PDF generation in MQL5 can work without DLLs when the format is treated as plain text…

MetaTrader 5 strategy optimization typically relies on brute-force sweeps or the built-in genetic algorithm, but GA behavior varies with implementation details. The article builds an alternative optimizer around Particle Swarm Optimization, treating EA inputs as coordinates and iteratively updating particle positions using inertia plus attraction to each particle’s best state and the best state within its social group. Because EA parameters are discrete, particle coordinates are rounded to configured steps. To avoid wasting passes on repeated parameter sets, each candidate point is hashed (CRC64 over the parameter bytes) and tracked in a binary search tree for fast “seen/not seen” checks. The PSO core is decoupled from any EA via a Functor interface returning a user-selected trading metric, then validated against benchmark functions. For parallel t... 👉 Read | Docs | @mql5dev

Many lot size calculators hardcode pip value assumptions that break on gold, indices, JPY crosses, and non-USD account currencies. A sizing routine should use the tick and volume properties the trade server publishes per symbol, then compute risk from those inputs. CalcLots() takes risk in account currency plus entry and stop prices, then returns the trade volume and the actual risk after broker constraints. Volume is always rounded down to the volume step. If requested risk is below the minimum lot, the minimum is returned, clamped_min is set, and the higher real risk is reported. The function is direction-agnostic and also provides stop distance in points and point value per 1.00 lot. The sizing math is isolated in Calc.mqh with no terminal state. It consumes a SymbolSpec struct and can be tested offline. SpecFromSymbol() is the only live-data bridge and... 👉 Read | NeuroBook | @mql5dev

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

Multi-symbol EAs that size trades from a correlation matrix often show unstable weights across adjacent rebalance windows. With N symbols and T bars, correlation estimates become dominated by sampling error when T is not much larger than N, even if market structure is unchanged. Random Matrix Theory offers a practical filter. Using the Marchenko–Pastur upper edge with Q=T/N, eigenvalues at or below lambda_max are treated as noise; only eigenvalues above the edge are retained as signal. A native MQL5 implementation can run without ALGLIB or DLLs by using a symmetric Jacobi eigendecomposition and a flat-buffer matrix class. Noise eigenvalues are replaced by their average to preserve the trace, then the cleaned matrix is reconstructed for downstream sizing. A useful runtime check is Frobenius distance between consecutive windows: denoised matrices ty... 👉 Read | VPS | @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