Roneira AI HIFI — High-Impact Finance Intelligence Platform

A production-grade financial analytics framework engineered for robust machine learning forecasting, advanced price-volume momentum analytics, and comprehensive portfolio intelligence in institutional or retail trading environments.

Live Demo • Documentation • API Reference • Contributing

Table of Contents

Click to expand navigation

• Overview
• Key Features
• Architecture & Technology Stack
• UI Components & Features
• Getting Started
• Configuration
• API Documentation
• Performance & Optimizations
• Security Implementation
• Testing Strategy
• Deployment
• Contributing
• Monitoring & Observability
• Roadmap
• Support & Community
• License

Overview

Roneira AI HIFI represents the convergence of advanced machine learning, real-time financial data processing, and institutional-grade analytics in a comprehensive financial intelligence platform. Built with modern microservices architecture, the platform delivers:

• Precision ML Forecasting: RandomForest-based regression models with engineered technical features
• Real-time Analytics: Live market data processing with sub-second latency
• Portfolio Intelligence: Advanced risk assessment and correlation analysis
• Scalable Infrastructure: Container-native architecture for seamless scaling

Core Objectives

Reliability Reproducible ML predictions Deterministic model training Comprehensive error handling

Scalability Microservices architecture Container orchestration Auto-scaling capabilities

Performance Sub-second API responses Optimized data pipelines Intelligent caching layers

Key Features

Machine Learning & Analytics

Advanced ML Predictions

• Multi-Model Ensemble: RandomForest, XGBoost, and LSTM models for different prediction horizons
• Feature Engineering: 50+ technical indicators with vectorized computation
• Model Versioning: MLOps pipeline with A/B testing capabilities
• Backtesting Framework: Historical performance validation with walk-forward analysis
• Confidence Intervals: Probabilistic predictions with uncertainty quantification

PDM Strategy Analytics

• Price Derivatives: Velocity (df/dt) and acceleration (d²f/dt²) calculations
• Volume Analysis: Volume-weighted price movements and momentum detection
• Signal Generation: Multi-timeframe confluence analysis
• Risk Management: ATR-based position sizing and stop-loss automation
• Performance Metrics: Sharpe ratio, maximum drawdown, and win-rate analytics

Technical Analysis Suite

• Core Indicators: SMA, EMA, RSI, MACD, Bollinger Bands, Stochastic
• Advanced Patterns: Candlestick recognition, support/resistance levels
• Custom Indicators: Proprietary momentum and volatility measures
• Multi-Timeframe: Synchronized analysis across different time horizons
• Alert System: Real-time notifications for signal triggers

Portfolio Management

Intelligent Portfolio Analytics

• Real-time Valuation: Live portfolio tracking with P&L calculations
• Risk Assessment: VaR calculations, correlation matrices, beta analysis
• Performance Attribution: Sector, geographic, and style factor analysis
• Rebalancing Algorithms: Automated portfolio optimization
• Tax Optimization: Harvest loss tracking and wash sale rule compliance

Architecture & Technology Stack

graph TB
    subgraph "Frontend Layer"
        UI[React 18 + TypeScript]
        PWA[Progressive Web App]
        Cache[TanStack Query Cache]
    end
    
    subgraph "API Gateway"
        API[Express.js + TypeScript]
        Auth[JWT Authentication]
        Rate[Rate Limiting]
        Validate[Zod Validation]
    end
    
    subgraph "ML Services"
        ML[Flask ML Service]
        Models[ML Models]
        Features[Feature Engineering]
        Cache2[Model Cache]
    end
    
    subgraph "Data Layer"
        DB[(PostgreSQL)]
        Redis[(Redis Cache)]
        Market[Market Data APIs]
    end
    
    subgraph "Infrastructure"
        Docker[Docker Containers]
        K8s[Kubernetes]
        Monitor[Monitoring Stack]
    end
    
    UI --> API
    API --> ML
    API --> DB
    API --> Redis
    ML --> Cache2
    ML --> Market
    
