Deep Learning: Neural Networks BitCoin Predictor
This repo will compare two different deep learning recurrent neural networks to model bitcoin closing prices. Specifically, this GitHub repository highlights 2 Python Jupyter notebooks, for building and evaluating the two deep learning models.
LSTM Model
RNN, or Recurrent Neural networks provide an analysis typically based on modelling sequence data, thanks to their sequential memory. LSTM (Long Short-Term Memory) RNNs are one solution for longer time windows. An LSTM RNN works like an original RNN, but it selects which types of longer-term events are worth remembering, and which can be discarded.
FNG Model
This 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. They use the FNG index values and simple closing prices to determine if the FNG indicator provides a better signal for cryptocurrencies than the normal closing price data.
Conclusions & Analysis (LSTM VS. FNG)
This is the final visualization from this analysis. However, let us dig deeper with a few questions and answers about what this comparative analysis reveals.
Question: Which model has a lower loss?
Answer: The model for the lstm_stock_predictor_closing has a significantly lower loss.
Question: Which model tracks the actual values better over time?
Answer: The model for the lstm_stock_predictor_closing tracks the actual values better over time
Question: Which window size works best for the model?
Answer: A lower window size works much better. Specifically for the lstm_stock_predictor_closing model, setting the window_size = 2 worked well.