Articles on MetaTrader 5 integration using MQL5

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.

In this article, we will implement a number of improvements to ensure that the position indicator accurately reflects the actual state on the trading server in terms of open positions and their current state. I should point out that the applications shown here are in no way intended to replace any of the elements available in MetaTrader 5. They should also not be used without due caution and a balanced approach, since their purpose is to provide educational code—that is, code intended solely for learning how the system works. The reason I call this code “educational” is that, in some cases, using messages is not the best way to implement certain functions.

Part 3 implements a scanline rasterizer in pure MQL5. We convert path segments into top‑down edges, sweep each row, sort crossings, and apply the non‑zero winding rule to decide which pixels are inside. The result is a single Fill(path, color, buffer, w, h) routine that draws rectangles, stars, glyphs, or 2000‑edge shapes without per‑shape code, ready to integrate into your MQL5 projects.

In this article, I have shown—in the clearest possible way—how to modify and improve code capable of handling specific tasks while making as few changes as possible to the existing code. We will add a volume display and, at the same time, ensure that users or traders cannot effectively remove objects created by the position indicator.

This article shows how a PDF works as plain text by hand‑written two files: a 592‑byte page and an 885‑byte trade ticket. It explains the file structure (header, body, xref, trailer), the required page objects and resources, and the operators that draw text, then provides an MQL5 script to generate them. First part of a pure‑MQL5 PDF library series.

The rasterizer now accumulates exact span coverage in X and sampled coverage in Y, and blends it via CairoBlendOver on straight ARGB. CairoAaSamples sets the number of vertical samples at runtime, making the cost nearly linear and localized to edges. Readers get smoother boundaries, correct compositing of translucent shapes, and controllable performance.

We map the parts of Algo Forge that impact day-to-day MQL5 work: opening repos in MetaEditor, README translation, Units, branch/tag protection, browser patching, packages, and CI on a Windows host. The article shows exact settings, required runner labels, and how to gate merges on builds. You leave with reproducible steps to protect code, automate compiles, and ship artifacts.

This second part adds the geometry layer to a Cairo‑inspired graphics library for MetaTrader 5. It defines a path of double‑precision points grouped into contours, records open/closed intent, and stores vertices in a flat array with start indices. We implement MoveTo, LineTo, Close, provide basic shape helpers, and include a demo that visualizes the built geometry for inspection and reuse.

This part introduces cubic Bézier curves and a quadratic verb, flattened on append by recursive midpoint splitting (De Casteljau). A conservative flatness test sums distances of control points to the chord and stops within a pixel-based tolerance, adapting detail to curvature. The design keeps downstream code intact while giving you efficient, scale-independent curve quality.

Until now, the application being developed as part of this series of articles has focused exclusively on simulating the graphical part. However, to obtain a more complete system in which we can test the Expert Advisor within the replay/simulation service, we also need to simulate the trading server. You'll notice that this simulation will include only the most essential elements. Nevertheless, you, dear reader, will be able to fill in the missing parts. Since these additional components don't affect what I want to show, we already have more than enough to implement what we have in mind.

A practical route from the option chain to probability levels inside MetaTrader 5. For GLD, SLV, SPY, and USO, the risk-neutral density implied by each ETF's options is recovered with the Breeden-Litzenberger identity and checked for static arbitrage, after the method itself is validated against an analytic lognormal. A Python feed publishes the percentiles; an MQL5 service rescales them onto a related symbol, draws levels, and alerts when price leaves the risk-neutral 5 to 95 percent band. Every reading carries its own diagnostics, and full source is included.

Learn how to create advanced visualizations in MetaTrader 5 using a Matplotlib-like API from MQL5 while Python handles rendering. We implement key chart types—scatter, bar, horizontal bar, histogram, pie, and hexbin—and show how to control axes, legends, colorbars, and styles. The result is a consistent workflow for exploring trading and financial datasets.

This part finishes the geometry layer: arcs use a sagitta‑based angular step tied to the pixel tolerance, so segment counts are computed, not guessed. The path gains ArcTo, AddCircle, AddEllipse and AddRoundRect with per‑corner radii, enabling common UI shapes such as tabs, pills, badges, rings, gauges and donut wedges.

We're entering the home stretch. The development of the replay/simulation system is nearly complete. Of course, we still have a few things left to finish, but compared to everything we've already done, completing what's left won't be difficult. However, it is essential to fully absorb and understand everything covered in this article. So I hope you enjoy reading this and, above all, that you enjoy this final stage of the journey.

In this article, we will make the necessary changes so that the position indicator displays the financial result. This way, the trader will be able to get an idea of the financial result of an open position. In addition, I will tell you something that many people do not know, even those who have been using MQL5 for a long time: how to use static variables to share memory and avoid declaring a global variable in the main code.

This article defines a Protocol Buffers contract for the MetaTrader 5-Python boundary and implements a length-prefixed Protobuf-over-TCP client in MQL5, since MQL5 cannot speak real gRPC natively. A small Python shim relays those frames to a genuine grpc.aio server, unary today, with streaming already live on the backend. You get schema-enforced, strongly-typed messages, explicit errors, retry/backoff, and a Strategy Tester cache for reproducible backtests where sockets don't run.

We present a multi‑symbol EA structure that moves symbol‑specific state into independently initialized CStrategyNode objects and manages them with CPortfolioManager. Using CArrayObj with FreeMode, Add(), At(), Delete(), Total(), and Clear(), the manager ensures safe ownership and isolation, while timer‑driven routing and completed‑bar gates keep processing deterministic. This lets you scale portfolios, control risk per node, and avoid duplicated event handling.

We build a Python reader that attaches to the MetaTrader 5 shared memory segment, reproduces the Arrow columnar layout, and constructs ArrowArray/ArrowSchema via ctypes so pyarrow imports ticks without copying. The code honors the three-way schema contract and the seqlock generation counter to read consistent batches. A standalone benchmark verifies performance against a legacy per-field struct.unpack path with real numbers.

Learn how a viewer opens a PDF backwards, why xref entries must be exactly 20 bytes, and how a two-character change falsifies multiple offsets. We reproduce three typical failures, compare results in PDFium and pypdf, and extract four strict checks a generator must pass. The included PdfValidate.mqh class for MQL5 applies these checks and returns actionable offsets and object numbers for reliable testing beyond “it opens”.

In the previous article, we looked at an example of implementing a class in MQL5 to provide basic support. Its purpose is specifically to allow SQL code to be stored in a separate script file. This way, we won't need to write the same SQL statements as string literals in the MQL5 source code. Although this solution is functional, there are some aspects of it that we can and should improve.

Although many people believe that we can easily embed SQL code into other code, this is usually not the case. The reason is that the SQL code is included in the executable as a string. Embedding SQL code as a string, while not a problem for small snippets, can ultimately cause us quite a few headaches.

The article presents CPdfBuffer and CPdfWriter, a minimal MQL5 pair that derives all offsets from the output buffer instead of hand-maintaining numbers. CPdfWriter records object starts, writes a fixed-width cross-reference table and trailer, and ensures structural consistency. The result passes Part 2's validator, and a ready-to-use script converts arbitrary text to a paginated PDF.

A published daily history of option-implied variance is combined with a broker symbol's daily bars to fit a HAR model of realized volatility in MQL5; the implied term enters the regression as a predictor, not as a ready-made forecast. Out-of-sample tests on four funds paired with their Cboe indices, ten US equities, and two broker symbols show where it improves the forecast. A background service refits on the broker's bars and draws a five-session price range on the chart, and a self-test matches independent C++ and Python implementations.