    Docker --> K8s
    K8s --> Monitor

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Core Services Matrix

Service	Technology	Purpose	Scalability
Frontend	React 18 + Vite + TypeScript	Modern UI with SSR capabilities	CDN + Edge caching
API Gateway	Node.js + Express + TypeScript	RESTful API with GraphQL support	Horizontal pod autoscaling
ML Service	Python + Flask + scikit-learn	ML inference and model training	GPU-accelerated containers
Database	PostgreSQL 15 + pgvector	OLTP with vector similarity search	Read replicas + sharding
Cache	Redis 7 + Redis Streams	Real-time caching and pub/sub	Redis Cluster mode
Message Queue	RabbitMQ + Celery	Async task processing	Queue federation

UI Components & Features

Dynamic Interface Components

Core UI Components Navigation System Responsive sidebar with collapsible menu Breadcrumb navigation with dynamic routing Global search with autocomplete User profile dropdown with settings Dashboard Components Real-time metric cards with animations Interactive charts with zooming/panning Customizable widget layouts Dark/light theme toggle Data Visualization Candlestick charts with volume overlays Technical indicator overlays Portfolio allocation pie charts Performance comparison line charts

Interactive Features Prediction Interface Multi-ticker symbol search with fuzzy matching Prediction horizon slider (1-30 days) Confidence interval visualization Historical accuracy metrics Portfolio Management Drag-and-drop position management Real-time P&L calculations Risk metrics dashboard Alert configuration panels Analysis Tools Interactive backtesting interface Strategy parameter optimization Performance attribution breakdowns Correlation heatmaps

Component Architecture

// Component Hierarchy Example
src/components/
├── ui/                     # Reusable UI primitives
│   ├── Button/
│   ├── Input/
│   ├── Modal/
│   └── Chart/
├── navigation/             # Navigation components
│   ├── Sidebar/
│   ├── Header/
│   └── Breadcrumb/
├── prediction/             # ML prediction features
│   ├── PredictionPanel/
│   ├── ModelMetrics/
│   └── ConfidenceChart/
├── portfolio/              # Portfolio management
│   ├── PositionTable/
│   ├── AllocationChart/
│   └── RiskMetrics/
├── analysis/               # Technical analysis
│   ├── TechnicalChart/
│   ├── IndicatorPanel/
│   └── PatternScanner/
└── pdm/                   # PDM strategy tools
    ├── PDMScanner/
    ├── SignalChart/
    └── BacktestResults/

Advanced UI Features

Progressive Web App (PWA)

• Offline functionality with service workers
• Push notifications for alerts
• App-like experience on mobile devices
• Background sync for data updates

Real-time Updates

• WebSocket connections for live data
• Optimistic UI updates
• Conflict resolution for concurrent edits
• Automatic reconnection handling

Accessibility & Performance

• WCAG 2.1 AA compliance
• Keyboard navigation support
• Screen reader compatibility
• Lazy loading with intersection observers
• Code splitting for optimal bundle sizes

Getting Started

Prerequisites Checklist

• Node.js >= 18.0.0 (Download)
• Python >= 3.11 (Download)
• Docker & Docker Compose (Download)
• Git for version control
• PostgreSQL 15+ (optional for local development)
• Redis 7+ (optional for local development)

Quick Start (5 minutes)

1. Clone and Setup

# Clone the repository
git clone https://github.com/aaron-seq/Roneira-AI-HIFI.git
cd Roneira-AI-HIFI

# Copy environment templates
cp .env.example .env
cp frontend/.env.example frontend/.env.local
cp backend/.env.example backend/.env
cp ml-service/.env.example ml-service/.env

# Make setup script executable and run
chmod +x scripts/setup.sh
./scripts/setup.sh

2. Docker Development (Recommended)

# Start all services with hot reloading
docker-compose up --build

# Or run in detached mode
docker-compose up -d --build

# View logs
docker-compose logs -f [service-name]

# Stop all services
docker-compose down

Service URLs:

