Vladislav Boyko / Newsfeed

Stop-loss and take-profit placement is usually the least-measured decision in a trading system. This Expert Advisor reads your closed history, replays M1 price between each entry and exit to measure Maximum Adverse and Favorable Excursion per trade, and splits winners from losers. From the distributions and trade efficiency it derives data-driven stop and target levels - measured from your own account, not a rule of thumb. Analysis only; it does not trade.

Symbolic Aggregate approXimation (SAX) encodes price windows as short words to enable fast, sound similarity search on history. We implement SAX in pure MQL5, including Gaussian breakpoints, PAA, and the lower-bounding MINDIST, and validate it with a test harness. An indicator applies a no-lookahead, two-stage search, summarizes forward paths in ATR units, and draws a forecast fan, explicitly indicating when the sample shows no edge.

We enhance the manual backtesting EA with real-time lot adjustment, an order module for buy/sell stops and limits, and a Trade Manager to modify TP/SL and close positions individually. The article explains control setup with CButton/CBmpButton/CEdit, logic in OnTick, and workarounds for Strategy Tester input constraints. Readers can reuse these components to speed up testing workflows and implement robust trade management.

A statistically significant backtest is not proof of a robust edge. This article presents a three-part validation battery in Python that consumes an MQL5 trade-level CSV export. A sign-randomization permutation test evaluates whether the Sortino reflects real directional skill, bootstrap BCa intervals assess metric stability, and Monte Carlo trade-order shuffling tests sequence dependence of drawdowns. The results feed a five-condition framework for deployment decisions.

The article introduces the Mapper pipeline in MQL5 by implementing the two fundamental components: CTDAMapperFilter (lens) and CTDAMapperCover (overlapping intervals). It explains three lens options—eccentricity, density, and coordinate—plus cover parameters (resolution and gain), and demonstrates how a price point cloud is reduced to one value per point and interval memberships. Readers obtain ready inputs for subsequent clustering and graph construction.

A refined MQL5 script exports trade history to a well‑formed JSON file in MQL5/Files/, reconstructing trades from deals by position ID and recovering stop loss and take profit via a two‑pass lookup that falls back closed to the originating order. It includes a dedicated JSON serializer and computes R‑multiple, pip profit, and duration. The result loads cleanly in Python, R, or Excel without custom parsing.

We implement ordinal pattern transition networks in MQL5: a Lehmer-code encoder, a directed network over ordinal price patterns, and three complexity metrics. Two indicators expose a trend-versus-range regime from time-irreversibility and an efficiency gauge from permutation entropy, with a transparent parameter sweep showing how to tune settings on FX data.

This article presents a distribution-adaptive spread monitor for MQL5 that replaces fixed thresholds with a rolling histogram of each symbol's recent spread. It explains percentile estimation from bins, a four-state GREEN/YELLOW/RED/WARMING classification, and a CCanvas dashboard rendered from real histogram data. You will get a ready workflow for per-symbol order gating and controlled alerting via arm/disarm hysteresis plus cooldown, with a verification script and clear calibration and resolution limits.