SGC-Finance

Financial modeling and risk analysis with statistical rigor

Planned for finance.sgcx.org

Modular Architecture: Free Libraries + Finance Focus

The Finance Interface follows SGCX's anti-SAS philosophy: instead of paying for a bloated product with features you'll never use, get a laser-focused tool built specifically for financial risk analysis and modeling.

The SGCX Financial Model

Finance-Specific: Every feature designed for risk management and portfolio analysis
No Programming Required: Upload portfolios, configure models, download reports
Built on Free Libraries: PyRegression, Project Lacuna, and financial-specific modules
Pay for Value: Finance tools only, not marketing analytics or clinical features

The Performance Story (0-60 MPH)

SGC-Finance delivers institutional-grade risk analysis at unprecedented speed:

  • Portfolio VaR: Monte Carlo VaR calculation on 10,000+ positions in under 2 minutes
  • Stress Testing: CCAR-compliant stress tests with full documentation
  • Real-Time Risk: Intraday risk monitoring with automatic alerts
  • Regulatory Reporting: Basel III, FRTB reports generated automatically
  • Missing Data Handling: Robust analysis of incomplete financial datasets

Under the Hood (The V8 Engine)

Powered by the most comprehensive statistical toolkit in quantitative finance:

  • PyRegression: Factor models, credit risk modeling
  • PyTimeSeries: ARIMA, GARCH, VAR - core financial modeling
  • PyMVNMLE: Missing financial data handling
  • PyExtreme: Tail risk, VaR, catastrophic loss modeling
  • PyMultivariate: Portfolio optimization, factor analysis
  • PyBootstrap: Risk model validation, confidence intervals
  • PyEconometrics: Econometric modeling, policy analysis
  • PyDistributions: Custom distributions for financial modeling

What's NOT Included (Finance-Focused)

Even though finance uses the most libraries, we still exclude irrelevant tools:

  • ❌ Clinical/Medical Tools: No ROC curves, clinical prediction models
  • ❌ Insurance Mathematics: No claims reserving, life tables
  • ❌ Non-parametric Tests: Limited use in quantitative finance
  • ⚠️ Survival Analysis: Occasionally useful for default timing, but not core
  • ⚠️ Bayesian Methods: Growing in finance but still specialized

💰 Quantitative Finance Powerhouse

8 core libraries covering regression, time series, extreme value theory, multivariate analysis, econometrics, and more. The most comprehensive toolkit because finance demands mathematical sophistication.

Workflow: Zero Financial Programming

Designed for risk managers and portfolio analysts, not Python developers:

  1. Upload Data: Drag-and-drop portfolio files, market data, or risk datasets
  2. Select Analysis: Choose from risk measurement, optimization, or regulatory templates
  3. Configure Parameters: Use sliders for confidence levels, time horizons, constraints
  4. Run Analysis: GPU-powered calculations run automatically in background
  5. Download Results: Executive dashboards, detailed reports, regulatory submissions

Advanced Risk Analytics

State-of-the-art risk measurement and management tools:

  • Value at Risk (VaR): Historical simulation, Monte Carlo, parametric methods
  • Expected Shortfall: Coherent risk measures and tail risk analysis
  • Stress Testing: Scenario analysis, sensitivity testing, reverse stress testing
  • Credit Risk: PD, LGD, EAD modeling with missing data handling
  • Market Risk: Delta, gamma, vega calculations with uncertainty quantification
  • Operational Risk: Advanced measurement approaches and loss distribution modeling

Portfolio Management

Modern portfolio theory with computational advantages:

  • Mean-Variance Optimization: GPU-accelerated efficient frontier calculation
  • Black-Litterman: Bayesian portfolio construction with view incorporation
  • Risk Parity: Equal risk contribution and hierarchical risk parity
  • Factor Models: Multi-factor risk models with statistical validation
  • Alternative Investments: Private equity, hedge fund, and real estate modeling
  • ESG Integration: Environmental, social, governance factor integration

Target Users

Designed for quantitative finance professionals:

  • Quantitative Analysts: Model development and validation specialists
  • Risk Managers: Enterprise risk measurement and reporting
  • Portfolio Managers: Asset allocation and investment strategy
  • Regulatory Reporting: Compliance and regulatory capital teams
  • Financial Researchers: Academic and industry research applications

Missing Data Expertise

Financial Data Challenges

Market Data Gaps: Holiday closures, trading halts, data vendor issues
Credit Data: Incomplete borrower information, regulatory restrictions
Alternative Data: Sparse alternative datasets with systematic missingness
Historical Series: Long-term datasets with structural breaks and gaps

Regulatory Frameworks

Built-in support for major regulatory requirements:

  • Basel III: Capital adequacy, liquidity coverage, leverage ratios
  • CCAR/DFAST: Comprehensive capital analysis and review
  • FRTB: Fundamental review of the trading book
  • IFRS 9: Expected credit loss modeling and impairment
  • Solvency II: Insurance regulatory capital requirements
  • MiFID II: Best execution and transaction cost analysis

Technology Advantages

Modern computational infrastructure for financial modeling:

  • GPU Acceleration: Massive parallelization for Monte Carlo simulations
  • Cloud Scalability: Elastic compute for large-scale risk calculations
  • Real-Time Analytics: Low-latency risk monitoring and alerting
  • API Integration: Seamless integration with market data providers
  • Distributed Computing: Multi-node processing for enterprise-scale analysis

Industry Applications

Specialized solutions for different financial sectors:

  • Investment Banks: Trading book analytics, regulatory capital, stress testing
  • Asset Managers: Portfolio optimization, risk attribution, performance analysis
  • Insurance Companies: Asset-liability management, Solvency II compliance
  • Hedge Funds: Alternative risk measures, factor analysis, strategy optimization
  • Central Banks: Systemic risk monitoring, monetary policy analysis
  • Fintech: Credit scoring, algorithmic trading, robo-advisory platforms

Data Integration

Market Data: Bloomberg, Refinitiv, direct exchange feeds
Alternative Data: Satellite imagery, social media, IoT sensors
Internal Systems: Risk management systems, trading platforms, accounting systems
Regulatory Data: Central bank datasets, regulatory reporting platforms

Security and Compliance

Enterprise-grade security for financial institutions:

  • Data Encryption: End-to-end encryption for sensitive financial data
  • Access Controls: Role-based access with multi-factor authentication
  • Audit Logging: Comprehensive audit trails for regulatory compliance
  • Geographic Controls: Data residency requirements and cross-border restrictions
  • Penetration Testing: Regular security assessments and vulnerability scanning

Competitive Positioning

Advantages over existing financial software:

  • vs. Bloomberg: Deep statistical expertise, missing data handling, lower cost
  • vs. MSCI RiskMetrics: Modern architecture, GPU acceleration, statistical rigor
  • vs. SAS Risk: Better UX, cloud-native, advanced missing data methods
  • vs. R/Python: No programming required, enterprise support, regulatory focus

Pricing Strategy

Flexible pricing for different organizational needs:

  • Individual Licenses: Analysts and researchers at smaller firms
  • Team Licenses: Risk management and quantitative teams
  • Enterprise Solutions: Bank-wide deployments with dedicated support
  • Cloud Services: Pay-per-use for computational-intensive analysis
Request Demo ← All Projects