Ushana Kevin Iorkumbul / Профиль

A templated CCircularBuffer class for MQL5 replaces the O(n) ArrayCopy array-shift pattern with O(1) insertion using a fixed-capacity ring buffer. The implementation is shown end to end and integrated into a rolling standard deviation indicator. Benchmarks across multiple window sizes compare both approaches and quantify the impact on real-time indicator calculations.

Parameter optimization inside MetaTrader 5's Strategy Tester routinely produces strategies that perform well in-sample and collapse on forward data. This article builds a native MQL5 Walk-Forward Efficiency scoring engine that quantifies how much of a strategy's in-sample Sharpe ratio transfers to each out-of-sample window. The distribution is rendered as a CCanvas histogram and validated against real EURUSD Daily backtest data.

Abnormal bars inflate mean and standard deviation estimates, distorting ATR, Bollinger Bands, and moving averages. We implement a native MQL5 indicator that detects such bars with the Modified Z-Score applied to four features: body, upper wick, lower wick, and tick volume. The indicator marks flagged bars on the chart and plots a composite score in a separate subwindow, helping you diagnose contamination in rolling-window indicators.

This article presents a multi‑broker CSV normalization framework. An MQL5 include file enriches exports with broker metadata. A Python module resolves schema divergences — pip conventions, symbol aliases, time offsets, commission models, and currency denomination — producing a unified canonical dataset. Comparative visualizations of slippage distributions and net‑of‑cost performance enable reliable cross‑platform strategy analysis without silent data corruption.

Implement a session-focused volume profile in MQL5: acquire ticks with CopyTicksRange(), bin prices, and compute POC, VAH, and VAL by the 70% approach. The indicator renders directly on the chart as native objects, supports fixed-width scaling for consistent geometry across timeframes, and refreshes on each new session. This provides objective reference levels without external dependencies.

MetaTrader 5 provides no native tool for visualizing intraday return patterns across time dimensions simultaneously. This article implements a custom indicator that aggregates historical bar returns into a 5×24 matrix indexed by weekday and hour of day, then renders the result as a color-interpolated heatmap inside an indicator subwindow using CCanvas. Green cells represent positive average returns, red cells negative, with color intensity encoding return magnitude.

A lightweight persistence design lets EAs retain counters, flags, and timestamps between terminal restarts. Using only MQL5, CPersistentStore writes a human-readable key=value file in MQL5/Files and serves reads from a CHashMap write-through cache via a typed API. The article analyzes O(1)/O(n) operations, partial‑write risks, and lack of locking, compares with GlobalVariables/SQLite, and provides a demo that reloads state deterministically.

Multi‑timeframe EAs that initialize every indicator handle in OnInit() pay a fixed startup cost even when most handles are never used. CIndicatorCache applies lazy loading with composite‑key lookup, reference‑counted Acquire/Release, and a deterministic FlushAll() for cleanup. Handles are created on first request and reused across ticks, reducing startup latency, avoiding repeated heap allocation, and preventing terminal resource leaks through centralized ownership.

The article implements rolling OLS regression channels in MQL5 and computes confidence and prediction bands with Student's t critical values instead of a fixed standard-deviation multiplier. It explains the leverage-driven widening at window edges, contrasts the result with Bollinger and Donchian channels, and reviews OLS assumptions on price data. A five-line rendering is documented to ensure reliable display in MetaTrader 5.

Nested if-else logic inside OnTick() creates implicit states that are hard to isolate, debug, and extend without regressions. A formal finite state machine in MQL5 uses an IState interface, a CStrategyContext mediator, and four concrete states to separate detection from behavior. A three-file include structure resolves circular dependencies and keeps declarations, definitions, and instantiation clean, making changes safer and debugging faster.

This article describes a live data export framework for MetaTrader 5 built around a decoupled, three‑layer design. The MQL5 component batches bar and tick records via a write buffer and rotates CSV files daily; a Python daemon tails the stream, renders a live dashboard, and flags anomaly thresholds. The demo indicator illustrates integration points, enabling real‑time monitoring and auditability during trading sessions.

This article presents a custom MetaTrader 5 indicator that computes a rolling annualized Sharpe ratio and plots configurable z-score significance bands based on Lo's asymptotic standard error. It uses a circular return buffer with incremental variance to keep O(1) updates. We explain the n^(-1/2) uncertainty scaling, the inflation of intervals at high Sharpe values, and how to set per-instrument annualization for correct deployment.

Fixed-weight moving averages introduce regime-insensitive lag. This work presents an adaptive scalar Kalman filter indicator in native MQL5 that estimates process noise Q from rolling return variance and measurement noise R from rolling price variance, with floor clamps for stability, and recomputes the Kalman Gain on every bar. The chart-overlay output is benchmarked against a 20-period EMA using MAE, RMSE, lag, and smoothness metrics to quantify tracking and noise suppression.

Cross-cutting concerns like logging, timing, and threshold filtering should not live inside indicator classes. We show how to apply the decorator pattern in MQL5 with a shared IIndicator interface, an owning CBaseDecorator, and concrete CLoggingDecorator, CTimingDecorator, and CThresholdFilterDecorator layers. You can stack behaviors per EA, keep computation code closed to modification, and get deterministic cleanup by deleting only the outermost decorator.

This article implements the NLMS-based Self-Adaptive Moving Average as a working MQL5 indicator. It provides the complete source code and explains the key design choices, including inline execution, uniform weight seeding, closed‑bar updates, and stability bounds, along with installation, usage, and limitations. The result is a compiled, chart‑ready SAMA_NLMS indicator and a clear basis for subsequent EA benchmarking.

Direct calls to the MQL5 History API inside analytics components create hidden terminal dependencies that make isolated testing structurally impossible. This article constructs an ITradeRepository abstraction layer with CLiveTradeRepository and CMockTradeRepository implementations, enabling the same analytics engine and equity curve panel to operate identically against live account data or a deterministic in-memory dataset. Repository injection eliminates direct API coupling, supports offline validation, and confines data source changes to a single implementation class.

The article presents a reproducible MetaTrader 5 to Python pipeline for large-scale indicator research. An MQL5 export schema captures fixed columns, including custom lag and whipsaw counters. A baseline module performs parameter-matched comparisons across symbols and timeframes, while a walk-forward module locks the InSample optimum and evaluates it on unseen data. Readers gain unbiased robustness measurements and automation that removes manual selection bias.

This article introduces the Self-Adaptive Moving Average (SAMA), an adaptive filter leveraging the Normalized Least Mean Squares (NLMS) algorithm. It explores why fixed-period averages fail, how NLMS adapts bar by bar, and the engineering protections required for production. This conceptual and mathematical foundation prepares you for the MQL5 code implementation in Part 2.

High-frequency MQL5 indicators that instantiate objects on every tick accumulate allocation overhead and timing jitter in OnCalculate(). This article constructs a generic templated object pool using a free-list index array, delivering O(1) Acquire() and Release() operations. The design includes double-release protection, strict separation of payload state from pool metadata in Reset(), and a fixed-capacity free list with no heap fallback. A dual-path custom indicator benchmark measures per-tick overhead difference using GetMicrosecondCount().

MetaTrader 5 provides rich performance data but limited structural analysis. This article shows how to export results to CSV from MQL5 and build five Python visualizations that expose cross-asset parameter consistency, the lag‑versus‑noise trade-off, walk‑forward decay, drawdown depth and duration, and intraday hour‑by‑day clusters. A unified automation module runs the full pipeline on any new export to deliver repeatable diagnostics.