Insights on trading automation and Traidies.
Compare manual, MT5, self-optimizing and AI tuning methods for MQL5; reduce overfitting with walk-forward testing and robust ranges.
Explore five practical ways to handle NaNs in price series—deletion, imputation, forward fill, interpolation, and model-based methods.
Algorithms adjust leverage using volatility, VaR/CVaR and dynamic position sizing to limit drawdowns and protect capital.
Turn plain-English strategy prompts into tested trading bots fast—automated code, backtests, and built-in risk rules.
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.