We will look at the development cycle of a trading robot: data collection, processing, sample expansion, feature engineering, model selection and training, creating a trading system via Python, and monitoring trades. Working in Python has its own advantages: speed in the field of machine learning, as well as the ability to select and generate features. Exporting models to ONNX requires exactly the same feature generation logic as in Python, which is not easy. That is why I have selected ...
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 ...
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. ...
For a few time-series, it is possible to devise a formula for the next value in the sequences basing off of previous values that appeared within it. Number walls allow this to be accomplished by preliminarily generating a ‘wall of numbers’, in the form of a matrix via what is referred to as the cross-rule. In generating this matrix, the primary goal is to establish if the sequence in question is convergent and the number wall cross rule algorithm gladly answers this question, if after a few rows ...
Adaboost, short for adaptive boosting is an ensemble machine learning model that attempts to build a strong classifier out of weak classifiers. more...
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