Recommended Topology

The modern architecture exposes individual FastAPI services:

Service	Port	Purpose
agent_service	8000	Control plane for workspaces, agents, skills
trading_service	8001	Read-only trading data APIs
news_service	8002	Read-only explain/news APIs
runtime_service	8003	Runtime lifecycle APIs
gateway	8765	WebSocket event channel

When to use: Multi-service deployments, independent scaling needs, service-level monitoring, or when following the architecture documented in docs/current-architecture.md.

To deploy in split-service mode, you would:

Deploy each service with its own systemd unit
Configure nginx to route /api/* to the appropriate service
Keep WebSocket proxy to gateway on port 8765

Set environment variables for service discovery:

TRADING_SERVICE_URL=http://localhost:8001
NEWS_SERVICE_URL=http://localhost:8002
RUNTIME_SERVICE_URL=http://localhost:8003

Important Paths And Ports

frontend root: /var/www/bigtime/current
gateway bind: 127.0.0.1:8765
public WebSocket path: /ws
working directory expected by systemd: /root/code/evotraders

systemd

No maintained systemd unit is checked into the repository anymore. If deployment work resumes, add units that mirror the split-service topology used in local development.

nginx

The HTTPS nginx config does two things:

redirects http://bigtime.cillinn.com to HTTPS
proxies /ws to the local gateway process with WebSocket upgrade headers

Typical install flow:

sudo cp deploy/nginx/bigtime.cillinn.com.conf /etc/nginx/sites-available/bigtime.cillinn.com.conf
sudo ln -s /etc/nginx/sites-available/bigtime.cillinn.com.conf /etc/nginx/sites-enabled/
sudo nginx -t
sudo systemctl reload nginx

The checked-in TLS config expects Let's Encrypt assets at:

/etc/letsencrypt/live/bigtime.cillinn.com/fullchain.pem
/etc/letsencrypt/live/bigtime.cillinn.com/privkey.pem

Environment Expectations

Before using the production scripts, ensure the runtime environment has:

a usable Python environment
backend dependencies installed from the checked-in Python package metadata in pyproject.toml
the package installed with pip install -e . or uv pip install -e .
frontend dependencies installed with npm install
repo dependencies installed
required market/model API keys
any desired TICKERS override

Recommended production install sequence:

python3 -m venv .venv
source .venv/bin/activate
pip install -e .
cd frontend && npm install && npm run build && cd ..

Skill Sandbox Configuration

Production deployments should enable Docker-based skill sandbox for security isolation:

# Install with sandbox support
pip install -e ".[docker-sandbox]"

# Verify Docker daemon is running
docker info

Example environment variables for a future deployment:

Variable	Default	Description
`SKILL_SANDBOX_MODE`	`docker`	Sandbox mode: `none` \| `docker` \| `kubernetes`
`SKILL_SANDBOX_IMAGE`	`python:3.11-slim`	Docker image for sandbox
`SKILL_SANDBOX_MEMORY_LIMIT`	`512m`	Memory limit per skill execution
`SKILL_SANDBOX_CPU_LIMIT`	`1.0`	CPU limit per skill execution
`SKILL_SANDBOX_NETWORK`	`none`	Network mode: `none` \| `bridge`
`SKILL_SANDBOX_TIMEOUT`	`60`	Execution timeout in seconds

Security recommendation: Always use SKILL_SANDBOX_MODE=docker in production. The none mode (direct execution) is for development only and displays a security warning.

What This Deployment Does Not Yet Cover

The checked-in deployment artifacts do not currently document or automate:

split FastAPI service deployment on 8000 to 8003
OpenClaw gateway deployment on 18789
database backup/retention workflows
frontend build/publish steps
secret management

If deployment returns to active development, rewrite this directory around the same split-service topology used by start-dev.sh.

README.md

Deployment Notes