QuantumVest

🔮 AI-Powered Predictive Investment Analytics Platform

QuantumVest is an advanced predictive investment analytics platform that leverages artificial intelligence, blockchain technology, and quantitative finance models to provide retail investors with actionable insights for smarter investment decisions.

Note: QuantumVest is currently under active development. Features and functionalities are being added and improved continuously to enhance user experience.

Table of Contents

• Overview
• Project Structure
• Key Features
• Technology Stack
• Architecture
• Installation and Setup
• Testing
• CI/CD Pipeline
• Documentation
• Contributing
• License

Overview

QuantumVest democratizes access to sophisticated investment analytics by providing retail investors with AI-driven predictions and insights previously available only to institutional investors. By combining machine learning algorithms with blockchain data transparency and quantitative finance models, the platform offers comprehensive market analysis, risk assessment, and investment recommendations across various asset classes.

Project Structure

The project is organized into several main components:

QuantumVest/
├── code/                   # Core backend logic, services, and shared utilities
├── docs/                   # Project documentation
├── infrastructure/         # DevOps, deployment, and infra-related code
├── mobile-frontend/        # Mobile application
├── web-frontend/           # Web dashboard
├── scripts/                # Automation, setup, and utility scripts
├── LICENSE                 # License information
├── README.md               # Project overview and instructions
├── eslint.config.js        # ESLint configuration
└── package.json            # Node.js project metadata and dependencies

Key Features

AI-Powered Market Predictions

• Trend Forecasting: Advanced time series models for predicting market movements
• Sentiment Analysis: NLP processing of news and social media for market sentiment
• Pattern Recognition: Identification of chart patterns and trading signals
• Anomaly Detection: Early warning system for unusual market behavior
• Correlation Analysis: Cross-asset correlation insights for diversification

Blockchain-Enhanced Analytics

• On-Chain Data Analysis: Insights from blockchain transaction patterns
• Whale Movement Tracking: Monitoring of large holder activities
• Smart Money Flow: Analysis of institutional investor behavior
• Network Health Metrics: Blockchain fundamentals assessment
• DeFi Protocol Analytics: Yield, TVL, and risk metrics for DeFi investments

Quantitative Investment Strategies

• Portfolio Optimization: Modern Portfolio Theory implementation
• Risk-Adjusted Returns: Sharpe, Sortino, and Calmar ratio calculations
• Monte Carlo Simulations: Probability-based outcome projections
• Factor Analysis: Multi-factor models for investment selection
• Algorithmic Strategy Backtesting: Historical performance validation

Personalized Investment Experience

• Risk Profiling: Customized risk tolerance assessment
• Goal-Based Planning: Investment recommendations aligned with financial goals
• Performance Dashboard: Real-time portfolio tracking and analysis
• Scenario Testing: "What-if" analysis for different market conditions
• Automated Alerts: Notifications for significant market events or opportunities

Technology Stack

Frontend

• Framework: React.js with TypeScript
• State Management: Redux Toolkit
• Styling: Tailwind CSS, Styled Components
• Data Visualization: D3.js, Recharts, TradingView
• Web3 Integration: ethers.js, web3.js

Backend

• API Framework: FastAPI, Flask
• Data Processing: Pandas, NumPy, SciPy
• Task Queue: Celery, Redis
• Authentication: JWT, OAuth2
• API Documentation: Swagger, ReDoc

AI/ML

• Frameworks: TensorFlow, PyTorch, scikit-learn
• Time Series Models: ARIMA, LSTM, Prophet
• NLP: BERT, Transformers, spaCy
• Feature Engineering: Feature-tools, tsfresh
• Model Serving: MLflow, TensorFlow Serving

Blockchain

• Networks: Ethereum, Binance Smart Chain
• Data Indexing: The Graph, Dune Analytics
• Smart Contracts: Solidity (for data collection)
• Web3 Libraries: web3.py, ethers.js
• Oracles: Chainlink (for market data)

Data Storage

• Relational DB: PostgreSQL
• Time Series DB: InfluxDB, TimescaleDB
• Caching: Redis
• Object Storage: AWS S3, MinIO
• Data Warehouse: Snowflake, BigQuery

DevOps

• Containerization: Docker
• Orchestration: Kubernetes
• CI/CD: GitHub Actions
• Monitoring: Prometheus, Grafana
• Infrastructure as Code: Terraform

Architecture

QuantumVest follows a modular microservices architecture with the following components:

