MQL5 Programs Market

The article describes a test of the hypothesis of randomness for quotes based on a chi-square statistic derived from the frequencies of overlapping s-chains. The article shows how to define discrete states and compare observed and expected frequencies in order to detect Markov memory in price increments. This approach helps distinguish structural dependence from noise and formalize the testing of trading hypotheses.

The article discusses the projection of EURUSD daily candlesticks onto the secp256k1 elliptic curve and the extraction of 96 features (EC+TA) to predict the direction of the next candlestick in CatBoost. The price mapping to the curve and the training pipeline on 2,000 D1 bars are shown; the full model achieves a test AUC of 0.6508, with EC features contributing 60.6%. The materials are suitable for reproduction in Python/MetaTrader 5.

This article presents an exception-like model for MQL5 that prevents VM faults through defensive guards and cooperative TRY/CATCH/FINALLY macros. It introduces structured error objects (CException), result propagation (CResult), manual stack tracing, and integrated logging for clear diagnostics. Readers learn how to validate risky operations, propagate failures predictably, and keep EAs stable when facing zero‑divide, null access, invalid handles, and array‑bounds conditions.

A compact MetaTrader 5 library builds a synthetic market from agents: a minority game, a zero‑intelligence double auction, a stylized‑facts analyzer, and a parameter scanner. The result is a testbed with known answers, providing the control real price backtests lack. Each result is cross‑checked by the model's phase transition, a matching Python implementation, and detection of a planted edge.

This article presents an MQL5 trade-management engine that converts exit rules into proposal generators. A policy engine ranks all proposals by a configurable reason priority with documented tie-breakers. A broker-aware validator checks the candidates in that order and falls back to the next one on rejection, and an execution adapter sends the first accepted request. Reconciliation confirms the result on the position before the internal state changes. The engine uses R-multiples for thresholds, persists intratrade state, and records a decision trace for each managed position on every tick.

In this article, we took a detailed look at the integration of the SSAM module into the SEW-ResNeXt block, demonstrating how the S3CE-Net framework enables the effective combination of spiking attention with residual blocks. This architecture ensures accurate processing of temporal and spatial data streams and high training stability. The modularity and flexibility of the components make it easier to extend the model and reuse proven methods.