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Name: Adonay Nunes
Type: User
Bio: Research fellow at Massachusetts General Hospital (MGH) and Harvard Medical School
Location: Boston, USA
Name: Adonay Nunes
Type: User
Bio: Research fellow at Massachusetts General Hospital (MGH) and Harvard Medical School
Location: Boston, USA
Stock Fundamental Analysis using Machine Learning Classification Models
A stock trading bot that uses machine learning to make price predictions.
I scrape the news data using webhose.io and stock price data using nsepy. then I labelled the data using information about rising or fall in prices corresponding to news scraped for that day. For the word embeddings, I used GloVe provided by Stanford University. I used stop words from NLTK to remove sentence fillers that do not change the context. Then I used TF-IDF to remove the words that provide the least information. then I used LSTM to map any time-dependent relations in the data set and predicted if a user should "buy" or "sell" the share and with what confidence based on today's news events.
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.
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