vmieres / deeplearning

This repo is about Deep Learning and Recurrent Neural Networks

DeepLearning

Deep Learning

In this repo I am using deep learning recurrent neural networks to model bitcoin closing prices. One model will use the FNG indicators to predict the closing price while the second model will use a window of closing prices to predict the nth closing price.

Preparing the data for training and testing

• For the closing price model, I used previous closing prices to try and predict the next closing price.
• For the Fear and Greed model, I used the FNG values to try and predict the closing price.
• For each model used 70% of the data for training and 30% of the data for testing.
• Applied a MinMaxScaler to the X and Y values to scale the data for the model. Lastly, reshaped the X_train and X_test values to fit the model's requirement of samples, time steps, and features. (example: X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1)))

Building and Training custom LSTM RNNs

In each Notebook, I created the same custom LSTM RNN architecture. In one notebook, I fitted the data using the FNG values. In the other notebook, I fitted the data using only closing prices.

Evaluating the performance of each model

Lastly, use the testing data to evaluate each model and compare the performance.

Use the above to answer the following:

• Which model has a lower loss? - LSTM RNN Closing Prices
• Which model tracks the actual values better over time? - LSTM RNN Closing Prices
• Which window size works best for the model? - window size = 5