Building an AI Trading Workflow for Crypto Perpetual Futures

Summary

The document describes a multi-stage system for using a language model to trade cryptocurrency perpetual futures. Its workflow periodically gathers account and position information, retrieves short- and longer-timeframe market data, calculates indicators such as moving averages, MACD, RSI, and ATR, and supplies structured inputs to an AI decision component before parsing and executing its proposed trades. The account node tracks equity and returns, while the data nodes organize coin-level market and position details. The article also discusses structured output and validation as ways to make automated signal processing more consistent.

The introduction cites a real-money competition among several AI models and claims that one model led, but the excerpt does not provide enough results or methodology to assess that comparison. It identifies possible system weaknesses, including delayed reactions between checks, missing event and news information, variable model outputs, and leverage-related liquidation or slippage risk. Suggested improvements include sentiment inputs, adaptive check frequency, model voting, and historical backtesting. The material is an architecture walkthrough rather than a demonstrated, independently evaluated trading strategy; key portions of the source are omitted from the supplied text.

Key ideas

• The proposed system separates recurring data collection, AI decisions, and trade execution into stages.
• Market inputs combine short-interval and longer-interval candles with technical indicators.
• Account equity, cash, and open positions are tracked and included in the decision context.
• Structured AI outputs can make signal parsing and automated validation more manageable.
• The article identifies event blindness, model variability, leverage, slippage, and fixed polling intervals as limitations.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.