Key Takeaways In MQL5 trade database design, it is important to store not only order results, but also signals, filters, risk checks, pre-order checks, and post-execution history as separate records.W […]
Key Takeaway A Data Logging EA is a design pattern for recording an EA’s decision inputs, order results, position state, errors, and test conditions in MetaTrader 5.In an MQL5 EA, testing become […]
Key Takeaway The important point in a sqlite-mql5 design is to use SQLite for EA state management, validation logs, and trade history storage, not as a replacement for trading signals.In MQL5, the mos […]
Conclusion of This Article The purpose of designing machine-learning-trading in MQL5 is to break trading decisions into verifiable features and scores, instead of relying on subjective conditional log […]
Key Takeaway The purpose of using Python in an MQL5 backtesting workflow is to make analysis, aggregation, and signal comparison easier outside MetaTrader 5, especially when those details are hard to […]
Key Takeaway When you include Python-based data analysis in MQL5 EA design, the most important point is to separate the roles of MQL5 and Python.The MQL5 EA should handle price retrieval, pre-trade ch […]
Key Takeaway The MT5 Python API is an integration method for connecting Python to the MetaTrader 5 terminal so you can retrieve price data, get symbol information, check positions, and send orders.Unl […]
Key Takeaway When designing an integration between MQL5 and Python, it is important to separate the responsibilities of the MQL5 side and the Python side instead of putting every decision inside the E […]
Conclusion The purpose of speeding up an MQL5 EA is to reduce unnecessary work inside OnTick and keep order decisions, position management, and logging stable.When improving EA performance, first revi […]
Key Takeaway MQL5 multi-thread optimization is a design topic for distributing EA parameter testing across multiple tester agents.If speed is the only goal, results can become unstable because of diff […]
