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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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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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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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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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Let's sum it up. We have created a system that looks for arbitrage opportunities. Remember that the system does not solve all your financial problems.
We have sorted out backtesting. It works with time-based data, and even better, it allows us to see how our system would have worked in the past.
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The idea was to study a lot of historical data using an algorithm that would find repeating patterns and evaluate their performance. Sounds interesting? In reality, the implementation turned out to be not so simple.
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Building algorithmic trading systems in the MQL5 programming language has been made easier with Standard Libraries that come preloaded in MetaEditor. These modules (libraries) come with functions and variables that simplify the process of opening, validating, closing the trades, etc.
Without these dependencies, it becomes harder to write even a simple program, such as making a simple script for opening a buy position (trade).
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We accomplished the task and the hypothesis was confirmed. So, next looked at how we could improve the results of such automatic selection. It turned out that if we split the set of all single instances into a relatively small number of clusters and make sure that instances from the same cluster do not end up in it when selecting a group, then this will help not only improve the trading results of the final EA, but also reduce the time for the selection process itself.
To perform clustering, we used a ready-made library scikit-learn for Python, or, more precisely, the implementation of the K-Means algorithm. This is not the only clustering algorithm, but considering other possible ones, comparing and choosing the best one, as applied to this problem, was beyond the acceptable limits. Therefore, essentially the first algorithm that came to hand was taken, and the results obtained using it turned out to be quite good.