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QuantStream Analytics Dashboard

A comprehensive real-time dashboard and monitoring system for financial analytics and trading operations.

🌟 Features

Real-time Dashboard

  • Live Market Data: Real-time candlestick charts with sub-second updates
  • Technical Indicators: Bollinger Bands, RSI, MACD, Moving Averages
  • Portfolio Tracking: Real-time P&L, position management, performance analytics
  • Interactive Charts: Zoom, pan, drill-down capabilities with Plotly
  • Anomaly Detection: AI-powered anomaly alerts and notifications
  • Mobile Responsive: Cross-device compatibility

Monitoring System

  • System Health: CPU, memory, disk, network monitoring
  • Performance Metrics: API response times, throughput, error rates
  • Service Monitoring: Database, Redis, WebSocket, API health checks
  • Alert Management: Configurable alerts with multiple notification channels
  • Data Quality: Real-time data quality metrics and validation

🏗️ Architecture

Frontend (Streamlit)

src/dashboard/frontend/
├── components/          # Reusable UI components
│   ├── sidebar.py      # Navigation and settings
│   ├── market_data.py  # Live market charts
│   ├── technical_indicators.py
│   ├── portfolio.py    # Portfolio management
│   ├── alerts.py       # Alert management
│   └── system_metrics.py
├── pages/              # Dashboard pages
├── utils/              # Frontend utilities
│   ├── config.py       # Configuration management
│   ├── auth.py         # Authentication
│   └── metrics.py      # User analytics
└── app.py              # Main application

Backend (FastAPI + WebSocket)

src/dashboard/backend/
├── api/                # REST API endpoints
│   ├── main.py         # FastAPI application
│   └── endpoints/      # API routes
│       ├── market_data.py
│       ├── portfolio.py
│       ├── alerts.py
│       └── system_metrics.py
├── websocket/          # Real-time streaming
│   └── websocket_manager.py
├── services/           # Business logic
│   ├── database_service.py
│   ├── redis_service.py
│   └── auth_service.py
└── models/             # Data models

Monitoring Infrastructure

src/monitoring/
├── prometheus/         # Metrics collection
│   └── metrics_collector.py
├── grafana/           # Dashboard configs
└── alerts/            # Alert management

🚀 Quick Start

Prerequisites

  • Python 3.8+
  • Docker & Docker Compose
  • PostgreSQL
  • Redis

Installation

  1. Clone the repository
git clone <repository-url>
cd quantstream-dashboard
  1. Install dependencies
pip install -r requirements.txt
  1. Start infrastructure services
docker-compose up -d postgres redis prometheus grafana
  1. Configure environment
cp .env.example .env
# Edit .env with your configuration
  1. Start the dashboard
# Frontend
streamlit run src/dashboard/app.py

# Backend API
cd src/dashboard/backend && python -m api.main

# WebSocket server (automatically started with API)

Docker Deployment

Start all services:

docker-compose up -d

Services will be available at:

📊 Key Components

1. Market Data Component

  • Real-time streaming: Sub-second price updates via WebSocket
  • Multiple timeframes: 1m, 5m, 15m, 1h, 1d, 1w, 1m
  • Technical analysis: 20+ technical indicators
  • Data sources: YFinance (demo), extensible to real feeds

2. Portfolio Management

  • Position tracking: Real-time P&L calculations
  • Performance analytics: Sharpe ratio, max drawdown, alpha/beta
  • Risk metrics: VaR, concentration risk, sector exposure
  • Transaction history: Complete audit trail
  • Tax reporting: Tax lot tracking, capital gains

3. Alert System

  • Price alerts: Target prices, percentage moves
  • Volume alerts: Unusual volume detection
  • Technical alerts: Indicator-based signals
  • Anomaly detection: AI-powered pattern recognition
  • Multi-channel notifications: Email, Slack, webhooks

4. System Monitoring

  • Infrastructure metrics: CPU, memory, disk, network
  • Application metrics: Response times, error rates, throughput
  • Service health: Database, Redis, API, WebSocket status
  • Performance optimization: Caching, query optimization

