Insights on trading automation and Traidies.
How AI enables real-time monitoring, reduces false positives, automates audit trails, and enforces dynamic risk limits for trading bots.
Guide to fetching and preprocessing MT5 volume data, using Isolation Forest, K-Means and LSTM, and deploying models inside MQL5 EAs.
Build interactive MQL5 optimization dashboards: setup, tabs, real-time updates, top results and replay to visualize test passes.
AI-driven rebalancing replaces fixed schedules with real-time, data-rich trades that cut costs, manage risk, and improve returns.
Lagged features deliver fast, low-cost gains in short-term AI trading by capturing momentum and seasonality without look‑ahead bias.
Compare AI and manual support/resistance methods—speed, accuracy, scalability, limits, and why a hybrid approach often wins.
Compare major currencies fast with a simple currency strength meter built for forex traders using mock price movement data.
Read more →Step-by-step checklist to prepare, train, deploy, and monitor AI models in MQL5 with ONNX or Python APIs and built-in risk controls.
Step-by-step process to prepare market data, convert AI models to ONNX, and run real-time inference inside MQL5/MT5.
Calculate the right trade size for forex or stocks based on account risk, stop-loss, and pip or tick value—fast, clear, and free.
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