In our latest article, we explore the Conformer method for weather forecasting, adapted for improving trading models using MetaTrader 5. Through timeseries data input, the Encoder model optimizes the Actor's policy. To enhance this process, we introduce Reversible Instance Normalization (RevIN), a simple yet effective normalization technique that addresses distribution shifts by normalizing input sequences and denormalizing output sequences. This technique enhances model accuracy by aligning mean and variance, thus improving timeseries forecasting. We also delve into practical implementation in MQL5, using neural layers for normalization and denormalization, enhancing the robustness and adaptability of trading algorithms.
#MQL5 #MT5 #forecasting #RevIN

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