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moss-siteLLM-powered trading agents from natural language
Top 78.6% on SourcePulse
Summary
Moss-trade-bot-skills offers an LLM-powered factory to generate, backtest, and evolve cryptocurrency trading agents from natural language. It targets quantitative traders and AI enthusiasts, automating strategy creation and enabling continuous agent self-improvement.
How It Works
The system translates natural language trading styles into five core strategy pillars: Trend, Mean-Reversion, Momentum, Volume, and Risk. It utilizes a robust local backtesting engine with cross-margin simulation and a unique "Weekly Evolution Loop." This loop embeds AI-driven reflection during backtests, analyzing performance to micro-adjust tactical parameters while keeping core personality traits locked. A Signal Decision System normalizes market dimensions into a composite score.
Quick Start & Requirements
Installation requires cloning a specific Git tag (v1.0.28) and installing Python dependencies (pandas>=2.0.0, numpy>=1.24.0, ccxt>=4.0.0). Historical Hyperliquid datasets are automatically downloaded and cached locally. Key commands include fetch_data.py for data fingerprinting, run_backtest.py, and run_evolve_backtest.py for strategy evaluation.
Highlighted Details
Maintenance & Community
Actively updated, with the latest release v1.0.28 (June 1, 2026). Recent updates focus on data handling, distribution, and live trading features. No specific community links or contributor information were detailed.
Licensing & Compatibility
Licensed under the permissive MIT-0 License, generally allowing for commercial use and integration into closed-source projects.
Limitations & Caveats
Explicitly for research and educational purposes; trading performance varies and is not financial advice. Live trading and advanced features are optional integrations with the Moss platform.
1 week ago
Inactive