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RiskOptimizer Documentation

Welcome to RiskOptimizer

RiskOptimizer is an advanced AI-powered portfolio risk management platform that combines traditional financial models with cutting-edge artificial intelligence and blockchain technology. This platform helps investors optimize their investment strategies, manage risk effectively, and make data-driven decisions.

Quick Navigation

Section Description Link
Installation Get started with RiskOptimizer installation INSTALLATION.md
Usage Guide Learn how to use the platform USAGE.md
API Reference Complete API documentation API.md
CLI Reference Command-line interface guide CLI.md
Configuration Configure the platform CONFIGURATION.md
Feature Matrix Overview of all features FEATURE_MATRIX.md
Architecture System architecture and design ARCHITECTURE.md
Examples Working examples and tutorials examples/
Contributing Contribution guidelines CONTRIBUTING.md
Troubleshooting Common issues and solutions TROUBLESHOOTING.md

Quick Start

Get RiskOptimizer running in 3 steps:

1. Clone and Setup

git clone https://github.com/quantsingularity/RiskOptimizer.git
cd RiskOptimizer
./scripts/setup_environment.sh

2. Install Dependencies

# Backend
cd code/backend
pip install -r requirements.txt

# web-frontend
cd ../..
cd web-frontend
npm install

# mobile-frontend
cd ../..
cd mobile-frontend
npm install

3. Start the Application

# Use the convenience script
./scripts/run_riskoptimizer.sh

# Or start components individually
# Backend: cd code/backend && python app.py
# Frontend: cd web-frontend && npm start

The API will be available at http://localhost:5000 and the web interface at http://localhost:3000.

Features

  • ✨ Performance monitoring with Prometheus metrics
  • 🔐 Advanced JWT-based authentication with refresh tokens
  • 📊 Real-time risk calculations with Redis caching
  • 🤖 AI-powered portfolio optimization models
  • ⛓️ Blockchain integration for transparent portfolio tracking
  • 📈 Advanced risk metrics (VaR, CVaR, Sharpe Ratio, Max Drawdown)
  • 🎯 Efficient frontier calculation for optimal portfolios
  • 📱 Responsive web dashboard and mobile app support

Key Features at a Glance

Risk Analysis

Calculate Value at Risk (VaR), Conditional VaR, stress testing, correlation analysis, and volatility forecasting using GARCH models.

Portfolio Optimization

Implement Modern Portfolio Theory, multi-objective optimization, automated rebalancing, and tax-efficient strategies.

AI-Powered Predictions

Leverage machine learning for market trend prediction, anomaly detection, sentiment analysis, and personalized recommendations.

Blockchain Integration

Transparent transaction records, smart contract automation, decentralized identity, and tokenized asset support.

System Requirements

Component Minimum Recommended
Python 3.8+ 3.10+
Node.js 14+ 18+
PostgreSQL 12+ 14+
Redis 5+ 7+
RAM 4GB 8GB+
Storage 10GB 20GB+

Technology Stack

Backend: Python (Flask), PostgreSQL, Redis, Celery
Frontend: React, TypeScript, D3.js, Recharts
AI/ML: TensorFlow, PyTorch, scikit-learn
Blockchain: Ethereum, Solidity, Web3.py
Infrastructure: Docker, Kubernetes, GitHub Actions

Documentation Structure

docs/
├── README.md                    # This file
├── INSTALLATION.md              # Installation guide
├── USAGE.md                     # Usage guide
├── API.md                       # API reference
├── CLI.md                       # CLI reference
├── CONFIGURATION.md             # Configuration guide
├── FEATURE_MATRIX.md            # Feature overview
├── ARCHITECTURE.md              # Architecture details
├── CONTRIBUTING.md              # Contribution guide
├── TROUBLESHOOTING.md           # Issue resolution
├── api/                         # API-specific docs
├── examples/                    # Code examples
│   ├── BASIC_USAGE.md
│   ├── ADVANCED_FEATURES.md
│   └── AI_OPTIMIZATION.md

Next Steps

  1. New Users: Start with INSTALLATION.md
  2. Developers: Read CONTRIBUTING.md and ARCHITECTURE.md
  3. API Integration: Check API.md and examples/
  4. Configuration: See CONFIGURATION.md for environment setup
  5. Troubleshooting: Visit TROUBLESHOOTING.md for common issues

Model Performance & Validation

See ML_MODEL_PERFORMANCE.md for full empirical validation:

  • Backtested VaR: All methods pass Kupiec unconditional coverage test (p > 0.05)
  • GBM VaR Model: Test RMSE 0.0041, 94.7 % coverage at 95 % confidence
  • Hybrid VaR (GBM + Historical): Best coverage at 95.1 %, pinball loss 0.00081
  • EVT (POT): Confirmed fat-tailed distributions (ξ > 0) for all assets
  • Max-Sharpe Portfolio: Out-of-sample Sharpe 1.15 vs. 0.83 equal-weight baseline
  • Parallel Engine: 5.9× speedup on 8-core CPU for Monte Carlo (100k scenarios)