In the Part 2 tutorial, I would like to continue the topic on stock price prediction and to endow the recurrent neural network that I have built in Part 1 with the capability of responding to multiple stocks. In order to distinguish the patterns associated with different price sequences, I use the stock symbol embedding vectors as part of the input. Dataset During the search, I found this library for querying Yahoo!...

This is a tutorial for how to build a recurrent neural network using Tensorflow to predict stock market prices. The full working code is available in github.com/aptsunny/stock-rnn. If you don’t know what is recurrent neural network or LSTM cell, feel free to check my previous post. One thing I would like to emphasize that because my motivation for writing this post is more on demonstrating how to build and train an RNN model in Tensorflow and less on solve the stock prediction problem, I didn’t try hard on improving the prediction outcomes....

(The post was originated from my talk for WiMLDS x Fintech meetup hosted by Affirm.) I believe many of you have watched or heard of the games between AlphaGo and professional Go player Lee Sedol in 2016. Lee has the highest rank of nine dan and many world championships. No doubt, he is one of the best Go players in the world, but he lost by 1-4 in this series versus AlphaGo....