System Architecture

DeepTrade employs a sophisticated architecture designed for scalability, reliability, and extensibility. This section provides an overview of the system's components and how they interact.

High-Level Architecture

DeepTrade is built as a modern web application with a clear separation of concerns:

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│                 │     │                 │     │                 │
│  Web Interface  │◄────┤  Core Analysis  │◄────┤   Data Sources  │
│                 │     │     Engine      │     │                 │
└─────────────────┘     └─────────────────┘     └─────────────────┘

Component Breakdown

1. Web Interface

The user-facing layer of DeepTrade is built with:

• Next.js: For server-side rendering and routing
• React: For component-based UI development
• TypeScript: For type safety and improved developer experience
• Tailwind CSS: For responsive and customizable styling

Key components include:

• Stock selection interface
• Analysis progress tracker
• Results visualization dashboard
• Agent-specific analysis views

2. Core Analysis Engine

The heart of DeepTrade is its analysis engine:

• Multi-Agent System: Coordinated AI agents with specialized roles
• Prompt Engineering: Carefully crafted prompts to guide AI analysis
• Concurrent Processing: Parallel execution of agent analysis
• Result Aggregation: Intelligent combination of diverse perspectives

3. Data Sources

DeepTrade integrates with various data sources:

• Financial APIs: Real-time and historical stock data
• Company Filings: SEC filings and financial statements
• News and Sentiment: Market news and sentiment analysis
• Economic Indicators: Broader economic context data

Agent Architecture

The multi-agent system follows a hierarchical structure:

                    ┌─────────────────┐
                    │                 │
                    │ Portfolio Manager│
                    │                 │
                    └─────────────────┘
                            ▲
                            │
                            │
        ┌──────────────────┼──────────────────┐
        │                  │                  │
┌───────┴───────┐  ┌───────┴───────┐  ┌───────┴───────┐
│               │  │               │  │               │
│Value Investors│  │Growth Investors│  │Risk Manager   │
│               │  │               │  │               │
└───────────────┘  └───────────────┘  └───────────────┘
        ▲                  ▲                  ▲
        │                  │                  │
┌───────┴───────┐  ┌───────┴───────┐  ┌───────┴───────┐
│               │  │               │  │               │
│  Technical    │  │  Fundamental  │  │   Market      │
│   Analyst     │  │    Analyst    │  │   Context     │
│               │  │               │  │               │
└───────────────┘  └───────────────┘  └───────────────┘

Data Flow

The system processes data through the following flow:

1. Data Collection: Gathering relevant financial and market data
2. Agent Analysis: Each agent processes the data according to its specialized approach
3. Insight Aggregation: Combining diverse perspectives into a coherent view
4. Recommendation Generation: Producing actionable investment recommendations
5. Explanation Formulation: Creating clear explanations for all recommendations

Technical Implementation

DeepTrade is implemented as a monorepo using:

• Turborepo: For managing the monorepo structure
• pnpm: For efficient package management
• Node.js: For server-side JavaScript execution
• OpenAI API: For powering the AI agents
• Financial Data APIs: For real-time market data

Extensibility

The architecture is designed for extensibility:

• Pluggable Agents: New analytical perspectives can be added
• Customizable Workflows: Analysis processes can be tailored
• API-First Design: Components communicate through well-defined interfaces

This architecture enables DeepTrade to provide sophisticated investment analysis while remaining flexible and maintainable.