History of MQL5.community development

Integer differentiation forces a binary choice between stationarity and memory: returns (d=1) are stationary but discard all price-level information; raw prices (d=0) preserve memory but violate ML stationarity assumptions. We implement the fixed-width fractional differentiation (FFD) method from AFML Chapter 5, covering get_weights_ffd (iterative recurrence with threshold cutoff), frac_diff_ffd (bounded dot product per bar), and fracdiff_optimal (binary search for minimum stationary d*).

This article presents a Time-of-Day capital rotation engine for MQL5 that allocates risk by trading session instead of using uniform exposure. We detail session budgets within a daily risk cap, dynamic lot sizing from remaining session risk, and automatic daily resets. Execution uses session-specific breakout and fade logic with ATR-based volatility confirmation. Readers gain a practical template to deploy capital where session conditions are statistically strongest while keeping exposure controlled throughout the day.

Tree-based classifiers are typically overconfident: true win rates near 0.55 appear as 0.65–0.80 and inflate position sizes and Kelly fractions. This article presents afml.calibration and CalibratorCV, which generate out-of-fold predictions via PurgedKFold and fit isotonic regression or Platt scaling. We define Brier score, ECE, and MCE, and show diagnostics that trace miscalibration into position sizes, realized P&L, and CPCV path Sharpe distributions to support leakage-free, correctly sized trading.

The article contains a detailed description of the cross-rate calculation algorithm, a visualization of the imbalance matrix, and recommendations for optimally setting the MinDiscrepancy and MaxRisk parameters for efficient trading. The system automatically calculates the "fair value" of each currency pair using cross rates, generating buy signals in case of negative deviations and sell signals in case of positive ones.

We continue studying the chaotic optimization algorithm. The second part of the article deals with the practical aspects of the algorithm implementation, its testing and conclusions.

This is an improved chaotic optimization algorithm (COA) that combines the effects of chaos with adaptive search mechanisms. The algorithm uses a set of chaotic maps and inertial components to explore the search space. The article reveals the theoretical foundations of chaotic methods of financial optimization.

This article demonstrates how to run DOOM inside MetaTrader 5 by integrating a native Windows DLL with an MQL5 Expert Advisor. We cover building the DLL, real-time framebuffer rendering via ResourceCreate, keyboard input with a key-up workaround using GetAsyncKeyState, and running the game loop on a background thread. The techniques are directly applicable to custom visualization, external data bridges, and robust MQL5–native code integration.