LLM-Driven Perpetual Futures Trading with Multi-Timeframe Risk Controls

Summary

The document describes an automated cryptocurrency perpetual futures system organized around data collection, AI decisions, order execution, and position monitoring. It combines short-interval candles for entry timing with longer-interval trend context, technical indicators, account state, and open-position details. The model is prompted to return structured entry, hold, or close signals with targets, stops, leverage, risk, confidence, and reasoning. The execution layer sizes positions from the stated risk amount and stop distance, observes exchange precision limits, and monitors exits.

Risk rules include limits on simultaneous positions and per-trade account risk, a minimum risk-reward ratio, volatility-aware stops, and a separate condition for invalidating a trade. The workflow also forbids adding to an existing position and provides a dashboard for review. The document presents architecture and example rules rather than measured results; it does not provide a validated performance record. Its claims about risk control depend on implementation details, while leveraged crypto trading and model decisions remain exposed to losses and execution risks.

Key ideas

• The system combines short-term entry data with longer-term trend analysis.
• It supplies the language model with indicators, account details, and current positions.
• Trade signals specify entry, hold, or exit actions alongside stops, targets, and risk fields.
• Position sizing uses the planned risk and stop distance, subject to exchange and leverage constraints.
• The document describes safeguards but gives no empirical performance evidence.

Tags

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