Training and Testing a Neural Network for Time-Series Prediction in MQL5

Summary

This document describes a demonstration of a backpropagation neural network (BPNN) for training and testing time-series predictions in MQL5. It says the implementation was ported from a C++ library and is presented as a coding demo rather than a real-world indicator. The library can either be embedded directly in an MQL5 program or compiled separately and linked through a header.

The accompanying file descriptions distinguish the network implementation, library interface, library module, demo indicator, and helper methods for MT4-style indicators. The instructions emphasize including either the embedded implementation or the standalone-library interface, but not both; the standalone module must be compiled before use. No model architecture details, input features, training procedure, forecast accuracy, trading rules, or out-of-sample results are given, so the material explains integration options rather than the predictive quality or trading value of the network.

Key ideas

• The example demonstrates training and testing a BPNN for time-series prediction in MQL5.
• The implementation is presented as a demo rather than a production trading indicator.
• The library can be embedded in a program or compiled and linked as a standalone module.
• Only one of the embedded implementation and standalone interface should be included.
• No forecast-performance evidence or trading rules are provided.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.