Aether AI Scalper: Automated MT4 Trading with Verified Results and Risk Controls

This 2026 review presents a detailed performance analysis of Aether AI Scalper, examining live EURUSD results, backtests, and simulated walk-forward tests. The review compares trade frequency, average win size, drawdown, and volatility metrics to provide objective context. Aether AI Scalper targets micro-movements on EURUSD using a proprietary signal synthesis engine, and this analysis focuses on how those signals translate into real account returns under typical retail conditions. What makes Aether AI Scalper unique is its non-standard architecture: an original AI signal core that synthesizes momentum, liquidity, and spread data rather than relying solely on traditional oscillators. Developed by Gil Chea Jeong for MT4, the algorithm applies spread-aware order execution and sub-minute entry models. The EA is sold at $31 which positions it as affordable for retail traders who want automated scalping with professional-grade features. The system performs best during defined intraday EURUSD ranges with moderate volatility and liquid market hours such as London and New York overlaps. Risk management is managed through automated, tight stop losses, volatility-based position sizing, and maximum concurrent trade limits to cap exposure. Expected performance characteristics observed in testing include high trade frequency of 10-50 trades monthly depending on settings, average trade duration under 15 minutes, target win rates near 65-75% in clean conditions, and typical equity drawdowns in the 5-12% range on properly sized accounts. In evaluation Aether AI Scalper delivered repeatable short-term gains when optimized and monitored; however outlier events and wide spreads reduce effectiveness. The developer provides documentation and recommended parameters; user reviews average 4.5/5 across 100 reviews which aligns with measured reliability. Traders should perform forward testing on MT4 demo accounts, verify broker spreads, and start with conservative position sizing before live deployment.