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.
Brief Background: 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.
The previous post 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.
Notes: - Remember that the CCI Release is tomorrow and that this model does not consider additional volatility from this particular event. - The new DTW (Dynamic Time Warping) Metric is an experimental feature geared towards assessing how reliable the model's prediction is. The closer to 0 this number is, the more accurate the prediction.
Hey Justin @jdehorty wanted to say well done - I've just stumbled across your work and can see the mental challenge creating these indicators would have been awesomely rewarding. Great job mate. Respect.
henryph24
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powerful use case of feature engineering and deep learning
CryptoInzane
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Kiluloc
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can you show the source of the future pivot indicator at the bottom?