borisbanushev/TradX

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Created about 4 years ago
Updated about 4 years ago

borisbanushev/TradX

borisbanushev

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stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

anomaliesinoptions

In this notebook we will explore a machine learning approach to find anomalies in stock options pricing.

Jupyter Notebook

predictions

Jupyter Notebook

ReducingMLcostsWithAWS

Jupyter Notebook

AWSGlue-SageMakerDebugger

Demo of using AWS Glue and SageMaker Debugger

Jupyter Notebook

CAPM_Databricks