An autonomous orchestrator for paper trading memecoins and prediction markets that evolves its own strategies through continuous learning and performance analysis.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install reef-paper-trader
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 reef-paper-trader 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 Paper Trader: Autonomous Self-Improving Trading Agent?
Paper Trader is a sophisticated framework within the Openclaw Skills ecosystem designed to act as a top-level orchestrator for autonomous trading operations. It manages a unified portfolio across multiple high-volatility strategies, including Solana memecoin discovery and Polymarket arbitrage. Unlike static trading bots, this skill is built on a self-improvement loop where the agent analyzes every trade outcome, documents the narrative in a journey log, and updates its own logic to refine its competitive edge over time.
The system prioritizes capital preservation through a rigorous risk management framework that overrides individual strategy rules. By maintaining a living journal and proactive communication via Telegram, Paper Trader ensures that the user is always informed of portfolio health, strategy evolution, and market insights without needing to prompt the agent for updates.
Paper Trader: Autonomous Self-Improving Trading Agent Use Cases
- Autonomous paper trading of Solana memecoins to test discovery and entry patterns without financial risk.
- Executing market-neutral arbitrage and research-based directional trades on prediction markets like Polymarket.
- Continuous strategy refinement through a structured learning loop that updates skill documents based on historical performance.
- Real-time portfolio monitoring and automated reporting for high-frequency trading environments.
How Paper Trader: Autonomous Self-Improving Trading Agent Works
- The agent initializes the environment by loading the master orchestrator and sub-strategy Openclaw Skills to define its operational boundaries.
- It executes a scheduled daily routine, beginning with an overnight review of positions and market-clearing events.
- The orchestrator delegates specific market scans to sub-strategies, such as the memecoin-scanner for Solana or the polymarket-research module for event-based trading.
- All trade entries and exits are executed within a unified risk management framework that monitors total portfolio exposure and correlation limits.
- Post-trade analysis is automatically logged, and every 10 to 30 trades, the agent perform a deep-dive analysis to update its own markdown-based strategy files, effectively evolving its code.
- The agent maintains a proactive communication loop, sending morning briefings and daily digests via Telegram to keep the user updated on P&L and focus areas.
Paper Trader: Autonomous Self-Improving Trading Agent Setup
To deploy this skill, ensure your agent has access to the filesystem and a configured Telegram bot for notifications.
# Navigate to your agent's skills directory
cd skills/
# Clone the paper-trader skill structure
git clone https://github.com/openclaw/paper-trader-skill.git
# Initialize the reference files for tracking
touch references/master_portfolio.md references/journey_log.md
Configure your Telegram API keys and starting capital in the references/master_portfolio.md file before initiating the first scan.
Paper Trader: Autonomous Self-Improving Trading Agent Data Schema & Taxonomy
The Paper Trader skill uses a structured markdown schema to maintain state, memory, and strategy evolution across sessions.
| File Name | Data Type | Description |
|---|---|---|
master_portfolio.md |
Markdown Table | Tracks unified balances, strategy allocations, and P&L metrics. |
journey_log.md |
Narrative Log | A chronological record of trades, market reflections, and agent discoveries. |
strategy_evolution.md |
Technical Log | Tracks changes made to trading logic and cross-strategy performance comparisons. |
risk_events.md |
Incident Log | Documents limit breaches, drawdowns, and the subsequent corrective actions taken. |
rick_preferences.md |
Metadata | Stores user-specific risk tolerance, communication styles, and focus areas. |
Paper Trader: Autonomous Self-Improving Trading Agent Advanced Features
- Multi-agent strategy delegation that allows for simultaneous execution of memecoin scanning and prediction market arbitrage.
- Automated risk-level response protocol that adjusts position sizing or pauses trading based on daily and weekly loss limits.
- Dynamic rebalancing logic that triggers automatically when strategy weights drift more than 15% from targets.
- Self-repair protocol that identifies gaps in strategy documentation and researches improvements to codify into new Openclaw Skills.
- Proactive Telegram integration for unprompted alerts, risk warnings, and scheduled performance reporting.
SKILL.md
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