returns. For example, pretty much anything you can implement in raw TensorFlow, you can also implement in Keras, likely at a fraction of the development effort. Well be exploring fully connected feedforward networks, various recurrent architectures including the Gated Recurrent Unit (GRU) and Long Short-Term Memory (lstm and even convolutional neural networks which normally find application in computer vision and image classification. In deep learning trading systems that Ive taken to market, Ive always used additional data, not just historical, regularly sampled price and volume data and transformations thereof. Deciding on an appropriate network architecture. Press h to open a hovercard with more details. How to enable cookie.
Not so fast, however, as anyone who has used deep learning in a trading application can attest, the problem is not nearly as simple as just feeding some market data to an algorithm and using the predictions to make trading decisions. Keras also plays nicely with CPUs and GPUs and can integrate with the TensorFlow, Theano and cntk backends without limiting the flexibility of those tools.
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