This article explores the practical use of OrderSendAsync in MT4 for latency arbitrage strategies. It includes a complete implementation with risk controls and addresses the nuances of asynchronous execution.
This article explores the undocumented use of OrderSendAsync in MQL4 to simulate realistic market execution and slippage during backtesting, complete with a working EA and practical migration notes.
This article explores a novel method for detecting order flow anomalies during MT4 backtesting using genetic algorithms. It includes a complete MQL4 implementation and addresses common pitfalls in EA optimization.
This article exposes five structural failures that cause EAs to fail in live trading, including Error 130 root causes, OrderModify() rejection patterns, and state persistence strategies, with MQL4 code examples.
This article exposes a critical flaw in MQL4 EA development: order comments can be altered by brokers, breaking logic. It offers a robust tracking system using OrderTicket() and a custom associative array to replace fragile comment-based filtering.
This article tackles the hidden dangers of using OrderComment() for order identification in MQL4, exploring how brokers modify comments. It provides a practical state machine architecture as a robust alternative.
This article exposes the undocumented broker practice of appending tags to OrderComment, breaking EA logic. It provides a battle-tested UUID mapping system and a multi-layered identification model to ensure robust trade management.
This article details how to construct a Volume Profile indicator and EA in MQL4 without access to tick-by-tick data. It introduces a "Pseudo-Delta" calculation method and a unique divergence filter for trade entry.
This article explores advanced EA parameter optimization using Bayesian Optimization in MQL5, contrasting it with genetic algorithms. It addresses overfitting, walk-forward validation, and provides unique insights into the "optimization paradox".
This article explores advanced EA parameter optimization in MQL4 using genetic algorithms. It provides runnable code, discusses common pitfalls like overfitting and look-ahead bias, and introduces an original "stability-first" optimization framework. Based on real backtesting experience.
This article dissects EA parameter optimization in MQL4, moving beyond simple grid searches. It presents a hybrid adaptive walk-forward method with a compilable genetic algorithm snippet, highlights the perils of overfitting, and offers a unique perspective on using optimization as a diagnostic tool rather than a performance booster.
A critical look at EA optimization that prioritizes robustness over curve-fit performance. Includes a custom MQL4 script for walk-forward analysis and an argument for shifting focus from GA to stability metrics.
This article explores practical parameter optimization strategies for Expert Advisors, comparing genetic algorithms with exhaustive grid search, and introduces a hybrid approach to balance speed and robustness.
This article challenges conventional EA optimization practices, exposing the hidden dangers of genetic algorithms and in-sample overfitting. It presents a walk-forward validation framework with MQL5 implementation and proposes a novel "stress decay" metric for parameter robustness.
This article challenges the traditional fixed-parameter EA optimization paradigm. It introduces a dynamic threshold adaptation mechanism combined with walk-forward validation, complete with compiled MQL4 code and empirical evidence from 5-year EURUSD data.
This article debunks common myths about MT4 backtesting accuracy, particularly the 'tick precision' setting. It provides a custom simulation framework that reveals hidden errors and offers a pragmatic approach to improving backtest reliability.
This article explores the overlooked nuances of MQL4's OrderSend function, focusing on slippage, hidden parameters, and a original dynamic slippage model. It provides complete code for a robust order execution function.
This article deconstructs the MQL4 OrderSend function, revealing critical flaws in market execution handling. It provides a robust, custom order-sending function, introduces the original "Shadow Order" concept for backtest reliability, and exposes a common OrderSelect pitfall.
A deep dive into MQL4 OrderSend function and MarketInfo limitations. Uncovers a critical timing bug in price retrieval and offers a robust, self-healing order placement module with exponential backoff retry logic.
This article scrutinizes MQL4's OrderSend function beyond the basics. It uncovers hidden execution pathologies, presents a robust order placement wrapper, and introduces an original "slippage stress-test" methodology. Includes production-grade code.
Confident in our product, so we welcome you to try it free! Strongly recommended to try directly on a live account. Of course, you can also start with a demo account to get familiar with the EA logic first.
♥ Limited slots, claim now ♥Any pattern that arises in nature or exists can be effectively discovered and modeled by classical learning algorithms.
"The market is always changing; the ability to adapt to change is the core advantage of a trader.
"Risk comes from not knowing what you are doing.
"EA automated trading is not meant to replace people entirely, but to overcome human weaknesses.