I magine being able to know when a stock is heading up or going down in the next week and then with the remaining cash you have, you would put all of your money to invest or short that stock. After playing the stock market with the knowledge of whether or not the stock will increase or decrease in value, you might end up a millionaire!<\/p>\n\n\n\n
Unfortunately, this is impossible because no one can know the future. However, we can<\/em> make estimated guesses and informed forecasts based on the information we have in the present and the past regarding any stock. An estimated guess from past movements and patterns in stock price is called Technical Analysis<\/strong><\/a>. We can use Technical Analysis (TA<\/em>)to predict a stock\u2019s price direction, however, this is not 100% accurate. In fact, some traders criticize TA and have said that it is just as effective in predicting the future as Astrology. But there are other traders out there who swear by it and have established long successful trading careers.<\/p>\n\n\n\n In our case, the Neural Network we will be using will utilize TA to help it make informed predictions. The specific Neural Network we will implement is called a Recurrent Neural Network \u2014 LSTM<\/strong><\/a>. Previously we utilized an RNN to predict Bitcoin prices (see article below<\/em>): https:\/\/towardsdatascience.com\/predicting-bitcoin-prices-with-deep-learning-438bc3cf9a6f<\/a><\/p>\n\n\n\n In the article, we explored the usage of LSTM to predict Bitcoin prices. We delved a little bit into the background of an LSTM model and gave instructions on how to program one to predict BTC prices. However, we limited the input data to Bitcoin\u2019s own price history and did not include other variables like technical indicators such as volume or moving averages.<\/p>\n\n\n\n Register & Get Data<\/span><\/a><\/p>\n\n\n\n Since the last RNN we constructed could only take in one sequence (past closing prices) to predict the future, we wanted to see if it would be possible to add even more data to the Neural Network. Maybe these other pieces of data could enhance our price forecasts? Perhaps by adding in TA indicators to our dataset, the Neural Network might be able to make much more accurate predictions? \u2014 Which is exactly what we want to accomplish here.<\/p>\n\n\n\n In the next few sections, we will be constructing a new Recurrent Neural Network <\/em>with the capability to take in not just one piece but multiple pieces of information in the form of technical indicators<\/em> in order to forecast future prices in the stock market.<\/p>\n\n\n\n In order to use a Neural Network to predict the stock market, we will be utilizing prices from the SPDR S&P 500 (SPY)<\/em><\/strong>. This will give us a general overview of the stock market and by using an RNN we might be able to figure out which direction the market is heading.<\/p>\n\n\n\n To retrieve the right data for our Neural Network, you will need to head over to Yahoo Finance and download the prices for SPY<\/em><\/a>. We will be downloading five years worth of price history for SPY as a convenient Another option would be to use a financial data API such as EOD Historical Data<\/strong><\/a>. It is free to sign up and you\u2019ll have access to vast amounts of datasets. Disclosure: I earn a small commission from any purchases made through the link above.<\/em><\/p>\n\n\n\n After we have downloaded the price history for SPY, we can apply a Technical Analysis Python library to produce the Technical Indicator values. A more in depth look into the process from which we were able to retrieve the indicator values was covered here: https:\/\/towardsdatascience.com\/technical-indicators-on-bitcoin-using-python-c392b4a33810<\/a><\/p>\n\n\n\n The article above goes over the exact TA Python library we utilized in order to retrieve the indicator values for SPY.<\/p>\n\n\n\n Let\u2019s begin coding out our Neural Network by first importing some libraries:<\/p>\n\n\n\nMultivariable Input<\/h2>\n\n\n\n
Price History and Technical Indicators<\/h2>\n\n\n\n
Downloading Price History<\/h3>\n\n\n\n
.csv<\/code> file.<\/p>\n\n\n\nTechnical Indicators<\/h3>\n\n\n\n
Coding the Neural Network<\/h2>\n\n\n\n
Import Libraries<\/h3>\n\n\n\n