A comprehensive paper trading environment for simulating cryptocurrency algorithmic strategies using real-time market data without financial risk.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install mock-trading
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install mock-trading using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Mock Trading Agent?
The Mock Trading Agent is a sophisticated simulation environment designed for developers and traders to test algorithmic strategies in the crypto market. As a standout entry in the Openclaw Skills library, it bridges the gap between strategy development and execution by providing a sandbox where users can refine SMA Crossover or Mean Reversion logic.
By leveraging a virtual portfolio and real-time data fetching, this skill allows for high-fidelity backtesting and forward-testing. It is an essential tool for those looking to build robust trading bots within a controlled, risk-free ecosystem before transitioning to live markets.
Mock Trading Agent Use Cases
- Testing algorithmic trading strategies like SMA Crossover without risking capital.
- Monitoring a mock portfolio's performance and PnL in real-time.
- Developing and debugging trading bot logic using Openclaw Skills scripts.
- Education and training on cryptocurrency market dynamics and portfolio management.
How Mock Trading Agent Works
- The skill initializes a virtual bank account using a standardized JSON template containing mock capital.
- A specialized Python bot executes a 'tick' by fetching the latest asset prices from market data sources.
- The bot evaluates the current price history against the selected technical strategy (e.g., SMA Crossover).
- If strategy conditions are met, the bot executes a mock trade (buy/sell).
- The virtual portfolio and trade history are updated and saved locally to reflect the new balance and PnL.
Mock Trading Agent Setup
To get started with this paper trading environment from the Openclaw Skills collection, follow these steps:
- Initialize your virtual portfolio:
cp {baseDir}/assets/portfolio.json ./my_portfolio.json
- Execute a single trading tick for a specific asset:
uv run {baseDir}/scripts/mock_bot.py --portfolio ./my_portfolio.json --asset bitcoin
- (Optional) For continuous automation, add the tick command to your
HEARTBEAT.mdor a system cron job to run every 5-10 minutes.
Mock Trading Agent Data Schema & Taxonomy
The skill manages data through a local JSON structure to ensure persistence and easy reporting:
| Component | File Path | Description |
|---|---|---|
| Portfolio Data | ./my_portfolio.json |
Stores cash balance, asset holdings, and full trade history. |
| Strategy Logic | scripts/mock_bot.py |
Contains the algorithmic evaluation logic and execution flow. |
| Assets | assets/portfolio.json |
The default template with a $10,000 USD starting balance. |
Mock Trading Agent Advanced Features
- Multi-strategy support allowing users to swap between SMA Crossover, Mean Reversion, Momentum Breakout, or RSI logic.
- Automated heartbeat integration for continuous 24/7 paper trading simulations.
- Extensible Python framework for adding custom technical indicators or alternative data sources.
- Detailed PnL and trade history reporting for performance analysis within the Openclaw Skills ecosystem.
SKILL.md
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