The integration of Large Language Models (LLMs) into crypto technical analysis modifies the speed of quantitative research, yet demands strict constraints.

LLMs function as non-deterministic reasoning engines. While they excel at structural synthesis (e.g., combining multi-indicator configurations into cohesive market bias narratives), they possess structural limitations. They cannot predict forward prices, they suffer from knowledge cutoff constraints, and they lack real-time API integrations with live order books.

To utilize models like GPT-4o, Claude 3.5, or Gemini 1.5 effectively, analysts must transition from open-ended queries to structured, data-rich prompts. Paste raw telemetry directly (RSI values, MACD histogram delta, Bollinger Band positions) rather than requesting the model to fetch live data.

Part 1 of our AI-Assisted Crypto Trading series breaks down the exact daily workflow, comparative strengths of top models, and 5 production-grade prompts.

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