• Frontend: http://localhost:3000
• Backend API: http://localhost:3001
• ML Service: http://localhost:5000
• Swagger Docs: http://localhost:3001/api/docs

3. Local Development Setup

Terminal 1 - Frontend:

cd frontend
npm ci
npm run dev

Terminal 2 - Backend:

cd backend
npm ci
npm run dev

Terminal 3 - ML Service:

cd ml-service
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt
gunicorn --bind 0.0.0.0:5000 enhanced_app:app --reload

Development Workflow

gitGraph
    commit id: "Initial setup"
    branch feature/new-indicator
    checkout feature/new-indicator
    commit id: "Add RSI calculation"
    commit id: "Add tests"
    commit id: "Update docs"
    checkout main
    merge feature/new-indicator
    commit id: "Release v1.1.0"

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Configuration

Environment Variables Reference

Frontend Configuration

Variable	Default	Description
VITE_API_BASE_URL	http://localhost:3001	Backend API endpoint
VITE_WS_URL	ws://localhost:3001	WebSocket server URL
VITE_APP_NAME	Roneira AI HIFI	Application display name
VITE_APP_VERSION	3.0.0	Version identifier
VITE_SENTRY_DSN	-	Error tracking DSN
VITE_ANALYTICS_ID	-	Google Analytics ID

Backend Configuration

Variable	Default	Description
PORT	3001	Server listening port
NODE_ENV	development	Runtime environment
DATABASE_URL	-	PostgreSQL connection string
REDIS_URL	-	Redis connection string
JWT_SECRET	-	JWT signing secret
ML_SERVICE_URL	http://localhost:5000	ML service endpoint
RATE_LIMIT_WINDOW	900000	Rate limit window (15min)
RATE_LIMIT_MAX	100	Max requests per window

ML Service Configuration

Variable	Default	Description
FLASK_ENV	development	Flask environment
MODEL_CACHE_TTL	3600	Model cache TTL (seconds)
FEATURE_CACHE_SIZE	1000	Feature cache size
HUGGING_FACE_API_KEY	-	HF API key for sentiment
ALPHA_VANTAGE_API_KEY	-	Market data API key
GUNICORN_WORKERS	4	Production worker count

API Documentation

RESTful API Endpoints

Prediction Endpoints

POST /api/v1/predict

Single ticker price prediction with ML models.

Request Body:

{
  "ticker": "AAPL",
  "days": 5,
  "models": ["randomforest", "xgboost"],
  "include_pdm": true,
  "confidence_level": 0.95
}

Response:

{
  "predictions": [
    {
      "date": "2024-01-15",
      "price": 185.42,
      "confidence_interval": [180.15, 190.69],
      "probability": 0.78
    }
  ],
  "model_metrics": {
    "accuracy": 0.82,
    "mae": 2.34,
    "rmse": 3.67
  },
  "pdm_signals": {
    "momentum": "bullish",
    "strength": 0.65
  }
}

POST /api/v1/batch-predict

Batch prediction for multiple tickers (max 10).

Rate Limits: 30 requests/minute per API key

Portfolio Endpoints

GET /api/v1/portfolio/:userId

Retrieve user portfolio with real-time valuations.

POST /api/v1/portfolio/:userId/positions

Add or update portfolio positions.

GET /api/v1/portfolio/:userId/analytics

Portfolio performance analytics and risk metrics.

PDM Strategy Endpoints

GET /api/v1/pdm/scan

Scan markets for PDM opportunities.

POST /api/v1/pdm/backtest

Run historical PDM strategy backtests.

