SOL Bearish Outlook According to Deep Learning

This post is a continuation of my ongoing efforts to fine-tune a predictive algorithm based on deep learning methods, and I am recording results in the form of ideas as future reference.

This algorithm is based on a custom CNN-LSTM implementation I have developed for multivariate financial time series forecasting using the Pytorch framework in python. If you are familiar with some of my indicators, the features I'm using are similar to the ones I use in the Lorentzian Distance Classifier script that I published recently, except they are normalized and filtered in a slightly different way. The most critical I’ve found are WT3D, CCI, ADX, and RSI.

Previous posts in this series:

As always, it is important to keep in perspective that while these predictions have the potential to be helpful, they are not guaranteed, and the cryptocurrency market, in particular, can be highly volatile. This post is not financial advice, and as with any investment decision, conducting thorough research and analysis is essential before entering a position. As in the case of any ML-based technique, it is most useful when used as a source of confluence for traditional TA.

Trade closed manually:

Total drop: 22.2 - 19.7

Closed manually due to additional volatility from CPI release on 2/14. Will be factoring in CPI and PPI dates into the next model release.

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