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Explore my products:
• Quant Apple Scavenger – https://www.mql5.com/en/market/product/113103
• Quant Scavenger NasDaq – https://www.mql5.com/en/market/product/167438
• Quant Scavenger XAUUSD – https://www.mql5.com/en/market/product/188572
Thank you for visiting my profile. Your feedback and support are always appreciated.
Try it at just 35$
Quant Scavenger FBS – Gold Series EA 概览请通过 https://www.mql5.com/en/users/frankmichel022-gmail 联系我，获取演示版本或实盘咨询，您的反馈对我很重要。 概览 Quant Scavenger – Gold Series EA 是一款基于机器学习的智能交易系统（Expert Advisor），专门为 黄金 CFD（XAUUSD） 设计。 该 EA 使用基于历史 H1（1小时） 数据训练的量化模型，并且仅运行于 H1 时间周期。 核心技术 该 EA 结合了四种机器学习模型： Random Forest XGBoost Gradient Boosting Machine (GBM) LightGBM 多个模型共同判断市场方向。只有当达到设定数量的模型确认同一方向时，EA 才会执行交易。 可选的 LazyFTL（Follow-the-Leader） 系统会评估近期交易表现，并根据表现自动调整当前使用的模型组合。 风险管理基于历史 MAE/MFE 分析 ，使止损和止盈能够根据市场实际行为进行调整。 模型信息
✅ Stop-loss is now calculated in dollar (account currency) terms instead of raw price points.
This ensures risk sizing remains consistent across brokers, regardless of differences in digits or quote conventions. Previously, a fixed-point stop-loss could represent significantly different dollar risk depending on the broker (for example, FBS vs. IC Markets) especially incase of different minimal lot, resulting in inconsistent position sizing.
With this update, the EA targets a real dollar risk amount directly, so both lot sizing and stop-loss placement behave consistently across supported brokers.
Thank you for your continued support and valuable feedback. More improvements are coming!. And Remember this EA works for FBS if you are out there and tested different or want something different contact me.
🔗 Quant Apple Scavenger: https://www.mql5.com/en/market/product/113103
Exness Quant Scavenger – Nasdaq USTECm EA Overview Exness Quant Scavenger – Nasdaq USTECm EA is a single-asset, machine learning–driven quantitative Expert Advisor engineered exclusively for trading: USTECm (Nasdaq CFD) on Exness This system is not multi-asset and not generalized. It is built specifically for the structural behavior of the Nasdaq index CFD (USTECm symbol) as provided by Exness. The embedded quantitative model is trained exclusively on: H1 (1-hour timeframe) data It is not a