CASE STUDY

Trading Assistant Core

An OpenClaw skill that agents can install for signals, sizing, and risk checks.

Trading Assistant Core

01. Project Overview

Installable OpenClaw / ClawHub trading skill that combines support/resistance analysis, multi-indicator signals, position sizing, and risk alerts — with Docker and CLI paths for non-agent users.

02. Problem

Why build this?

Trading workflows were stuck in notebooks and chat prompts — agents could not install a durable skill with clear risk guardrails.

03. Solution

How was it solved?

Packaged support/resistance, multi-indicator signals, position sizing, and risk alerts as a ClawHub skill with Docker and pip / CLI entrypoints.

04. Architecture

System architecture

Decision loop

  1. Market inputs
  2. Indicator / S-R engine
  3. Signal synthesis
  4. Position sizing
  5. Risk alerts
  6. Agent / CLI output

Install surfaces

  1. ClawHub skill
  2. Docker image
  3. pip / ta CLI

05. Key Features

Core capabilities

  • Support and resistance analysis
  • Multi-indicator trading signals
  • Position sizing helpers
  • Risk-first alerts and guardrails
  • ClawHub / OpenClaw skill install
  • Docker and CLI for human-facing runs

06. My Role

What did I specifically do?

  • Defined the skill contract: signals without risk controls are not shippable.
  • Implemented indicator / S-R logic and packaging for ClawHub, Docker, and CLI.
  • Published and maintained the installable skill with live download metrics.

07. Tech Stack

What technologies power it?

  • Python
  • OpenClaw
  • ClawHub
  • Docker
  • pip / CLI
  • Technical indicators

08. Metrics

What results can we show?

959ClawHub installs
Skill · Docker · CLISurfaces

09. Deployment

How do you run it?

Install as an agent skill or run locally without an agent runtime.

  1. ClawHub: install Trading Assistant Core for OpenClaw agents.
  2. Docker: pull and run the published image for containerized use.
  3. CLI: install via pip and use the ta entrypoint for local workflows.

12. Tradeoffs

  • Risk sizing and alerts beat signal fireworks — a skill that only shouts entries is unsafe to install.
  • Kept CLI / Docker paths so traders without an agent runtime can still run the same logic.
  • Published on ClawHub instead of notebook-only workflows so agents get a durable package.

13. What shipped

  • Published as an installable OpenClaw / ClawHub skill
  • CLI and Docker paths for non-agent use
  • Risk-first defaults instead of signal-only demos

14. Inspectable proof

ClawHub skill

Installable OpenClaw skill with live download counts.

Open ClawHub

GitHub + Docker

Human-facing installs alongside the agent skill.

GitHub

Contact:

Open for collaboration, consulting, and engineering opportunities.

  • AI Testing tools / ClawHub skill customization
  • QA / SDET consulting and team advisory
  • Test infrastructure / Docker environment enablement
  • Open-source collaboration with InnoNestX