Docker Orchestration

The system is designed to run as a multi-container application using Docker Compose. This ensures that the database, analysis backend, trading agents, and web dashboard are pre-configured to communicate seamlessly in an isolated environment.

Environment Architecture

The orchestration setup manages four primary services:

• Database: A persistent storage layer for backtest results, trade logs, and agent reasoning.
• Analysis Backend: A FastAPI service that processes trading logic, interacts with the vector store (RAG), and serves the REST/WebSocket API.
• Dashboard Frontend: A Vite-powered React application for visualizing performance and real-time trade data.
• LLM Provider: Connection to either local inference (Ollama) or cloud-based providers (Groq/OpenAI).

Prerequisites

Before deploying the stack, ensure you have the following installed:

• Docker Desktop (includes Docker Compose)
• An API Key for your chosen LLM provider (e.g., Groq, OpenAI) if not using local Ollama.

Configuration

The orchestration relies on environment variables defined in a .env file at the project root. Copy the template and fill in your credentials:

# Example .env configuration
GROQ_API_KEY=your_groq_key_here
NEWSAPI_KEY=your_newsapi_key_here
DATABASE_URL=postgresql://user:password@db:5432/trading_db
BACKEND_URL=http://backend:8000

Quick Start

To build and start the entire trading ecosystem, run:

docker-compose up --build

Once the containers are healthy, the following endpoints will be available:

• Dashboard: http://localhost:5173
• API Documentation (Swagger): http://localhost:8000/docs
• Database: Internal port 5432

Service Definitions

1. Analysis Backend (backend)

The core Python service. It handles the TradingOrchestrator logic and manages the RAG (Retrieval-Augmented Generation) pipeline using the local knowledge/ directory.

• Internal Port: 8000
• Volumes: Mounts the ./logs and ./knowledge directories to ensure backtest reports and vector indexes persist across restarts.

2. Dashboard Frontend (frontend)

The React-based user interface. It communicates with the backend via REST for historical data and WebSockets for live trading updates.

• Internal Port: 5173
• Dependencies: Requires the backend service to be healthy.

3. Database (db)

A relational database (PostgreSQL) used to store structured data from the agents/schemas.py models.

• Persistence: Uses a Docker volume (e.g., postgres_data) to prevent data loss when containers are stopped.

Running Specific Workloads

You can use docker-compose run to execute specific tasks without starting the web dashboard:

Run a Backtest

docker-compose run backend python run_backtest.py --days 30 --strategy ensemble

Index Knowledge Base (RAG)

If you have added new research papers or markdown files to the knowledge/ folder, re-index the vector store:

docker-compose run backend python -m agents.rag.indexer

Logs and Monitoring

To monitor the interaction between agents and the supervisor during a live analysis or backtest, follow the backend logs:

docker-compose logs -f backend

This will output the individual agent recommendations (Technical, Sentiment, Fundamental, Risk) and the final decision reached by the orchestrator.

Troubleshooting

| Issue | Solution | | :--- | :--- | | Connection Refused (Backend) | Ensure the DATABASE_URL in .env uses the service name db instead of localhost. | | Frontend shows "Offline" | Check if the WebSocket connection is blocked by a firewall or if the BACKEND_URL is incorrectly configured in the frontend build. | | LLM Timeout | If using local Ollama, ensure the Ollama service is accessible from within the Docker network (usually via host.docker.internal). |