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In the previous article, we have thoroughly examined training via meta learner and cross-validation, as well as saving models in the ONNX format. I have also noted that machine learning models are not capable of finding patterns out of the box in disparate and contradictory data. In this case, it is very important what exactly is sent to the input and output of a neural network or any other machine learning algorithm.

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This article describes an attempt to understand

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The proposed method is based on the Encoder-Decoder architecture. It was developed to solve problems of safe control of robotic systems. It allows the generation of sequences of trajectories for multiple agents consistent with the scene. AutoBots can predict the trajectory of one ego-agent or the distribution of future trajectories for all agents in the scene. In our case, we will try to apply the proposed model to generate sequences of price movements of currency pairs consistent with market dynamics.

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The noise prediction module solves the auxiliary problem of identifying noise in the analyzed trajectories. This helps the movement prediction model better model potential spatial diversity and improves understanding of the underlying representation in movement prediction, thereby improving future predictions.
The authors of the method conducted additional experiments to empirically demonstrate the critical importance of the spatial consistency and noise prediction modules for SSWNP.

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Bill Williams has a doctorate in Psychology, this is important because some traders use markets sentiment. This is very used and can be done with a sentiment script with an IA to score the sentiment of traders feelings with web markets scrapping in a for example social web. At the end of the article, there will be an example of Sentiment Analysis of market news.

Bill Williams psychology formation helped him adapt psychological concepts to trading. He studied market from a human perspective

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Opting for MQL5's matrix and vector capabilities over alternatives like ONNX and Python has considerable benefits. Using an ONNX model would require separate model instances for each trading symbol and new models for any minor parameter changes, such as time frame adjustments. MQL5, however, offers adaptability without the need to manage numerous models for varying conditions.

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