interfaces
  • Storage: Parquet files, metadata tracking

Common Workflows

Initial Setup

# Launch the risk control system
python launch.py
# Select: 📊 Risk Control Dashboard

Daily Processing

  1. Upload Reports: Use Upload & Process page
  2. Quality Check: Run through QA pages
  3. Risk Analysis: Review Risk Dashboard
  4. Generate Alerts: Monitor concentration limits

Troubleshooting

Common Issues

  • OCR Errors: Usually caused by poor PDF quality or unusual formatting
  • Classification Issues: Manual review required for complex instruments
  • Data Validation Failures: Check source document for inconsistencies
  • Performance Issues: Large portfolios may require processing optimization

For detailed technical implementation, see the Margin ELT Loading Guide.

Support Resources