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Python Jupyter notebooks for building and evaluating deep learning models using both the FNG values and simple closing prices to determine if the FNG indicator provides a better signal for cryptocurrencies than the normal closing price data.

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benjaminweymouth/Neural_Networks_Stock_Predictor

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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.

Live Deployed Site (Full Comparative Analysis)

https://benjaminweymouth.github.io/Neural_Networks_Stock_Predictor/

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.

Live Site for LSTM Analysis

https://benjaminweymouth.github.io/Neural_Networks_Stock_Predictor/LSTMRNN/

Here are some screenshots:

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Loading the Two Datasets for Comparative Analysis image

Building and Training the LSTM / RNN

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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.

Live Site for FNG Analysis

https://benjaminweymouth.github.io/Neural_Networks_Stock_Predictor/FNG/

Screenshots for the FNG Analysis:

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Loading the Historical Prices for BitCoin

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Training the Model (with a Batch Size of 5)

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Evaluating the Model using the Test Data

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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.

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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.

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Python Jupyter notebooks for building and evaluating deep learning models using both the FNG values and simple closing prices to determine if the FNG indicator provides a better signal for cryptocurrencies than the normal closing price data.

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