Insights on trading automation and Traidies.
Build and automate a correlation matrix in MQL5 to select assets, manage risk, and implement diversification and pairs trading.
Hybrid models beat standalone deep learning for risk-adjusted trading by pairing ML signals with classical portfolio optimization.
Explains how Transformers, LSTMs and forecasting improve anomaly detection, scoring, and automated trading workflows.
How multi-objective optimization (NSGA-II, MOEA/D) balances return vs. risk for EUR/USD strategies and automates MQL5 deployment.
Low-volume backtests mislead: thin books, wide spreads and partial fills often erase profits—model slippage, order size and use tick data.
Use PCA in MQL5 to reduce correlated indicators, lower overfitting, speed optimization, and improve portfolio weighting.
Build and validate trading features: indicators, scaling, lag/rolling stats, multicollinearity checks, and anti-leak safeguards.
Compare four AI platforms that generate MQL5 code, backtest strategies, and automate indicator-based trading.
Overview of MQL5 encryption: AES, SHA-256, hybrid RSA+AES, key management and common pitfalls for securing trading scripts.
Compare MinMax, Standard, Robust, Power, MaxDiff & Quantile scaling for financial features and trading automation.