MQL5 Programs Market

This article builds an MQL5 dashboard that classifies closed deals into Sydney, Tokyo, London, and New York trading sessions by UTC close time. It computes per-session P&L, win rate, and average hold time, then renders the results as a persistent CCanvas bar chart with an account-wide summary row, alongside an Experts tab summary table. A verification script confirms the classification and metrics logic behaves correctly.

Article revisits Trading Robot that merged Discrete Fourier Transform with Leaky Integrate-and-Fire Spiking Neural Network. Evaluation is made over the seven operating modes with different symbols, timeframes, and test windows. We use two-thirds of the test window to optimize while the one-third does a forward walk run. This study tries to detail parameter interactions, input scaling, gating effects, and outlines practical checks that include rolling windows, frozen inputs, and others. The goal remains identifying settings worth further testing or paper trading.

At first, this article may seem rather confusing because of the material I'm going to cover in it. Nevertheless, I've tried to explain everything as simply and clearly as possible. I hope you'll understand what I'm about to show you here, and that it will come in handy someday.

One of the issues that bothered me the most was that the `C_ElementsTrade` class contains code for accessing positions. Don't take this as a mistake, because it really isn't one. However, this increases the risk of errors in some of the tasks we will need to handle later. All work on implementing the position indicator was carried out with a view to its use in the replay/simulation service. However, when running in this environment, we will have no access to actual positions. Consequently, any call to the MQL5 library intended to retrieve position data will have no effect in this environment.