The article shows how MQL5 operator overloading can be used to express data-structure…

This update finalizes the MT5 replay/simulation system for training by aligning simulated pricing with live-server behavior. A small fix removes hardcoded SYMBOL_DIGITS side effects, and a new config option lets users set per-symbol decimal precision so the position indicator formats prices correctly (e.g., instruments with 0.5 ticks). Database stability is improved by moving table-creation SQL into an external script embedded as a resource and adding constraints to prevent invalid or duplicate records, reducing corruption risks without extra application logic. Take Profit and Stop Loss become functional by adding a close-price check inside the position indicator and firing a custom event to the EA to close positions. The same event-driven pattern can be adapted to simulate pending orders by changing the trigger conditions and emitting the appropri... 👉 Read | Freelance | @mql5dev

PDF generation in MQL5 can work without DLLs when the format is treated as plain text plus a bottom index. A minimal PDF has five regions: header, numbered objects, xref table, trailer, and %%EOF. Only the object list grows; most of the file is boilerplate. The body is a flat set of indirect objects referenced by “N 0 R”. Pages do not embed content or fonts directly; they reference a content stream and resource objects. PDF values are limited to eight types, with names (/Helvetica) distinct from strings ((Hello)). Streams require an exact /Length byte count. A single page typically needs Catalog, Page Tree, Page, Contents stream, and Font. Correct xref byte offsets and startxref are critical; a one-byte shift breaks the file. Content streams use postfix operators (BT, Tf, Td, Tj, ET). Multi-line layouts rely on relative Td moves and can switch fonts mid-s... 👉 Read | Docs | @mql5dev

Average True Range (ATR) is derived from True Range (TR), defined as max(High-Low, abs(High-Close[1]), abs(Low-Close[1])). Initialization typically computes TR over Length bars, then uses the SMA of those values as the first ATR. RMA uses alpha=1/Length and updates as: rma = alpha*TR + (1-alpha)*prev_rma. SMA mode recalculates the simple average of TR over the last Length bars on each candle. EMA uses alpha=2/(1+Length) with: ema = alpha*TR + (1-alpha)*prev_ema. WMA applies linear weights: sum = N*TR[0] + (N-1)*TR[1] + … + 1*TR[N-1], then wma = sum / (N*(N+1)/2). Common validation setup: XAUUSD on H1, comparing RMA, EMA, SMA, and WMA outputs side by side. 👉 Read | Docs | @mql5dev

SAGDFN targets noisy, redundant market data by keeping only neighbors that measurably influence the system. After implementing Significant Neighbors Sampling in OpenCL, the focus shifts to Sparse Spatial Multi-Head Attention to extract structure from the selected links while staying compute-efficient. A key optimization avoids per-pair embedding concatenation. By splitting the first linear layer into separate query/key projections computed once per node, pair logits become simple vector additions, cutting complexity from O(NMhd) to O(Nhd)+O(NMh) and reducing memory pressure—well-suited to GPU execution in MQL5+OpenCL. For attention normalization, iterative α-Entmax is replaced with Sparse-SoftMax to keep sparsity without expensive τ searches. The OpenCL forward kernel computes head-wise sparse weights with local reductions, NaN/Inf guards, bounds-che... 👉 Read | Calendar | @mql5dev

Dragonfly Algorithm (DA), proposed by Seyedali Mirjalili in 2015, models two swarm modes: static hunting and dynamic migration. These map directly to exploitation and exploration in population-based optimization. Each agent applies five terms per iteration: separation, alignment, cohesion, attraction to the current best (Food), and repulsion from the current worst (Enemy). Velocity is updated as a weighted sum plus inertia, then position is advanced and clamped to bounds. When an agent has insufficient neighbors and Food is out of range, DA switches to Lévy flight using Mantegna sampling to generate heavy-tailed steps. Adaptive control drives convergence: neighborhood radius increases over epochs, inertia drops from 0.9 to 0.4, and s/a/c/e decay to zero mid-run, leaving Food attraction dominant. Population size is the main external parameter. 👉 Read | Signals | @mql5dev