GraphQL Schema (Beta)

type Query {
  predictions(
    tickers: [String!]!
    days: Int = 1
    models: [ModelType!]
  ): [Prediction!]!
  
  portfolio(userId: ID!): Portfolio
  
  marketData(
    ticker: String!
    range: TimeRange!
  ): [OHLCV!]!
}

type Prediction {
  ticker: String!
  predictions: [PricePoint!]!
  confidence: Float!
  modelMetrics: ModelMetrics!
}

Performance & Optimizations

Frontend Optimizations

Bundle Optimization Tree shaking with Vite Dynamic imports for routes Code splitting by features Asset compression (Brotli/Gzip) Runtime Performance React.memo for expensive components useMemo/useCallback for computations Virtual scrolling for large lists Intersection observer for lazy loading

Caching Strategy TanStack Query with stale-while-revalidate Service worker for offline assets CDN edge caching for static resources Browser cache optimization Network Optimization Request deduplication Batch API calls where possible WebSocket for real-time data HTTP/2 server push

Backend Optimizations

// Connection Pooling Example
const pool = new Pool({
  host: process.env.DB_HOST,
  port: 5432,
  database: process.env.DB_NAME,
  user: process.env.DB_USER,
  password: process.env.DB_PASSWORD,
  max: 20,                    // Maximum pool size
  min: 5,                     // Minimum pool size
  idleTimeoutMillis: 30000,   // Close idle connections after 30s
  connectionTimeoutMillis: 2000, // Timeout connection attempts after 2s
});

// Redis Caching Strategy
const cacheStrategy = {
  market_data: { ttl: 60 },     // 1 minute for market data
  predictions: { ttl: 3600 },   // 1 hour for ML predictions
  portfolio: { ttl: 300 },      // 5 minutes for portfolio data
};

ML Service Optimizations

• Vectorized Operations: NumPy/Pandas for batch processing
• Model Caching: LRU cache with intelligent eviction
• Feature Pipelines: Efficient data transformation chains
• GPU Acceleration: CUDA support for training workloads

Security Implementation

Multi-Layer Security Architecture

graph LR
    subgraph "Edge Layer"
        CDN[CDN/WAF]
        TLS[TLS 1.3]
    end
    
    subgraph "Application Layer"
        CORS[CORS Policy]
        CSP[Content Security Policy]
        JWT[JWT Authentication]
        RBAC[Role-Based Access]
    end
    
    subgraph "Data Layer"
        Encrypt[Data Encryption]
        Audit[Audit Logging]
        Backup[Encrypted Backups]
    end
    
    CDN --> CORS
    CORS --> JWT
    JWT --> Encrypt

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Security Features

Authentication & Authorization

• JWT Tokens: Stateless authentication with refresh token rotation
• OAuth 2.0: Social login integration (Google, GitHub)
• Multi-Factor Authentication: TOTP and SMS-based 2FA
• Role-Based Access Control: Granular permissions system
• Session Management: Secure session handling with Redis

Data Protection

• Encryption at Rest: AES-256 database encryption
• Encryption in Transit: TLS 1.3 for all communications
• API Security: Rate limiting, request validation, CORS policies
• Input Sanitization: XSS prevention and SQL injection protection
• Audit Logging: Comprehensive security event logging

Infrastructure Security

• Container Security: Non-root users, minimal base images
• Network Segmentation: Private subnets and security groups
• Secrets Management: HashiCorp Vault integration
• Vulnerability Scanning: Automated dependency and container scanning
• Security Headers: HSTS, CSP, X-Frame-Options, etc.

Testing Strategy

Comprehensive Test Coverage

Unit Tests Frontend: Vitest + React Testing Library Backend: Jest + Supertest ML Service: pytest + unittest Target: >90% code coverage

Integration Tests API endpoint testing Database integration tests ML model validation WebSocket connection tests Target: >80% integration coverage

E2E Tests Playwright for cross-browser testing User workflow automation Performance regression testing Visual regression testing Target: Critical user paths covered

Test Execution

# Run all tests with coverage
npm run test:all

# Frontend tests with UI
cd frontend && npm run test:ui

# Backend integration tests
cd backend && npm run test:integration

# ML service performance tests
cd ml-service && pytest --benchmark

# E2E tests across browsers
npm run test:e2e

# Load testing
npm run test:load

CI/CD Pipeline

# GitHub Actions Workflow Example
name: CI/CD Pipeline
on: [push, pull_request]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Setup Node.js
        uses: actions/setup-node@v4
        with: { node-version: '18' }
      
