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Manual Installation (Bare-metal)

If you prefer to deploy AITradingSystem manually without using the interactive install.sh script, follow this step-by-step bare-metal installation guide.

Prerequisites​

Before starting, ensure your system has the following tools installed and added to your PATH:

  • Python ≥ 3.10 (available as python3)
  • uv (high-performance Python package installer)
  • Node.js (LTS version recommended)
  • pnpm (Node package manager)
  • PostgreSQL (a running database instance is required)

Step 1 — Create Project Directory​

Create a unified directory to store both the frontend and backend services:

mkdir AITradingSystem
cd AITradingSystem

Step 2 — Clone the Repositories​

Clone the frontend and backend repositories into your project folder:

# Clone Backend Repository
git clone https://github.com/QuantaGenesis/AITradingSystem-backend.git

# Clone Frontend Repository
git clone https://github.com/QuantaGenesis/AITradingSystem-frontend.git

Your folder structure should look like this:

AITradingSystem/
├── AITradingSystem-backend/
└── AITradingSystem-frontend/

Step 3 — Configure Environment Variables​

Each repository requires its own .env file for configuration.

1. Configure the Backend​

Navigate to the backend directory, copy the template, and edit it:

cd AITradingSystem-backend
cp .env.example .env

Open .env and fill in the required fields:

  • DATABASE_URL: Set your PostgreSQL connection string (e.g. postgresql+psycopg://postgres:password@localhost:5432/tradingos).
  • AUTH_SECRET: Set a unique 32-character security key. Generate one with: 
    python3 -c "import secrets; print(secrets.token_hex(32))"
  • ADMIN_PASSWORD: Choose a strong password for your admin account.
  • Update other optional variables like CORS_ORIGINS, sync intervals, or LLM models as needed.

2. Configure the Frontend​

Navigate to the frontend directory and create a .env file:

cd ../AITradingSystem-frontend
touch .env

Open the frontend .env file and add the URL of your backend API:

VITE_API_BASE_URL=http://127.0.0.1:8000

Step 4 — Install and Start the Backend​

Navigate back to the backend directory, sync dependencies, run Alembic migrations to set up the database tables, and run the service:

cd ../AITradingSystem-backend

# Sync dependencies using uv
uv sync

# Run database migrations
uv run alembic upgrade head

# Start the FastAPI server
uv run app/main.py --host 127.0.0.1 --port 8000

The backend API is now running at http://127.0.0.1:8000. Keep this terminal open or run it using a process manager like pm2 or nohup.

Step 5 — Install and Start the Frontend​

In a new terminal window, navigate to the frontend directory, install dependencies, compile the production build, and serve the application:

cd AITradingSystem/AITradingSystem-frontend

# Install dependencies
pnpm install

# Build for production
pnpm run build

# Start the local preview server on port 3333
pnpm run preview --host 127.0.0.1 --port 3333

The frontend dashboard is now running and accessible at http://127.0.0.1:3333.