It executes discipline; it does not create edge
An EA's real value is removing the human element from execution: no FOMO, no revenge trading, no moving the stop under pressure. Those are genuine and expensive execution problems.
But it does not solve the strategy problem. A system with negative expectancy will lose automatically and consistently, and discipline here accelerates the loss rather than preventing it. The right question before running one is not "is this EA good?" but "what is the expectancy of the strategy it executes, after costs?" — see manual versus automated.
Where the assumptions break
Platform terms are usually explained as menu paths, but the real problem is not where to click — it is where the assumption breaks. So this family is written around failure modes: a backtest that looks excellent and does not repeat, a server you believe is the cause of slow fills when it is not, a timeframe you analyse on and execute on differently.
The shared rule: the platform does exactly what you asked, and the error lives in the assumption rather than the tool.
A worked example
An EA executes a system with +0.20R expectancy at 1% risk on $1,000 — $2 a trade before costs. It works tirelessly and takes 200 trades instead of 100.
If execution costs $3 per trade, net expectancy is −$1, and doubling the trade count doubles the loss to −$200 rather than −$100. Automation fixed nothing here; it multiplied the error with perfect discipline — which is exactly why execution cost is the first question before running one, not the last.
Common mistakes with this term
- Judging an EA by backtest results without checking its spread and slippage assumptions.
- Assuming automation creates edge, when it executes whatever edge exists — negative or positive.