Discussing the article: "Neural Networks in Trading: Effective Feature Extraction for Accurate Classification (Building Objects)"
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Check out the new article: Neural Networks in Trading: Effective Feature Extraction for Accurate Classification (Building Objects).
The true value of Mantis becomes apparent when multi-channel information is fed into it. The RSI dancing in rhythm, trading volumes, moving averages, and currency-pair correlations form a complex picture. Direct concatenation of these signals leads to an explosion in the number of parameters, while separate processing loses cross-dependencies. The solution in Mantis is lightweight adapters that compress inter-channel interactions, leaving only what matters most: the strength of the connection between indicators. The model saves memory, and the trader saves time on configuration.
To gain a deeper understanding of the Mantis architecture, let's walk through its steps. The first stage is the initial convolution: a time series with d channels passes through a layer with 256 output channels, forming a dense representation. The tensor is then divided into equal parts, and channel-wise mean pooling transforms the resulting data into 32 tokens, each carrying local information.
In parallel, a differential stream is constructed: first-order differences of the input data enhance sensitivity to short-term dynamics. The resulting data then follow the same patching process.
The third and fourth streams — the statistical streams — collect the mean and standard deviation of the input data within the same 32 windows, conveying the overall backdrop of volatility and level.
These four streams pass through separate linear projection layers to align their dimensionality, then are concatenated and projected into global tokens of the specified size.
Author: Dmitriy Gizlyk