History of MQL5.community development

Monitoring manually drawn trendlines requires constant chart observation, which can cause important price interactions to be missed. This article develops a trendline monitoring Expert Advisor that synchronizes manually drawn trendlines with automated monitoring logic in MQL5, generating alerts when price approaches, touches, or breaks a monitored line.

Build an MQL5 Expert Advisor that automates Larry Williams Hidden Smash Day reversals. It reads confirmed signals from a custom indicator, applies context filters (Supertrend alignment and optional trading‑day rules), and manages risk with stop‑loss models based on smash‑bar structure or ATR and a fixed or risk‑based position size. The result is a reproducible framework ready for testing and extension.

GridSearchCV and RandomizedSearchCV share a fundamental limitation in financial ML: each trial is independent, so search quality does not improve with additional compute. This article integrates Optuna — using the Tree-structured Parzen Estimator — with PurgedKFold cross-validation, HyperbandPruner early stopping, and a dual-weight convention that separates training weights from evaluation weights. The result is a five-component system: an objective function with fold-level pruning, a suggestion layer that optimizes the weighting scheme jointly with model hyperparameters, a financially-calibrated pruner, a resumable SQLite-backed orchestrator, and a converter to scikit-learn cv_results_ format. The article also establishes the boundary — drawn from Timothy Masters — between statistical objectives where directed search is beneficial and financial objectives where it is harmful.

The alignment of higher-timeframe liquidity structures with lower-timeframe reversal patterns can greatly influence both the likelihood and direction of the next price movement. By integrating structural liquidity zones from higher timeframes with precise reversal confirmations on lower timeframes, traders can improve entry timing and overall trade quality. This article demonstrates how to reinforce liquidity-based trading strategies through higher-timeframe structural confirmation—and how to implement this approach effectively using MQL5.

The DUET framework offers an innovative approach to time series analysis, combining temporal and channel clustering to uncover hidden patterns in the analyzed data. This allows models to adapt to changes over time and improve forecasting quality by eliminating noise.

In part 2, we extend the news filter to protect existing positions during news events. Instead of closing trades, we temporarily remove stop-loss and take-profit levels, storing them safely in memory. When the news window ends, stops are deterministically restored, adjusted if price has already crossed the original levels, while respecting broker minimum distance rules. The result is a mechanism that preserves trade integrity without interfering with entry logic, keeping the EA in control through volatility.

This article develops a practical MQL5 indicator that identifies Hidden Smash Day bars by strict numeric criteria and optional confirmation on the following session. We cover detection routines, buffer registration, and plot configuration to place arrows at valid bars. The approach delivers stable, non-repainting signals for historical testing and real-time monitoring.

The article outlines a practical data pipeline for quantitative analysis based on Parquet storage, Hive-style partitions, and DuckDB. It details migrating selected SQLite tables to Parquet, structuring market data by source, symbol, timeframe, and date, and querying it with SQL window functions. A Golden Cross example illustrates cross‑symbol evaluation of forward returns. Accompanying Python scripts handle data download, conversion, and execution.

This article explains why standard walkforward and k-fold CV inflate results on financial data, then shows how to fix it. V-in-V enforces strict data partitions and anchored walkforward across windows, CPCV purges and embargoes leakage while aggregating path-wise performance, and CSCV measures the Probability of Backtest Overfitting. Practitioners gain a coherent framework to assess regime robustness and selection reliability.

This article applies Depth-First Search to market structure by modeling swing highs and lows as graph nodes and tracking one structural path as deeply as conditions remain valid. When a key swing is broken, the algorithm backtracks and explores an alternative branch. Readers gain a practical framework to formalize structural bias and test whether the current path aligns with targets like liquidity pools or supply and demand zones.