Selecting a Live Crypto Trader by Competing AI Model Performance
Summary
The workflow pits multiple language models against one another as virtual traders, each with a separate simulated account and tracked performance. Models receive daily, hourly, and five-minute price data with RSI, MACD histogram, ATR, and OBV, then issue standardized instructions to open, close, or hold positions. A selection mechanism promotes a model after its simulated profit clears a threshold; a live account then mirrors that model’s positions, with the stated ability to switch when another model performs better. The system also describes dashboards and structured logs for monitoring model standings and decisions.
This is a design description, not evidence that the method produces reliable returns: no comparative results, test period, risk-adjusted metrics, or costs are reported. Ranking models by accumulated profit can favor luck or short-term conditions, and the document does not explain how it controls for those effects or validates a model before live copying. The workflow’s performance depends on the quality of model prompts, data, execution, and safeguards; indicator inputs and competitive rankings alone do not establish a durable trading edge.
Key ideas
- Each AI model trades a separate simulated account and is ranked by performance.
- Models use indicators from daily, hourly, and five-minute data to produce standardized trade actions.
- A model that passes a profit threshold can be selected for live position mirroring.
- The described system can change which model it follows as rankings change.
- No empirical comparison or evidence of durable profitability is provided.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.