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MetaTrader 5 Python package

This is a discussion on MetaTrader 5 Python package within the HowToBasic forums, part of the Announcements category; We will tackle the problem of price to angle conversion from an entirely fresh perspective. This time, we will be ...

      
   
  1. #41
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    Feature Engineering With Python And MQL5 (Part III): Angle Of Price (2) Polar Coordinates

    We will tackle the problem of price to angle conversion from an entirely fresh perspective. This time, we will be using a more mathematically sophisticated and robust approach in comparison to the tools we created on our first attempt. Readers who are already familiar with polar coordinates should feel free to jump straight to the “Getting Started in MQL5” section, to see how these mathematical tools are implemented in MQL5.
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    Neural Network in Practice: Sketching a Neuron

    In the previous article Neural Network in Practice: Pseudoinverse (II), I discussed the importance of dedicated computational systems and the reasons behind their development. In this new article related to neural networks, we will delve deeper into the subject. Creating material for this stage is no simple task. Despite appearing straightforward, explaining something that often causes significant confusion can be quite challenging.

    What will we cover at this stage? In this series, I aim to demonstrate how a neural network learns. So far, we have explored how a neural network establishes correlations between different data points.
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    Neural Networks in Trading: Hierarchical Vector Transformer (HiVT)

    The HiVT method begins by representing the road scene as a collection of vectorized elements. Based on this scene representation, the model hierarchically aggregates spatiotemporal information. The road scene consists of agents and map information. For structured scene representation, vectorized elements are first extracted, including trajectory segments of road agents and lane segments from map data.
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    Feature Engineering With Python And MQL5 (Part IV): Candlestick Pattern Recognition With UMAP Regression

    Candlestick patterns are widely used across many different trading strategies and styles by most algorithmic traders in our community. However, our understanding of these patterns is limited to the candlesticks that we have uncovered, while in truth there may be many other profitable candlestick patterns we are simply not aware of yet. Due to the wealth of information covering most modern markets, it is materially challenging for traders to be confident that they are always using the most reliable candlestick patterns available to them in their chosen market.
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