Insights on trading automation and Traidies.
Reset and read MQL5 errors, validate orders with OrderCheck, implement retries, and use structured logging to protect automated trading.
Step-by-step MQL5 backtest debugging: check tester settings, validate history, fix code and order logic, then re-test for reliable results.
Design maintainable, scalable MQL5 trading systems by splitting logic into signal, risk and execution modules using OOP.
Retrieve, validate, and structure MQL5 tick and bar data, parse external JSON feeds, and prepare clean inputs for reliable trading strategies.
How to exchange market data and predictions between TensorFlow and MQL5 via REST, sockets, or ONNX with proper sync and error handling.
Connect MT5 EAs to external APIs with WebRequest, JSON parsing, secure authentication, caching, and Python bridges.
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.