🔧 Configuration

Dashboard Configuration (config/dashboard/config.yaml)

app:
  name: "QuantStream Analytics Dashboard"
  environment: "development"

server:
  host: "0.0.0.0"
  port: 8501

database:
  url: "postgresql://user:pass@localhost:5432/db"

redis:
  host: "localhost"
  port: 6379

dashboard:
  refresh_rate: 1000  # milliseconds
  auto_refresh: true

charts:
  default_theme: "plotly_white"

Monitoring Configuration (config/monitoring/prometheus.yml)

  • Custom metrics collection
  • Service discovery
  • Alert rules configuration
  • Data retention settings

📈 Performance

Metrics

  • Latency: <300ms API response times
  • Throughput: 1000+ concurrent users
  • Real-time updates: <1 second chart refresh
  • Uptime: 99.9% availability target
  • Cache hit rate: >95% for market data

Optimization Features

  • Redis caching: Market data and user sessions
  • Database indexing: Optimized queries
  • WebSocket streaming: Efficient real-time updates
  • Load balancing: Horizontal scaling support
  • Connection pooling: Database and Redis connections

🔒 Security

Authentication & Authorization

  • JWT tokens: Secure API access
  • Role-based access: Admin, Analyst, Trader, Viewer roles
  • Session management: Redis-based sessions
  • Password security: PBKDF2 hashing with salt

API Security

  • Rate limiting: Configurable request limits
  • CORS protection: Cross-origin request filtering
  • Input validation: Pydantic model validation
  • Error handling: Secure error responses

📊 Monitoring & Observability

Metrics Collection

  • System metrics: CPU, memory, disk, network
  • Application metrics: Request rates, response times, errors
  • Business metrics: Portfolio values, trading volumes
  • Custom metrics: Domain-specific KPIs

Dashboards

  • Grafana dashboards: Pre-configured monitoring views
  • Real-time alerting: Threshold-based notifications
  • Historical analysis: Time-series data visualization
  • Performance trending: Long-term performance analysis

🧪 Testing

Test Structure

tests/
├── dashboard/          # Dashboard tests
├── unit/              # Unit tests
├── integration/       # Integration tests
└── performance/       # Performance tests

Running Tests

# Unit tests
pytest tests/unit/

# Integration tests
pytest tests/integration/

# Performance tests
pytest tests/performance/

# All tests with coverage
pytest --cov=src tests/

🐳 Deployment

Docker Services

  • Application containers: Dashboard, API, WebSocket
  • Infrastructure: PostgreSQL, Redis, Prometheus, Grafana
  • Monitoring: Node Exporter, Redis Exporter, Postgres Exporter

Environment Variables

ENVIRONMENT=production
DATABASE_URL=postgresql://...
REDIS_URL=redis://...
SECRET_KEY=your-secret-key
PROMETHEUS_URL=http://prometheus:9090

Scaling

  • Horizontal scaling: Multiple dashboard instances
  • Load balancing: Nginx reverse proxy
  • Database scaling: Read replicas, connection pooling
  • Cache scaling: Redis cluster support

📝 API Documentation

REST API

  • Interactive docs: http://localhost:8000/docs
  • OpenAPI spec: Comprehensive API documentation
  • Authentication: JWT bearer tokens
  • Rate limiting: Configurable per endpoint

WebSocket API

  • Market data streaming: Real-time price feeds
  • System metrics: Live monitoring data
  • Event notifications: Alert and system events

🤝 Contributing

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

Code Standards

  • Python: PEP 8, type hints, docstrings
  • Testing: >90% coverage requirement
  • Documentation: Comprehensive API docs
  • Security: Security review for all changes

📄 License

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

🆘 Support

  • Documentation: [Link to documentation]
  • Issues: [GitHub Issues]
  • Discussions: [GitHub Discussions]
  • Email: support@quantstream.ai

QuantStream Analytics Dashboard - Real-time financial data visualization and monitoring platform.