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在职业生涯的每一步，我都在不断提升自己的技能，经历了挑战、失败和成功，这让我深刻理解到，交易不仅仅是数字，它更需要纪律、策略，以及对持续改进的执着追求。我的目标始终明确：为交易者打造能够在任何市场环境中都能自信操作的解决方案。
回首过去，我不仅看到了多年的经验，更感受到了驱动我不断创新的热情，以及帮助他人实现目标的动力。我开发的每一项工具，都源于多年的研究和实践，同时也源于我真诚的愿望：提供可靠且高效的成果。对我来说，交易不仅是一份工作，它是一门艺术与科学，合理运用时，它能真正改变人生。
Presentamos una clase reutilizable de dimensionamiento adaptativo, CRiskEngine, que calcula el lote por riesgo y stop y lo reduce según tres factores medibles: racha de pérdidas, volatilidad actual frente a su promedio y caída de capital. Se incluye un asesor de prueba con modos conmutables para comparar seis esquemas (lote fijo, riesgo fijo, cada factor y combinados) sobre 842 operaciones de EURUSD, con métricas y curvas. El lector obtiene un motor listo para integrar y una medición clara del efecto de cada factor.
A Sharpe ratio read off the best of many optimization runs is not the number it looks like. This article ships a reusable native CDeflatedSharpe class that turns a raw Sharpe into an honest confidence statement. The Probabilistic Sharpe Ratio corrects it for sample length and for skew and kurtosis; the Deflated Sharpe Ratio adds the correction almost nobody applies, for the number of variants you tried before keeping the best. Everything is from scratch, the sample moments, the normal CDF and its inverse included, so there is no Python, no DLL and no library. On a real sweep of 56 moving-average variants on XAUUSD the winner looked significant at 98.5 percent by PSR, then fell to 90.6 percent once the 56 trials were admitted, below the usual bar. That gap is the selection bias, made measurable.
The series develops state persistence for MQL5 Expert Advisors. Part 1 delivers a crash-safe key-value store: a CStateStore class that saves through a temporary file and a rename, carries a versioned header with a checksum, and stores integers, doubles, strings, booleans, and double arrays, plus a demo advisor that resumes a counter and a rolling window after a restart. Readers get a compact include file and a pattern that protects the live state file if the process dies mid-save.
This article applies the Kelly criterion to position sizing in native MQL5. It presents a reusable CKelly class that estimates win rate and payoff from closed deals, derives the Kelly fraction, and sizes lots from a stop distance. A Monte Carlo sweep of the Kelly multiplier shows growth peaking at full Kelly while drawdown and ruin increase, motivating fractional Kelly such as half Kelly that preserves most growth with materially lower drawdown.
大多数黄金 EA 要您相信一条曲线。EVA 向您展示每一个决定，并在您问“为什么”时给出回答。 回测可以做得完美无瑕。用测试器回放的同一段历史训练模型，或者给亏损单一个巨大的止损直到它回来，几乎任何曲线都会上扬。EVA 两者都不做。它的引擎是固定规则，每笔交易都有真实止损，参数也在没有用于选参的数据上做过检验。EVA 也会有亏损的周，您会在日志中按引擎逐一看到每一周。 您得到的，是对自己黄金交易的掌控： 您能看到 EVA 为什么交易、为什么没有交易：在等待时段、波动率低、新闻窗口、点差或亏损限额。 用您的语言，在图表上或手机上提问，EVA 会根据您的实盘账户回答。 保护功能监控整个账户，内置自营交易公司（Prop firm）模式。如有需要，也可覆盖您的手动交易和其他 EA。 每次成交都会显示经纪商向您收取了多少：点差、滑点和手续费，以账户货币计算。 首发价：前 10 份 399 USD。 每售出 10 份，价格上涨 100 USD。下一个价格：499 USD。 每笔交易都有真实止损 · 无网格 · 无马丁格尔 · 不摊平亏损 · 在策略测试器中亲自验证 免费演示： 在策略测试器中用
The series develops machine learning in 100% native MQL5 with no external dependencies. Part 1 delivers logistic regression from first principles: a CLogReg class with standardization, a stable sigmoid, SGD training, and model persistence, plus a script that builds ATR-normalized features, labels the next bar, and tests out-of-sample against a baseline. Readers get a compact include file and a clear template for leakage-free evaluation.
