Exchange Market Algorithm (EMA)

EMA is a behavior-inspired population-based metaheuristic that simulates stock-market trading to do global numerical optimization (it is not a trading strategy — the market is just the metaphor). Source: andrey-dik's MQL5 implementation.

How it works

The population is split into three trader groups by success:

• Elite (top ⅓) — keep their (best) positions unchanged.
• Middle class (middle ⅓) — learn by imitating the elite.
• Beginners (bottom ⅓) — learn from both groups and take higher risks.

Two phases per iteration: a balanced-market phase (conservative, mentor-imitation → exploit) and a fluctuating-market phase (aggressive exploration with random resets + opposition-based learning). See exploration-vs-exploitation.

Benchmark (context-relative)

Ranked 45/45 (37.40%) on the population-optimization-benchmark — near the bottom: "limited practical performance despite conceptual elegance." A data point for the wiki's worry about metaphor-inflation (a fresh metaphor over standard explore/exploit mechanics) and for the no-free-lunch-theorem (see synthesis).

Why T2 (re-tiered 2026-08-25)

Held at T3 since ingest, which graded the venue — MQL5, a broker's community article site — rather than what the page rests on. This page's load-bearing claims are a mechanism description of an algorithm published by Ghorbani and Babaei (2014) and a comparative score, both from andrey-dik, who did not write the algorithm and has no stake in where it lands. That is somebody measuring somebody else's work, which QUALITY.md's ladder puts at T2.

The spoke's standing finding — that in this field the scorer is usually the author — is real and does not apply here; it applies to DOAm, which stays T3 because there Dik is scoring his own modification. The confound that does remain is implementation: Dik re-implements each optimizer in MQL5, so his figure grades his code as much as the method, and his scoring scheme changed mid-series (population-optimization-benchmark). Both stay flagged in prose, where a methodological limit belongs — not in the tier.

Note the floor is unmoved by this: T1 ÷ tiered counts T1 against the same denominator either way, so the change alters the tier mix and not the spoke's floor of 0.18.

Related

metaheuristic-optimization · exploration-vs-exploitation · population-optimization-benchmark · no-free-lunch-theorem · andrey-dik · deterministic-oscillatory-search