Selecting a Live Trader from Competing AI Trading Models
Summary
This proof of concept runs four AI models in parallel on the same market data. Each model makes independent decisions using multiple timeframes and technical indicators, then trades in a virtual account. The system ranks models by realized profit and copies the virtual position of the current leader to a live account. Standardized actions and recorded rationales make decisions easier to compare and review. The design also uses ranking prompts to test whether competitive framing changes model behavior.
The account describes differing model styles and gives a snapshot of their technical rationales, but it does not provide rigorous comparative performance results or evidence that competition improves returns. Its main stated limitation is inference latency, which can cause execution prices to diverge from decision prices. API cost and availability also matter. The author positions the system as a research prototype, not a solution for large scale live trading; model selection based on past virtual performance may not persist in changing markets.
Key ideas
- Several AI models receive the same market data and produce independent trading decisions for comparison.
- Virtual portfolios rank model performance, and the current leader's position is synchronized to live trading.
- Standardized actions and logged rationales support consistent monitoring of model decisions.
- Ranking prompts are intended to influence model behavior, though the account offers no controlled evidence of improved results.
- Inference delays, API costs, and service reliability limit practical live execution.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.