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moon-dev-trading-agents

by moondevonyt

Master Moon Dev's Ai Agents Github with 48+ specialized agents, multi-exchange support, LLM abstraction, and autonomous trading capabilities across crypto markets

Installation

Pick a client and clone the repository into its skills directory.

Installation

Quick info

Author
moondevonyt
Category
Data Science
Views
232
GitHub repo

About this skill

Master Moon Dev's Ai Agents Github with 48+ specialized agents, multi-exchange support, LLM abstraction, and autonomous trading capabilities across crypto markets

How to use

  1. Sklonuj repozytorium Moon Dev's AI Agents i przejdź do folderu .claude/skills/moon-dev-trading-agents. Upewnij się, że masz zainstalowanego Pythona 3.10.9 — jeśli używasz conda, utwórz środowisko poleceniem conda create -n tflow python=3.10.9, lub skorzystaj z venv/pip.

  2. Aktywuj swoje środowisko Python — w conda wpisz conda activate tflow, w venv użyj source venv/bin/activate (Linux/Mac) lub venv\Scripts\activate (Windows).

  3. Zainstaluj zależności: pip install -r requirements.txt. Jeśli dodasz nowe pakiety, zaktualizuj plik poleceniem pip freeze > requirements.txt.

  4. Uruchom główny orchestrator, który kontroluje wiele agentów, poleceniem python src/main.py. Alternatywnie, aby testować pojedynczego agenta, uruchom python src/agents/trading_agent.py, python src/agents/risk_agent.py lub python src/agents/rbi_agent.py (do backtestingu).

  5. Skonfiguruj giełdy i dostawcę LLM w plikach konfiguracyjnych — system obsługuje Hyperliquid, Solana (BirdEye), Asterdex i Extended Exchange. Sprawdź dokumentację w repozytorium, aby dostosować strategie w folderze src/strategies/.

  6. Monitoruj wyniki agentów w folderze src/data/, gdzie system zapisuje logi, historię transakcji i pamięć agentów. W razie problemów sprawdź logi interakcji między agentami i dostawcą LLM.

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