RiskX is a comprehensive risk intelligence platform designed to help U.S. financial institutions and supply chain operators detect vulnerabilities, predict disruptions, and make informed decisions in real time. The system integrates explainable machine learning and predictive analytics to enhance transparency, resilience, and national economic stability.
Built as a professional "Bloomberg Terminal for Risk Intelligence," RiskX provides transparent, ethical insights through rigorous quantitative analysis and explainable model outputs. The platform serves policymakers, risk officers, financial institutions, and researchers with comprehensive risk assessment capabilities.
To create a transparent, open-source risk intelligence platform that maps, quantifies, and explains risk propagation across finance and supply chains using ethical machine learning principles, supporting national economic stability through data-driven insights.
- Live knowledge graph linking economic, financial, and supply chain entities
- Real-time risk propagation modeling across interconnected systems
- Comprehensive visualization of systemic interdependencies
- Quantitative risk scoring with confidence intervals
- Predicts disruptions (defaults, shortages, cyber events) with transparent justifications
- SHAP and LIME-based feature importance analysis
- Model interpretability reports with confidence scores
- Algorithmic bias detection and fairness assessment
- Real-time monitoring of national risk indicators
- Interactive network visualizations of risk propagation
- Policy simulation capabilities with impact modeling
- Bloomberg Terminal-inspired professional interface
- 12+ government and institutional data sources
- Economic indicators from FRED, BEA, BLS, IMF, BIS
- Trade data from U.S. Census, UN Comtrade, World Bank
- Disruption signals from NOAA, USGS, CISA, GDELT
- FastAPI Framework: High-performance API with automatic OpenAPI documentation
- Machine Learning Pipeline: Scikit-learn, XGBoost models with SHAP explainability
- Database Layer: PostgreSQL for persistent storage, Redis for high-speed caching
- ETL Orchestration: Apache Airflow for data pipeline management
- Model Registry: MLflow for experiment tracking and model versioning
- Next.js Framework: Server-side rendering with TypeScript strict mode
- Visualization Engine: D3.js for network graphs, Chart.js for time series
- Real-time Updates: WebSocket connections with HTTP fallback
- Professional UI: Tailwind CSS with Bloomberg Terminal-inspired design
- Component Architecture: Modular React components with error boundaries
- Multi-layer Caching: Redis primary, PostgreSQL persistent, file fallback
- Real-time Processing: WebSocket streaming for live risk updates
- Data Validation: Comprehensive quality checks and anomaly detection
- Backup Systems: Automated data archival and disaster recovery
- Authentication: JWT-based authentication with role-based access control
- Input Validation: Comprehensive sanitization and SQL injection prevention
- Rate Limiting: DDoS protection and API abuse prevention
- Data Privacy: GDPR-compliant data handling and processing
- Python: 3.11 or higher
- Node.js: 18 LTS or higher
- Database: PostgreSQL 15+ or Docker
- Cache: Redis 7+ or Docker
- Memory: 8GB RAM minimum, 16GB recommended
- Storage: 10GB available space
git clone https://github.com/risksintelligence/riskx.git
cd riskx
cp .env.example .env
docker-compose up --build# 1. Environment Setup
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txt
# 2. Database Setup
createdb riskx
python scripts/setup/init_db.py
# 3. Frontend Setup
cd frontend
npm install
npm run build
# 4. Start Services
# Terminal 1: Backend
uvicorn src.api.main:app --reload --host 0.0.0.0 --port 8000
# Terminal 2: Frontend
cd frontend && npm run dev
# Terminal 3: Data Pipeline (Optional)
python etl/scheduler.pyRegister for these free API keys and add to your .env file:
- FRED (Federal Reserve): https://fred.stlouisfed.org/docs/api/api_key.html
- Census Bureau: https://api.census.gov/data/key_signup.html
- Bureau of Economic Analysis: https://apps.bea.gov/API/signup/
- Bureau of Labor Statistics: https://www.bls.gov/developers/api_signature_v2.htm
# Pre-commit Verification (Required)
npm run typecheck && npm run lint && npm run build
python -m pytest tests/ --tb=short
python -m py_compile src/**/*.py
curl -f http://localhost:8000/health
# Code Quality Standards
black src/ tests/ # Python code formatting
isort src/ tests/ # Python import sorting
npm run lint # TypeScript/React linting
npm run type-check # TypeScript strict validation- Color Palette: Navy blue (#1e3a8a), charcoal gray (#374151), white (#ffffff) exclusively
- Typography: Inter font family, professional hierarchy
- Language: Financial and risk management terminology only
- Interface: Bloomberg Terminal-inspired layout with conservative styling
- Accessibility: WCAG 2.1 AA compliance mandatory
- Zero Placeholder Code: All functions must contain complete, working implementations
- Multi-layer Caching: Every data source requires Redis, PostgreSQL, and file fallback
- Comprehensive Testing: Unit, integration, and end-to-end test coverage required
- Security First: Input validation, SQL injection prevention, XSS protection
- Error Handling: Graceful degradation and comprehensive logging
- Real-time: Risk calculations, system health monitoring
- Hourly: Market data updates, disruption event detection
- Daily: Economic indicators, trade statistics, weather data
- Weekly: Model retraining, performance evaluation, bias testing
- Monthly: Data archival, comprehensive system audits
Government APIs (12+ sources) → ETL Validation → Cache Layers → Risk Models → Dashboard
↓ ↓ ↓ ↓ ↓
FRED, Census, BEA, Quality Checks Redis/Postgres ML Pipeline Live Updates
BLS, NOAA, CISA, etc. Anomaly Detection File Fallback SHAP/LIME WebSocket
- Container Orchestration: Docker Compose or Kubernetes
- Database: PostgreSQL 15+ with connection pooling
- Cache: Redis Cluster for high availability
- Load Balancer: Nginx or equivalent for API and frontend
- Monitoring: Prometheus/Grafana for system metrics
# Production deployment verification
./test-deployment.sh # Pre-deployment validation
docker-compose -f docker-compose.prod.yml up --build
curl -f https://your-domain.com/api/v1/health- API Status:
/api/v1/health- Comprehensive system health - Data Pipeline:
/api/v1/health/data- Data source connectivity - ML Models:
/api/v1/health/models- Model performance metrics - Cache System:
/api/v1/health/cache- Cache layer status
RiskX: Risk Intelligence Observatory
Version: [Current Version]
URL: https://github.com/risksintelligence/riskx
- Open Dataset: U.S. Risk Interdependency Dataset (US-RID)
- Research APIs: Programmatic access for academic research
- Documentation: Comprehensive methodology and technical documentation
- Reproducibility: All models and analyses fully reproducible
- Fork and Clone: Create personal fork of the repository
- Feature Branch: Create feature-specific branch from main
- Implementation: Follow all professional standards and testing requirements
- Quality Verification: Pass all verification protocols
- Pull Request: Submit with comprehensive description and test results
- Security Review: All changes undergo security assessment
- Performance Impact: Benchmark testing for performance-critical changes
- Documentation: Update technical documentation for API changes
- Testing: New features require corresponding test coverage
This project is licensed under the MIT License - see the LICENSE file for details.
For technical support, feature requests, or research collaboration:
- Issues: GitHub Issues for bug reports and feature requests
- Discussions: GitHub Discussions for technical questions
- Security: security@risksintelligence.org for security-related issues
- Research: research@risksintelligence.org for academic collaboration