We scrape the news data using webhose.io and stock price data using NSEPY (for Indian stock market). The data is labelled using information about open and close prices corresponding to stock news for that day. For the word embeddings, we used GloVe provided by Stanford University. We used stop words from NLTK to remove sentence fillers that do not change the context. Then we used TF-IDF to remove the words that provide the least information. LSTM is used 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.