Using Deep Learning to Create a Stock Trading Bot

There are so many articles on predicting stock prices, but this article provides two things to the reader, that no other article talks about:

Both of which provide important tools to the next generation of machine learning trading algorithms.

The program will consist of three main parts:

The data will be accessed via the yfinance library, in daily intervals, The data will include the opening,high,low and closing price of the asset. The data will be normalized and then reshaped to fit the neural network

The neural network will be a convolutional LSTM network that can extract the feature and also access temporal features of the dataset. This network fits the data because some of the complex patterns are not only convolutional, they are also time based.

The Neural Network will predict the daily opening and closing prices. If the opening prices is larger than the closing price, the network will short sell the stock. If the closing price is larger than the opening price, the network will buy the stock.

After training the network, I will compute the loss of the network and use this value as a a confidence interval, to determine the stop loss and take profit values. I will use requests to access the Alpaca API to make orders.

With the key concept in place let’s move to the code.

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