Pragmatic Deep Learning Model for Forex Forecasting. Using LSTM and TensorFlow on the GBPUSD Time Series for multi-step prediction
In an attempt to solve the classical question, “Can machine learning predict the market?”, I landed on Forex GBPUSD as a challenging financial series with an abundant and free data set. Although there are tens of stories on this platform on stock ML prediction and a handful on Forex ML prediction, here you will see me delve into the peculiarities that are often missed and aim to take my model to the reality spectrum:
At the end of the story, readers with some Python and ML experience will be able to use the concepts and modify the linked code to produce their own variation of the model. In part 2, reader will be able to use a commercial algo trading platform with the model.
The model is built in Python 3.8 using TensorFlow/Keras 2.3. To keep this story focused on concepts, the full source code and the environment preparation, along with the explanation related to running and changing the code are here:
Also, you can view the environment setup and the steps to run the model, visually explained:
Explaining the environment setup and the steps to run the model
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