AI-Powered Wealth Management and Robo-Advisory Platform
QuantumWealth is a production-grade, fully containerized Django platform combining modern portfolio theory, machine learning, and real-time market data to deliver institutional-grade wealth management to individual investors.
QuantumWealth/
|-- code/
| |-- backend/ Django REST API, database models, business logic
| |-- ai_models/ AI and ML modules (portfolio, risk, tax, sentiment, backtesting)
|-- docs/ Full project documentation
|-- infrastructure/ Nginx configuration, PostgreSQL initialization
|-- scripts/ Utility scripts
|-- docker-compose.yml Full-stack container orchestration
|-- README.md This file
cp code/backend/.env.example code/backend/.env
# Edit .env: set SECRET_KEY and DB_PASSWORD
docker compose up --build -d
docker compose exec backend python manage.py seed_demo_data| Service | URL |
|---|---|
| Swagger UI | http://localhost/api/docs/ |
| ReDoc | http://localhost/api/redoc/ |
| Django Admin | http://localhost/admin/ |
Demo login: demo@quantumwealth.ai / Demo1234!
cd code/backend
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env
python manage.py migrate
python manage.py seed_demo_data
python manage.py runserver| Module | Algorithms |
|---|---|
| Portfolio Optimizer | Mean-Variance, Black-Litterman, Risk Parity, Hierarchical Risk Parity |
| Risk Engine | Historical VaR, Parametric VaR, CVaR, Monte Carlo GBM, 5 Stress Scenarios |
| Robo Advisor | FV/PMT Goal Planning, ERC Rebalancing, Concentration Detection |
| Market Predictor | GBM Price Forecasting, Rolling Regime Detection, RSI + SMA Indicators |
| Tax Optimizer | Greedy Harvest Scheduling, After-Tax Return Model, Wash-Sale Calendar |
| Sentiment Analyzer | VADER News Scoring, Momentum, Volume, RSI Composite Signal |
| Factor Models | Fama-French 5-Factor OLS, BHB Attribution, Sector Decomposition |
| Backtester | Event-Driven Simulation, Transaction Costs, Benchmark Comparison |
| Anomaly Detector | Isolation Forest, Z-Score Outliers, Wash-Sale Clustering |
| Document | Description |
|---|---|
| docs/overview.md | Platform goals, architecture, technology stack |
| docs/api-reference.md | Complete endpoint reference with request and response schemas |
| docs/ai-models.md | Mathematical foundations for all AI and ML modules |
| docs/database-schema.md | Full table definitions with column types and indexes |
| docs/architecture.md | System design, request lifecycle, security model, scalability |
| docs/developer-guide.md | Contributing guide, code style, testing patterns |
| docs/deployment.md | Local, Docker, and production deployment instructions |
| docs/testing.md | Test suite overview and instructions |
| docs/changelog.md | Version history and change log |
MIT License. See LICENSE file for details.