AI Trading Bot: Architecture, Tech Stack, and Component Interaction

[gotoenchanter]

I want to start a discussion about building an AI-powered trading bot and explore the relationship between its main components, including the AI engine, trading data, frontend (Next.js), and backend services.

The goal is to discuss which tech stack choices are optimal for performance, scalability, and maintainability, as well as how these components interact in a real-time trading environment.

[gotoenchanter]

How should an AI trading bot be architected to optimize performance and scalability, considering the interaction between:

• AI engine (model inference, strategy computation)
• Trading data (market data, historical data, order books)
• Frontend (Next.js, dashboards, real-time visualization)
• Backend/API layer (data processing, trading execution, caching)

Specifically, what tech stacks are recommended for each component, and how should data and requests flow between them to minimize latency and maintain reliability?

[codingwizard0831]

1. AI Engine

• Purpose: Analyze market data and generate trading signals.

• Tech Stack Recommendations:

  • Python for AI/ML models (TensorFlow, PyTorch, scikit-learn).
  • GPU or CPU clusters depending on computation needs.
  • Optional: Rust or C++ for latency-critical signal processing.

• Interaction: Receives processed market data from the backend or streaming services, outputs trading signals to the backend or directly to the execution engine.

2. Trading Data

• Purpose: Provide historical and real-time market data.

• Tech Stack Recommendations:

  • Streaming: WebSockets or Kafka for real-time updates.
  • Storage: TimescaleDB, PostgreSQL, or InfluxDB for historical data.
  • Caching: Redis for frequently accessed data to reduce latency.

• Interaction: Data flows to the AI engine for analysis and to the frontend for visualization.

3. Backend/API Layer

• Purpose: Central hub connecting frontend, AI engine, and trading execution.

• Tech Stack Recommendations:

  • Node.js or FastAPI for lightweight API handling.
  • WebSocket endpoints for real-time updates to frontend.
  • REST endpoints for non-real-time data or historical queries.
  • Microservices architecture for scaling AI computation and trade execution independently.

• Interaction: Receives signals from AI engine, executes trades, stores logs, and streams updates to the frontend.

4. Frontend (Next.js)

• Purpose: Display dashboards, charts, trading performance, and real-time market info.

• Tech Stack Recommendations:

  • Next.js with React for SSR/CSR hybrid, optimized SEO, and fast UI.
  • Charting libraries: Recharts, ApexCharts, or TradingView widgets.
  • WebSockets or server-sent events for real-time data streaming.

• Interaction: Subscribes to backend streams for live trading updates, displays AI signals, and provides user interactions (manual overrides, parameter adjustments).

---

Example Data Flow

1. Market Data → Backend: Real-time price feeds arrive via WebSocket/Kafka.
2. Backend → AI Engine: Data is normalized and batched, then sent to the AI engine for inference.
3. AI Engine → Backend: AI generates signals (buy/sell/hold) with confidence scores.
4. Backend → Execution/Database: Signals trigger trades and are logged.
5. Backend → Frontend: Updates (market, signals, trades) are streamed to the dashboard in real-time.

[Market Data] → [Backend/API] → [AI Engine] → [Backend/API] → [Execution]
                                  ↓
                               [Database]
                                  ↓
                               [Frontend Dashboard]

---

Recommendations

• Use a microservices architecture to decouple AI processing from the trading API and frontend.
• Prioritize low-latency channels (WebSockets, in-memory caching) for real-time trading.
• Separate historical analysis from live trading to avoid blocking critical paths.
• Consider GPU acceleration if AI inference is heavy and needs to run in real-time.
• Monitoring & Logging: Centralized logging (ELK stack or Prometheus/Grafana) is critical for debugging and risk management.