Scaling Deep Learning Multivariate Forecasting with no&low code

Training hundreds of DL models based on data model templates

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1/the no code step

To create a DMT, we use our no code platform to describe the model using a data source (database or csv). We select features, prediction column, lookback and lookahead horizon, and add placeholders for values that we’ll parameterize later via our no code API (e.g. symbol name). This process takes just a few minutes and results in a DMT like the one shown below (important parts highlighted):

2/the low code step

Now we are ready to scale by applying the DMT we created to as many stocks/crypto we want. The only thing we have to do is to call our nocode api, fetch our DMT by its id, handle the replacements by providing the {SYMBOL}/{FROM_DATE} and finally send for training.

Replacements JSON for placeholders
Training via API function
Training method call — we can call with any number of symbols

Wrapping up

Our platform for training and deploying deep learning models for stock and cryptocurrency forecasting has proven to be a powerful and efficient tool. By using data model templates and a low code API, we’re able to quickly and easily train and deploy hundreds of models, providing valuable forecasts for our users.



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Stock & Cryptocurrency Forecasting AI. Based on News and Options Data. Powered by Intelligible Deep Learning models.