Stratum EA: Adaptive MT5 Scalper with Q-Learning Self-Optimization for Consistent Returns

This Stratum EA review for 2026 examines live and backtested performance with transparent analysis of win rate, drawdown, and trade cadence. In this review and analysis we focus on how the adaptive Q-Learning engine alters behavior after each trade and whether that yields persistent improvement over months. Stratum EA is built for MT5 and combines Smart Money Concepts signals with a reinforcement learning module that ingests 14 features per signal to optimize entries and exits. What makes Stratum EA unique is the integration of SMC patterns—order blocks, fair value gaps, break of structure and CHoCH—with a self-optimizing Q-Learning core written entirely in MQL5. The EA evaluates H4 and H1 trend bias and times entries on M15, M5 and M1 to scalp liquidity in high-probability zones. Kaan Caliskan, the developer, emphasizes transparency: the product supports paper trading and auto-promotion to live accounts so the model can continue learning without risking full capital initially. The algorithm favors short-duration trades, typically holding positions a few minutes to a few hours depending on setup confirmation. Stratum EA is designed to perform best in trending and structured ranging markets where SMC levels are cleanly defined, and it uses adaptive position sizing plus configurable risk limits to control drawdown. Expect periods of learning where performance stabilizes after 200–500 live trades as the reinforcement model refines state-action values.