Omega J Msigwa / 个人资料

The article builds a reusable validation layer for Expert Advisors in MQL5. It implements lot-size rules and normalization, SL/TP and freeze-level guards, price digit normalization, margin sufficiency checks, unchanged-level filtering on modifications, account order-limit control, new-bar detection, symbol tradability checks, economic-calendar news windows, and session detectors. The result is cleaner code and fewer terminal errors in live trading.

This article presents a compact MQL5 utility layer for routine trade operations. It includes position existence checkers, position counters, bulk close helpers, and functions to retrieve the most recent or oldest position by symbol, magic, or type. A simple SMA crossover Expert Advisor demonstrates integration. The result is cleaner EAs, fewer inconsistencies across projects, and faster maintenance.

This article presents a MetaTrader 5–compatible backtesting workflow that scales across symbols and timeframes. We use HistoryManager to parallelize data collection, synchronize bars and ticks from all timeframes, and run symbol‑isolated OnTick handlers in threads. You will learn how modelling modes affect speed/accuracy, when to rely on terminal data, how to reduce I/O with event‑driven updates, and how to assemble a complete multicurrency trading robot.

From ChatGPT to Gemini and many model AI tools for text, image, and video generation. Transformers have rocked the AI-world. But, are they applicable in the financial (trading) space? Let's find out.

In this fascinating article, we build our very first trading robot in the simulator and run a strategy testing action that resembles how the MetaTrader 5 strategy tester works, then compare the outcome produced in a custom simulation against our favorite terminal.

In this article we introduce Python-MetaTrader5-like ways of handling trading operations such as opening, closing, and modifying orders in the simulator. To ensure the simulation behaves like MetaTrader 5, a strict validation layer for trade requests is implemented, taking into account symbol trading parameters and typical brokerage restrictions.

In this article, we introduce functions similar to those provided by the Python-MetaTrader 5 module, providing a simulator with a familiar interface and a custom way of handling bars and ticks internally.

This article shows how to simplify complex MQL5 file operations by building a Python-style interface for effortless reading and writing. It explains how to recreate Python’s intuitive file-handling patterns through custom functions and classes. The result is a cleaner, more reliable approach to MQL5 file I/O.

In this article, we will attempt to predict the market with a decent model for time series forecasting named DeepAR. A model that is a combination of deep neural networks and autoregressive properties found in models like ARIMA and Vector Autoregressive (VAR).

Integrating Python's logging module with MQL5 empowers traders with a systematic logging approach, simplifying the process of monitoring, debugging, and documenting trading activities. This article explains the adaptation process, offering traders a powerful tool for maintaining clarity and organization in trading software development.

Unlike MQL5, Python programming language offers control and flexibility when it comes to dealing with and manipulating time. In this article, we will implement similar modules for better handling of dates and time in MQL5 as in Python.

The MetaTrader 5 module offered in Python provides a convenient way of opening trades in the MetaTrader 5 app using Python, but it has a huge problem, it doesn't have the strategy tester capability present in the MetaTrader 5 app, In this article series, we will build a framework for back testing your trading strategies in Python environments.

The schedule module in Python offers a simple way to schedule repeated tasks. While MQL5 lacks a built-in equivalent, in this article we’ll implement a similar library to make it easier to set up timed events in MetaTrader 5.

N-BEATS是一款革新性的深度学习模型，专为时间序列预测打造。该模型的初衷是超越ARIMA、PROPHET、VAR等传统时间序列预测模型。在本文中，我们将探讨该模型，并利用其实现股市行情预测。

在本文中，我们实现了一个类似于 Python 中 requests 模块的功能，以便更轻松地使用 MQL5 在 MetaTrader 5 中发送和接收 Web 请求。

Python 中的 sqlite3 模块提供了一种使用 SQLite 数据库的简单方法，它既快速又方便。在本文中，我们将在内置的 MQL5 函数的基础上构建一个类似的模块，用于处理数据库，使在 MQL5 中使用 SQLite3 数据库更容易，就像在 Python 中一样。

Prophet模型由Facebook研发，是一款稳健的时间序列预测工具，能够自动捕捉趋势、季节性与节假日效应，且几乎无需人工调整。该模型已被广泛应用于需求预测与商业规划。在本文中，我们将探讨Prophet模型在外汇交易品种波动率预测中的效果，展示其如何应用于传统商业场景之外的任务。

与 Telegram 类似，Discord 可以使用其通信 API 以 JSON 格式接收信息和消息。在本文中，我们将探讨如何使用 Discord API 将 MetaTrader 5 的交易信号和更新发送到您的 Discord 交易社区。

Explore how Vector Autoregression (VAR) models can forecast Forex OHLC (Open, High, Low, and Close) time series data. This article covers VAR implementation, model training, and real-time forecasting in MetaTrader 5, helping traders analyze interdependent currency movements and improve their trading strategies.

你是否曾盯着行情图，产生过一种奇妙的感觉…… 仿佛表面之下就藏着某种模式？一套秘密代码 —— 只要能破解它，就能揭示价格的走向？来认识一下 LGMM 吧 —— 市场隐藏模式探测器。这是一种机器学习模型，帮助识别市场中的那些隐藏模式。