Context Engineering for Crypto Trading Agents

Context engineering for AI agents is the process of structuring, filtering, and pre-analyzing data before it enters an LLM context window. For crypto trading agents, the difference between raw JSON and pre-reasoned market context is measurable: a typical market data pull drops from 21,000 tokens to 1,250 tokens with pre-analysis, a 94% reduction.

Controlled experiments show agents receiving pre-analyzed context outperform agents receiving raw data by +4.46 to +15.97 percentage points in portfolio returns. The format, structure, and pre-analysis of data affects decision quality as much as the data itself.

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