Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Robonet's MCP server exposes 24 tools for building, backtesting, and deploying crypto and prediction market trading strategies. Tools span data exploration, AI-powered strategy generation, historical backtesting, Polymarket YES/NO trading, and live deployment on Hyperliquid. Strategies are written in Python using the Jesse framework and can be augmented with Allora Network ML price predictions.
Combines AI strategy generation, parameter optimization, ML signal integration, and live deployment in one tool chain without requiring manual exchange API setup or custom backtesting infrastructure.
When to use it
- Building a custom RSI momentum strategy for BTC-USDT on Hyperliquid
- Backtesting a mean-reversion strategy across 6 months of ETH price history
- Adding Allora ML price predictions to an existing trend-following strategy
- Trading Polymarket prediction markets on political or economic outcomes
- Stopping or restarting a live trading deployment without touching exchange APIs
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Create and backtest a momentum strategy for ETH-USDT on the 4h timeframe
- 1Call get_all_symbols to confirm ETH-USDT is available on Hyperliquid
- 2Call get_all_technical_indicators to identify relevant momentum indicators
- 3Call create_strategy with a description of the desired entry and exit logic
- 4Call run_backtest on 6 months of historical ETH-USDT 4h data
- 5Review returned metrics: Sharpe ratio, max drawdown, win rate, and total return
Python strategy code stored in Robonet with backtest results showing performance metrics across the test period
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.