MQL5 Algo Trading

We've released a new video demonstrating how to significantly speed up Expert Advisor optimization in MetaTrader 5. The [Strategy Tester](https://www.metatrader5.com/en/automated-trading/strategy-tester) lets you run multiple optimization passes in parallel by utilizing all available CPU cores. Instead of testing parameters sequentially, you can distribute the workload and discover the most profitable settings much faster. For even greater performance, connect to the [MQL5 Cloud Network](https:

This article demonstrates how to turn MetaTrader 5 into a lightweight visualization runtime using the MQL5 canvas, not indicator buffers. The focus is rendering a parametric butterfly curve with a clean math-style presentation inside a draggable, resizable floating panel. Key techniques include supersampling via an offscreen high-resolution canvas and manual downsampling with per-pixel ARGB averaging for smooth anti-aliased curves. The plot adds a gradient background, axis grid with readable ti

Market Entropy Indicator logic has been formalized into an MQL5 Expert Advisor to remove manual monitoring issues such as inconsistent interpretation and late capture of compression-to-expansion transitions. The EA computes fast/slow/base Shannon entropy from up/down/flat bar states on every tick, then derives divergence, momentum, regime (trend/transition/chaotic), and compression/decompression states. Signals come from entropy crossovers with momentum and regime filters, plus compression brea

HSIC (Hilbert-Schmidt Independence Criterion) answers a core algo-trading question: do features truly relate to the target, beyond out-of-sample hope. Unlike Pearson correlation, HSIC detects both linear and non-linear dependence using kernel similarity matrices, working with scalars or high-dimensional vectors without explicit joint distribution modeling. The article implements HSIC in MQL5 with an RBF (Gaussian) kernel, including practical sigma selection via a median-distance heuristic and e

An experimental CCI-style indicator modifies the original CCI calculation, producing substantially different absolute values. As a result, fixed threshold usage typical for classical CCI becomes unreliable. To address this, dynamic levels are added using a Donchian-channel-like approach, allowing context-aware bounds that adapt to recent ranges. Multiple average types are supported for the core computation and for optional price smoothing. The smoothing layer can act as a signal filter without