paper-trader

What This Skill Does

The Paper-Trader skill acts as an autonomous financial agent designed for simulated trading environments. It functions as a master orchestrator that manages multiple sub-strategies, including high-volatility memecoin scanning, neutral polymarket arbitrage, and directional prediction market research. By operating a virtual portfolio of $30,000, it teaches the AI to manage risk across diverse asset classes while adhering to strict drawdown limits and correlation constraints. Unlike basic trading scripts, this agent keeps a detailed journal of its performance, updates its own operational logic through a continuous learning loop, and provides proactive status updates to its human administrator, 'Rick'. It emphasizes capital preservation through mandatory stop-losses and automated circuit breakers that pause activity if specific daily or weekly loss thresholds are breached.

Installation

To integrate this agent into your system, use the terminal command: clawhub install openclaw/skills/skills/rimelucci/reef-paper-trader. Ensure your OpenClaw environment has adequate access permissions for file I/O, as the agent requires read/write access to its local directory to maintain the journey_log.md and master_portfolio.md files. Once installed, the agent will initialize its three core sub-strategy modules automatically. It is recommended to initialize the portfolio state by reviewing the default references/ directory configurations before allowing the agent to begin its first trading day.

Use Cases

This skill is perfect for developers or traders who want to backtest high-risk market strategies without risking real capital. It is ideal for testing how an AI agent handles volatility in the memecoin market while balancing those risks against more stable prediction market arbitrage. It also serves as an excellent demonstration of autonomous self-improvement, where the agent modifies its own strategy documentation based on past win/loss ratios. Use it to prototype new trading logic, train on market data analysis, or simply to track how an LLM makes decisions under stress in simulated financial conditions.

Example Prompts

1. "Analyze the current performance of the memecoin-scanner. Did it stay within our risk parameters during today's market dip?"
2. "Update the strategy_evolution.md file with the lessons learned from the failed election prediction trade yesterday."
3. "Summarize our total exposure and provide a risk report for all active positions across the three strategies."

Tips & Limitations

To get the most out of the Paper-Trader, ensure that the rick_preferences.md file is regularly updated with your personal risk tolerance settings. The agent is strictly designed for paper trading; do not attempt to connect this to real exchange APIs without significant review, as it lacks the security hardening required for live capital. Remember that the agent's 'self-improving' capabilities are based on its internal logs; if the initial training data or the system's reasoning is biased, the agent may double down on losing strategies. Always review the risk_events.md file weekly to catch any dangerous patterns the agent may have developed.