QuantumVest/
├── Frontend Layer
│   ├── User Interface
│   ├── Data Visualization
│   ├── Authentication
│   └── Web3 Integration
├── Backend Services
│   ├── API Gateway
│   ├── User Service
│   ├── Analytics Service
│   ├── Notification Service
│   └── Authentication Service
├── AI Engine
│   ├── Prediction Models
│   ├── Sentiment Analysis
│   ├── Pattern Recognition
│   └── Risk Assessment
├── Blockchain Layer
│   ├── On-Chain Data Collector
│   ├── Whale Tracker
│   ├── Smart Money Analyzer
│   └── Network Health Monitor
├── Quantitative Engine
│   ├── Portfolio Optimizer
│   ├── Risk Calculator
│   ├── Strategy Backtester
│   └── Monte Carlo Simulator
└── Data Layer
    ├── Market Data
    ├── User Data
    ├── Model Training Data
    └── Blockchain Data

Installation and Setup

Prerequisites

• Node.js (v14+)
• Python (v3.8+)
• Docker and Docker Compose
• PostgreSQL
• Redis

Quick Start with Setup Script

# Clone the repository
git clone https://github.com/quantsingularity/QuantumVest.git
cd QuantumVest

# Run the setup script
./setup_quantumvest_env.sh

# Start the application
./run_quantumvest.sh

Manual Setup

1. Clone the repository:

git clone https://github.com/quantsingularity/QuantumVest.git
cd QuantumVest

2. Install frontend dependencies:

cd code/frontend
npm install

3. Install backend dependencies:

cd ../backend
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

4. Install blockchain dependencies:

cd ../blockchain
npm install

5. Set up environment variables:

   • Create .env files in both frontend and backend directories based on the provided .env.example files

6. Start the services:

# Start database and Redis
docker-compose up -d

# Start backend
cd ../backend
uvicorn main:app --reload

# Start frontend
cd ../frontend
npm start

Testing

The project maintains comprehensive test coverage across all components to ensure reliability and accuracy.

Test Coverage

Component	Coverage	Status
Frontend Components	78%	✅
Backend Services	85%	✅
AI Models	82%	✅
Blockchain Integration	75%	✅
Quantitative Engine	83%	✅
Data Processing	79%	✅
Overall	80%	✅

Unit Tests

• Frontend component tests with Jest and React Testing Library
• Backend API and service tests with pytest
• AI model validation tests
• Blockchain integration tests

Integration Tests

• End-to-end API tests
• Data pipeline tests
• Cross-service workflow tests
• User journey tests

Performance Tests

• Load testing for API endpoints
• Model inference performance tests
• Database query optimization tests
• Real-time data processing tests

Running Tests

# Run frontend tests
cd code/frontend
npm test

# Run backend tests
cd ../backend
pytest

# Run AI model tests
cd ../ai_models
python -m unittest discover

# Run all tests
./run_all_tests.sh

CI/CD Pipeline

QuantumVest uses GitHub Actions for continuous integration and deployment:

Stage	Control Area	Institutional-Grade Detail
Formatting Check	Change Triggers	Enforced on all push and pull_request events to main and develop
	Manual Oversight	On-demand execution via controlled workflow_dispatch
	Source Integrity	Full repository checkout with complete Git history for auditability
	Python Runtime Standardization	Python 3.10 with deterministic dependency caching
	Backend Code Hygiene	autoflake to detect unused imports/variables using non-mutating diff-based validation
	Backend Style Compliance	black --check to enforce institutional formatting standards
	Non-Intrusive Validation	Temporary workspace comparison to prevent unauthorized source modification
	Node.js Runtime Control	Node.js 18 with locked dependency installation via npm ci
	Web Frontend Formatting Control	Prettier checks for web-facing assets
	Mobile Frontend Formatting	Prettier enforcement for mobile application codebases
	Documentation Governance	Repository-wide Markdown formatting enforcement
	Infrastructure Configuration	Prettier validation for YAML/YML infrastructure definitions
	Compliance Gate	Any formatting deviation fails the pipeline and blocks merge

Documentation

Document	Path	Description
README	README.md	High-level overview, project scope, and repository entry point
Installation Guide	INSTALLATION.md	Step-by-step installation and environment setup
API Reference	API.md	Detailed documentation for all API endpoints
CLI Reference	CLI.md	Command-line interface usage, commands, and examples
User Guide	USAGE.md	Comprehensive end-user guide, workflows, and examples
Architecture Overview	ARCHITECTURE.md	System architecture, components, and design rationale
Configuration Guide	CONFIGURATION.md	Configuration options, environment variables, and tuning
Feature Matrix	FEATURE_MATRIX.md	Feature coverage, capabilities, and roadmap alignment
Contributing Guidelines	CONTRIBUTING.md	Contribution workflow, coding standards, and PR requirements
Troubleshooting	TROUBLESHOOTING.md	Common issues, diagnostics, and remediation steps

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.