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Throughout my career, I’ve honed my skills, overcoming challenges, failures, and successes that taught me trading is not just about numbers—it’s about discipline, strategy, and an unwavering commitment to improvement. My mission has always been clear: to create solutions that empower traders to operate confidently, even in uncertain times.
Looking back, I don’t just see years of experience; I see a passion that drives me to keep innovating and helping others reach their goals. Every tool I create is built on years of study and practice, but also on a genuine desire to deliver reliable and effective results. For me, trading is not just a job—it’s an art and a science that, when applied correctly, can truly transform lives.
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.
Ваш советник по золоту сегодня молчал. EVA объяснит почему.
MOST GOLD EAs ASK YOU TO TRUST A CURVE. EVA SHOWS YOU EVERY DECISION, AND ANSWERS WHEN YOU ASK WHY. A backtest can be made to look perfect. Train a model on the same history the tester replays, or give losing trades a huge stop until they come back, and almost any curve goes up. EVA does neither. Its engines are fixed rules with a real stop on every trade, and their settings were checked on data that was not used to choose them. EVA will have losing weeks. You will see every one of them, engine
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.