      - name: Install dependencies
        run: npm ci
      
      - name: Run linting
        run: npm run lint
      
      - name: Run type checking
        run: npm run type-check
      
      - name: Run unit tests
        run: npm run test:coverage
      
      - name: Run E2E tests
        run: npm run test:e2e
        
      - name: Upload coverage
        uses: codecov/codecov-action@v3

Deployment

Deployment Strategies

Development Deployment

Docker Compose (Local)

# Start all services
docker-compose up --build

# Scale specific services
docker-compose up --scale ml-service=3

# View service logs
docker-compose logs -f backend

Environment-specific configs

• Development: Hot reloading, debug logs, test databases
• Staging: Production-like with synthetic data
• Production: Optimized builds, monitoring, real data

Cloud Deployment

Free Tier Platforms

• Frontend: Vercel, Netlify, GitHub Pages
• Backend: Railway, Render, Fly.io
• Database: Supabase, PlanetScale, Neon
• Cache: Upstash Redis, Redis Cloud

Production Platforms

• Kubernetes: AWS EKS, Google GKE, Azure AKS
• Serverless: AWS Lambda, Google Cloud Functions
• Platform-as-a-Service: Heroku, Railway (paid tiers)

Infrastructure as Code

Terraform Configuration

# AWS EKS Cluster Example
resource "aws_eks_cluster" "roneira_cluster" {
  name     = "roneira-ai-hifi"
  role_arn = aws_iam_role.cluster_role.arn
  version  = "1.28"

  vpc_config {
    subnet_ids = [
      aws_subnet.private_1.id,
      aws_subnet.private_2.id
    ]
    endpoint_private_access = true
    endpoint_public_access  = true
  }
}

Kubernetes Manifests

# ML Service Deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: ml-service
spec:
  replicas: 3
  selector:
    matchLabels:
      app: ml-service
  template:
    metadata:
      labels:
        app: ml-service
    spec:
      containers:
      - name: ml-service
        image: roneira/ml-service:latest
        resources:
          requests:
            memory: "512Mi"
            cpu: "250m"
          limits:
            memory: "1Gi"
            cpu: "500m"

Deployment Checklist

• Environment Variables: All secrets configured
• Database Migrations: Schema updates applied
• SSL Certificates: TLS configured and validated
• Monitoring: Observability stack deployed
• Backup Strategy: Data backup procedures in place
• Load Testing: Performance validated under load
• Security Scan: Vulnerability assessment completed
• Rollback Plan: Deployment rollback procedure tested

Contributing

We welcome contributions from the community! Please see our Contributing Guide for detailed information on:

• Development workflow and branching strategy
• Code style guidelines and linting rules
• Testing requirements and coverage goals
• Pull request process and review guidelines
• Commit message conventions

Quick Contribution Steps

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

Development Tools

# Setup development environment
npm run dev:setup

# Run linting and formatting
npm run lint:fix
npm run format

# Pre-commit hooks
npm run pre-commit

# Generate documentation
npm run docs:generate

Monitoring & Observability

Monitoring Stack

Application Monitoring Prometheus: Metrics collection and alerting Grafana: Visualization and dashboards Jaeger: Distributed tracing Sentry: Error tracking and performance monitoring Infrastructure Monitoring Node Exporter: System metrics cAdvisor: Container metrics Alertmanager: Alert routing and notification Uptime monitoring: Service availability checks

Key Metrics Dashboard const keyMetrics = { api: { response_time: 'p99 < 500ms', error_rate: '< 0.1%', throughput: '1000 rps', availability: '99.9%' }, ml: { prediction_latency: 'p95 < 2s', model_accuracy: '> 80%', cache_hit_rate: '> 90%', training_time: '< 10min' }, database: { connection_pool: '< 80% utilized', query_time: 'p95 < 100ms', replication_lag: '< 1s' } };

