All about MQL5 Wizard : create robots without programming. - page 13

[Sergey Golubev]

MQL5 Wizard Techniques you should know (Part 96): Using Wavelet Thresholding and LSTM Network in a Custom Money Management Class

We continue this series on different ideas and trade setups that can be explored and tested thanks to the MQL5 Wizard. In the last article where we looked into money management, in our custom class, we married the Suffix Automation Algorithm with an Autoencoder network. The model we had then could be the suitable tool for traders that like exploiting consolidating markets and where the historical "Price DNA" provides some repetitive patterns. It appeared to thrive in these conditions, provided we had a rhythm. This could then beg the question, what happens when the regime changes. For some traders that test volatile markets such as intraday forex or crypto, repeatable and dependable patterns can be rare. The primary obstacle here often seems that the market is static-erratic with high-frequency noise often disguising as prevalent trends.

For this article, we thus move from pattern recognition to signal processing. Instead of looking out for macro repeatable patterns we now attempt to come up with a surgical tool that is best suited for noisy, momentum-driven environments. We build this tool as a custom money management class that merges the algorithm Wavelet-Thresholding (adopted to de-noise log returns) and a Long Short-Term Memory (LSTM) network. We are sticking to the dual-engine approach of our recent articles as we continue with the theme of introducing more adaptability in scaling position size as we strip away static and lagging signals.

[Sergey Golubev]

MQL5 Wizard Techniques you should know (Part 97): Using Convex Hull and a miniature GRU Network in a Custom Trailing Stop Class

We resume this series on the MQL5 Wizard where we rotate our focus between entry signals, money management and trailing stops. These three domains are the main ways Expert Advisors assembled with the MQL5 Wizard, can be customized beyond the built-in classes. For the last published articles we have looked at entry signals and then money management which means that trailing stops are our focus for this article. In the last piece on trailing stops we married the Reservoir Sampling algorithm with a linear regression network to build a model whose goal was hugging a 'fair median' price. This was meant to guard open-positions from sideways chop.

This approach can work well in choppy markets but in trending markets it would be strained, marginally at least. Today therefore we venture into 'trading the median for the perimeter". We introduce a trailing stop class that is run by the Convex Hull algorithm - logic that is purposeful in defining an "outer-shell" of price action and we pair this as always with a neural network, with the choice for this article being a simplified Gated Recurrent Unit (GRU). The network serves as a momentum filter.

[Sergey Golubev]

MQL5 Wizard Techniques you should know (Part 98): Using an Unscented Kalman Filter and a Capsule Network in a Custom Signal Class

For this article, we rotate from regime classification (determinism) and return to real-time state estimation where we are introducing a custom signal class that is run by the algorithm Unscented Kalman Filter (UKF) and is paired with a Capsule Neural Network. This expands the toolkit library of what is on offer in prototyping and developing trading systems within MetaTrader and as always, is not intended as a silver bullet. This model is meant to bring to the fold trade set ups that work by stripping away market-noise without adding the lagging problems of many indicators such as the moving average.

[Sergey Golubev]

MQL5 Wizard Techniques you should know (Part 99): Using a KD-Tree and an Echo State Network in a Custom Money Management Class

As the approach to Money Management continues to evolve, the pursuit of risk-adjusted returns usually leads us to rethink how we filter market noise. In the last take on this subject, we addressed high-frequency environment challenges by engaging a variety of algorithms that have included Fenwick Trees, Wavelet Denoising, and even Suffix Automata. These algorithms run within models that were purpose-built to capture hidden micro-patterns and remove noise from short-term signal sequences. These approaches were inherently deterministic in that they leaned towards precise pattern matching within high-velocity trending markets.

[Sergey Golubev]

MQL5 Wizard Techniques you should know (Part 100): Sliding Window Median and Bidirectional LSTM for a Custom Trailing Stop

Today, we consider a different model that is powered by a Sliding Window Median aka Hampel Filter and a Bidirectional LSTM neural network. In all these articles we do not aim to give the silver bullet, but rather try to expand the MQL5 Wizard toolkit by introducing a special situation model, that we can contrast with previous models already covered. We are making the case that algorithmic trading success could be dependent on pairing the right tools to the "right market" as perceived by the trader who is using this model(s).