Phase 1: Planning & Design

1. Project Scope & Requirements

  • Objective: Build an MVP for Finance One Stop App focusing on real-time financial tracking and AI-driven insights.
  • Core Features:
    • Real-time stock and crypto data streaming.
    • AI-driven analytics and predictions.
    • Secure user authentication and management.
      • Modular microservices architecture.
  • Constraints:
    • Maintain simplicity for a solo developer.
    • Ensure scalable architecture without excessive complexity.
    • Use Docker for consistent development and deployment.

2. High-Level Architecture Overview

A Microservices-Based approach with the following key components:

  1. Frontend (React + TypeScript / Next.js)

    • Communicates with API Gateway.
    • Handles UI rendering and real-time updates via WebSockets.

  2. Backend (Node.js + Express/NestJS or Python + FastAPI)

    • Serves API requests.
    • Authenticates users.
    • Interfaces with microservices.

  3. Microservices:

    • AI Analytics Service (Python + TensorFlow/PyTorch) – Runs predictive models.
    • Data Aggregator Service (Node.js/Python) – Streams real-time financial data.
    • User Management Service (Node.js/Express) – Handles authentication & authorization.

  4. Databases:

    • PostgreSQL – Main database for transactional data.
    • Redis – Caching layer for high-performance queries.
    • Optional: InfluxDB – Time-series data storage for real-time metrics.

  5. Message Broker (RabbitMQ/Kafka)

    • Handles real-time updates and inter-service communication.

  6. DevOps & Infrastructure:

    • Docker for containerization.
    • Docker Compose for local development.
    • Optional: Kubernetes for production orchestration.
    • CI/CD with GitHub Actions/GitLab CI.

3. Folder Structure for Docker Containers

tom-app/
│── frontend/                  # React + TypeScript (Next.js)
│   ├── src/
│   ├── public/
│   ├── Dockerfile
│── backend/                   # API Gateway + Business Logic
│   ├── src/
│   ├── Dockerfile
│── services/
│   │── ai-service/            # AI Model Microservice
│   │   ├── model/
│   │   ├── src/
│   │   ├── Dockerfile
│   │── data-aggregator/       # Financial Data Service
│   │   ├── src/
│   │   ├── Dockerfile
│   │── user-service/          # Authentication Service
│   │   ├── src/
│   │   ├── Dockerfile
│── db/                        # Database Services
│   │── postgres/
│   │   ├── Dockerfile
│   │── redis/
│   │   ├── Dockerfile
│── message-broker/            # RabbitMQ/Kafka
│   ├── Dockerfile
│── monitoring/                # Prometheus, Grafana
│   ├── Dockerfile
│── docker-compose.yml         # Local Development Orchestration
│── .env                       # Environment Variables
│── README.md                  # Project Documentation

4. Next Steps

  1. Define Detailed User Stories & Wireframes

    • Outline key workflows (user onboarding, data tracking, AI insights, etc.).
    • Sketch wireframes for the main UI components.

  2. Create Architecture Diagrams

    • High-level microservices interaction.
    • API request flows.
    • Data flow between services.

  3. Set Up Development Environment

    • Initialize Git repository.
    • Set up Docker containers and Compose for local development.
    • Configure environment variables.

  4. Plan Initial API Contracts

    • Define RESTful API endpoints.
    • Document request/response formats.

  5. Schedule Regular Reviews & Adjustments

    • Weekly progress tracking.
    • Iterative refinement of architecture and scope.

This plan ensures a clear structure, maintains scalability, and avoids overcomplexity, making it manageable for a solo developer. Let’s begin with defining the user stories and UI wireframes!