MQL5 Programs Market

The classic Market Profile from forty years ago is still replicated in dozens of indicators that differ only in the color of their bars. In this article, I examine three conceptual blind spots in the original theory — the monolithic Value Area in bimodal distributions, TPO’s blindness to the aggressor, and the lack of memory between sessions — and build an indicator that addresses each of them: bimodality detection with a dead zone, order flow via CopyTicksRange with absorption detection, and composite market memory using Naked POC and HVN/LVN. The complete source code is included.

This part turns stored account references into a financial monitoring engine for MetaTrader 5. It calculates daily drawdown from the confirmed session balance, overall drawdown from the initial challenge balance with a static equity floor, and profit‑target progress from realized balance. The EA derives usage, remaining allowance, and status for each rule, then aggregates them into a single guard state where risk conditions override target completion.

Build and deploy a conservative, offline‑trained policy entirely in MQL5. Logged trades become a fixed dataset for a two‑layer Q‑network trained with Conservative Q‑Learning, which pushes down Q‑values for poorly supported actions to limit extrapolation error. The EA loads the frozen weights and applies an out‑of‑distribution guard using saved feature statistics. This yields reproducible training and execution without live exploration.

The article implements two swing setups in MQL5: Anti, which enters on the first 3/10 oscillator pullback after a signal line zero confirmation with price confirmation, and Holy Grail, which enters on the first pullback to the 20 EMA in a strong rising ADX trend. A single EA runs two independent state machines with precise entry and exit rules, ATR-based stops, and state recovery. You get runnable code and traceable logging.

The article guides you through building a real‑time Hilbert Spectrum indicator in MQL5: EMD produces IMFs, a discrete FIR Hilbert transform yields analytical signals, and a CCanvas renderer draws a time–frequency–energy heatmap. Implementation details include extrema detection, SD stopping, phase unwrapping, Nyquist clipping, and bin accumulation. Use the panel to identify active cycle bands and regime shifts while keeping trade rules separate.