Why LLM Trading Agents Need Market Microstructure Data, Not Just Candlesticks

Developer and MidasFlow contributor argues that LLM-based crypto trading agents fail when fed only standard OHLCV candlestick data, which strips out the context needed to explain price moves. Candles cannot distinguish between organic buying, forced liquidations, or single-exchange anomalies — all of which carry different implications for price continuation. The author identifies four data types that meaningfully improved agent decision quality: order flow via Cumulative Volume Delta, cross-exchange origin tracking, multi-exchange liquidation data, and calibrated probability scores. Rather than hardcoding REST API parsers, the post advocates using Model Context Protocol with a self-describing schema, which allows agents to interpret data feeds more reliably in a reasoning loop. The core argument is that better data transport and richer market context — not a smarter model or longer prompt — is what separates plausible-sounding agent outputs from accurate ones.