build-trading-strategies

What problem does it solve?

This skill automates the generation of complete, production-ready crypto trading strategy code using AI. It outputs production-grade Python code based on the Jesse framework, including entry/exit logic, position sizing, and risk management, ready for backtesting and deployment after validation.

Core Features & Use Cases

• AI-driven strategy code generation: Creates ready-to-run Jesse strategies for crypto trading, including should_long(), should_short(), go_long(), and go_short() methods, plus optional hooks for on_open_position() and update_position().
• Two generation tools: create_strategy (crypto) and create_prediction_market_strategy (Polymarket YES/NO strategies).
• Best practices: Requires prior data verification and backtesting via test-trading-strategies before deployment.
• Use Case: A developer defines a clear concept and data availability, then generates production code and validates it with backtests.

Quick Start

Use browse-robonet-data to verify indicators and symbols, then call create_strategy with a detailed description and a clear strategy_name. After generation, run test-trading-strategies to backtest and validate performance before deploying to live trading.