Alert Configuration

# Prometheus Alert Rules
groups:
- name: roneira-alerts
  rules:
  - alert: HighAPILatency
    expr: histogram_quantile(0.99, rate(http_request_duration_seconds_bucket[5m])) > 0.5
    for: 2m
    labels:
      severity: warning
    annotations:
      summary: "High API latency detected"
      
  - alert: MLServiceDown
    expr: up{job="ml-service"} == 0
    for: 1m
    labels:
      severity: critical
    annotations:
      summary: "ML service is down"

Roadmap

Short-term Goals (Q1 2024)

• Enhanced Authentication: OAuth2 provider integration
• Real-time Notifications: WebSocket-based alert system
• Mobile Optimization: Progressive Web App improvements
• API v2: GraphQL endpoint with subscriptions
• Advanced Charting: Technical analysis drawing tools

Medium-term Goals (Q2-Q3 2024)

• Multi-asset Support: Cryptocurrency and forex integration
• Social Trading: Copy trading and signal sharing
• Advanced ML Models: Transformer-based price prediction
• Risk Management: Advanced portfolio optimization
• White-label Solution: Customizable branding options

Long-term Vision (Q4 2024+)

• Institutional Features: Prime brokerage integration
• Regulatory Compliance: MiFID II and SEC reporting
• AI Assistant: Natural language query interface
• Blockchain Integration: DeFi protocol connectivity
• Global Expansion: Multi-currency and localization

Community Requests

Based on community feedback, we're prioritizing:

1. Dark mode improvements (In Progress)
2. Mobile app development (Planning)
3. Integration with TradingView (Research)
4. Options trading support (Research)

Support & Community

Getting Help

Documentation & Resources 📚 Full Documentation 🔗 API Reference 📺 Video Tutorials 📰 Technical Blog

Community & Support 💬 Discord Community 🐛 Issue Tracker 💡 Feature Requests 📧 Enterprise Support

FAQ

How accurate are the ML predictions?

Our models achieve 80-85% directional accuracy on 1-day predictions and 70-75% on 5-day predictions. Accuracy varies by market conditions and asset volatility. Always combine predictions with fundamental analysis and risk management.

What data sources are used?

We integrate with multiple data providers including Alpha Vantage, Yahoo Finance, and Quandl for market data. News sentiment is sourced from various financial news APIs. All data is validated and normalized before processing.

Is the platform suitable for institutional use?

Yes, the platform is designed with institutional-grade features including API rate limiting, audit logging, compliance reporting, and enterprise authentication options. Contact us for custom institutional solutions.

How do I contribute new ML models?

Follow our ML Model Contribution Guide. We welcome contributions of new algorithms, especially in the areas of sentiment analysis, alternative data integration, and risk modeling.

Performance Benchmarks

Environment: AWS c5.4xlarge, PostgreSQL RDS, Redis ElastiCache

API Response Times:
├── Single prediction: 150ms (p99)
├── Batch prediction: 800ms (p99)
├── Portfolio analysis: 300ms (p99)
└── Market data: 50ms (p99)

ML Model Performance:
├── Feature engineering: 2.1s (10 tickers)
├── Prediction generation: 450ms (single ticker)
├── Model training: 8.5min (RandomForest)
└── Cache hit rate: 94.2%

Database Performance:
├── Connection pool utilization: 68%
├── Query response time: 45ms (p95)
├── Concurrent connections: 150
└── Replication lag: 0.3s

License

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

Third-party Licenses

This project uses several open-source libraries. Key dependencies include:

• React: MIT License
• Node.js: MIT License
• Python/Flask: BSD License
• PostgreSQL: PostgreSQL License
• Redis: BSD License
• TensorFlow: Apache 2.0 License

For a complete list of dependencies and their licenses, see LICENSES.md.

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Built with precision engineering for institutional-grade financial intelligence

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