README.md

QuantumVest Documentation

AI-Powered Predictive Investment Analytics Platform

Overview

QuantumVest is an enterprise-grade investment analytics platform that combines artificial intelligence, blockchain technology, and quantitative finance models to provide retail investors with institutional-quality insights. The platform offers real-time market predictions, portfolio optimization, risk management, and comprehensive analytics across multiple asset classes including stocks, cryptocurrencies, ETFs, bonds, commodities, and forex.

Quick Start

Get started with QuantumVest in 3 simple steps:

1. Clone and Setup

   git clone https://github.com/quantsingularity/QuantumVest.git
   cd QuantumVest
   ./scripts/setup_quantumvest_env.sh

2. Configure Environment

   # Backend: Create .env file in code/backend/
   cp code/backend/.env.example code/backend/.env
   # Edit .env with your database credentials and API keys

3. Start Services

   ./scripts/run_quantumvest.sh
   # Or start services individually:
   # Backend: cd code/backend && source venv/bin/activate && python app.py
   # Frontend: cd web-frontend && npm start

Access the application at http://localhost:3000 (frontend) and http://localhost:5000/api/v1 (backend API).

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Documentation Index

Getting Started

• Installation Guide — System prerequisites, installation options (Docker, pip, manual), and environment setup
• Quick Start Guide — First-time user walkthrough, account setup, and basic operations
• Configuration Guide — Environment variables, database setup, API keys, and service configuration

Core Documentation

• Usage Guide — Common workflows, CLI usage, library API usage, and practical examples
• API Reference — Complete REST API documentation with endpoints, parameters, and examples
• CLI Reference — Command-line interface commands, flags, and usage patterns
• Feature Matrix — Comprehensive feature catalog with module mapping and availability

Architecture & Development

• Architecture Overview — System design, module structure, data flow, and component diagrams
• Data Pipeline Architecture — ETL processes, data storage, feature engineering, and model serving
• AI Models Documentation — Machine learning models, training procedures, and inference APIs
• Blockchain Integration — Smart contracts, on-chain data collection, and Web3 integration

Advanced Topics

• Developer Guide — Contributing guidelines, code style, testing, and development workflow
• Infrastructure Guide — Kubernetes deployment, CI/CD, monitoring, and DevOps
• Troubleshooting Guide — Common issues, debugging tips, and FAQ

Examples & Tutorials

• Examples Directory — Working code examples demonstrating key features
  • Portfolio Management Example
  • AI Prediction Example
  • Risk Analysis Example

Additional Resources

• Technical Documentation — Deep technical specifications and design decisions
• User Manual — End-user guide for web and mobile applications
• Project Structure — Directory layout and file organization

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Key Features at a Glance

Category	Features
AI/ML	LSTM price predictions, sentiment analysis, pattern recognition, anomaly detection
Blockchain	On-chain analytics, whale tracking, smart money flow, DeFi metrics
Quantitative	Portfolio optimization, Monte Carlo simulations, risk-adjusted returns, factor analysis
Asset Classes	Stocks, crypto, ETFs, bonds, commodities, forex, options, futures
Risk Management	VaR calculation, stress testing, scenario analysis, exposure tracking
Authentication	JWT tokens, OAuth2, 2FA support, role-based access control
Real-time	WebSocket streaming, live market data, instant notifications
Compliance	KYC/AML status tracking, audit logging, regulatory reporting

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System Requirements

• Backend: Python 3.8+, PostgreSQL 12+, Redis 5+
• Frontend: Node.js 14+, npm 6+
• Mobile: Expo SDK 52+, React Native 0.76+
• Blockchain: Ethereum/BSC node access (optional)
• Infrastructure: Docker 20+, Kubernetes 1.20+ (for production)

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Technology Stack

Layer	Technologies
Frontend	React 17, TypeScript, Material-UI, D3.js, Chart.js, Web3.js
Backend	Flask 2.3, SQLAlchemy 2.0, Celery 5.3, Redis 5.0
AI/ML	TensorFlow 2.13, PyTorch 2.0, scikit-learn 1.3, Prophet 1.1
Data	PostgreSQL, InfluxDB, TimescaleDB, Pandas, NumPy
Blockchain	Web3.py 6.10, Solidity contracts, Ethers.js
DevOps	Docker, Kubernetes, GitHub Actions, Terraform, Ansible
Monitoring	Prometheus, Grafana, Sentry, ELK Stack

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Next Steps

1. New Users: Start with the Installation Guide
2. Developers: Read the Developer Guide and Architecture Overview
3. API Users: Check the API Reference and Examples
4. DevOps: See the Infrastructure Guide for deployment

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