Forecasting long-term timeseries is of specifically great importance for trading. The Transformer architecture, which was introduced in 2017, has demonstrated impressive performance in the areas of Natural Language Processing (NLP) and Computer Vision (CV). The use of Self-Attention mechanisms allows the effective capturing of dependencies over long time intervals, extracting key information from the context. Naturally, quite quickly a large number of different algorithms based on this mechanism ...
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Forecasting timeseries is one of the most important elements in building an effective trading strategy. When performing a trading operation in one direction or another, we proceed from our own vision (forecast) of the upcoming price movement. Recent advances in deep learning models, especially architecture-based Transformer models, have demonstrated significant progress in this area, offering a great potential for solving the multifaceted problems associated with long-term timeseries forecasting. ...
We stick to this format for this article, where we go over pattern by pattern for the parabolic SAR before concluding with a test run that combines multiple patterns as we did in the last articles. The parabolic SAR is computed almost independently with each new bar, since some of the parameters that go into its formula need to be adjusted, as we will see below. This trait, though, makes it very sensitive to price changes and trends in general, which in turn makes the case for its use within a custom ...
In this article we will create the zone recovery martingale forex trading strategy expert advisor (EA) in MetaQuotes Language 5 (MQL5) for MetaTrader 5 (MT5) step by step. The zone recovery martingale strategy is a common strategy that is oriented to counter loosing positions by opening opposite positions with a slightly greater trading volume that cancels the losing positions. It is basically a trend following strategy that does not care about the direction of the market, with the hope that ...
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