akabbott0303 / deep-learning-neural-network-cryptocurrency-analysis

A comparison of Long Short-Term Memory neural network deep learning models for predicting cryptocurrency prices.

Long Short-Term Memory (LSTM) Cryptocurrency Model

A LSTM model is a recurrent neural network (RNN) used for time series data with long time frames. The LSTM-RNN predicts which values it needs to "remember" as it runs, allowing it to use data from long time periods without being overwhelmed with extraneous data.

In this repository, I have used two datasets to build two LSTM models. Both data sets contain information about Bitcoin. One data set contains historic Fear & Greed Index values and the other data set contains historic closing price data.

The Crypto Fear & Greed Index uses sentiment analysis from several data sources to score current feelings about Bitcoin and other large cryptocurrencies. From the Crypto Fear & Greed Index site:

The crypto market behaviour is very emotional. People tend to get greedy when the market is rising which results in FOMO (Fear of missing out). Also, people often sell their coins in irrational reaction of seeing red numbers. With our Fear and Greed Index, we try to save you from your own emotional overreactions. There are two simple assumptions:

• Extreme fear can be a sign that investors are too worried. That could be a buying opportunity.
• When Investors are getting too greedy, that means the market is due for a correction.

Therefore, we analyze the current sentiment of the Bitcoin market and crunch the numbers into a simple meter from 0 to 100. Zero means "Extreme Fear", while 100 means "Extreme Greed". See below for further information on our data sources.

The closing price data set is exactly what it sounds like - closing prices for Bitcoin over time. In both LSTM models, the data sets are imported into Pandas and joined together. For the FNG Predictor file, the FNG values are used as the features data. For the Closing Predictor file, the closing prices are used as the features data.

Results and Analysis

I found that the LSTM model was not a very good fit when using the FNG index as the features data. I used mean squared error (MSE) as a measurement of loss. The MSE is 0.1260603666305542 and the model doesn't really fit the data:

When the closing prices were used as the features data, the LSTM model performs well. I used mean squared error (MSE) as a measurement of loss. The MSE 0.021986134350299835, and the model appears to be a good a fit for the data:

The model using historic closing prices to predict future closing prices consistently performed the best with a lower loss value. This model was better at tracking the actual values over time. I experimented with adding an additional layer, changing the time window, and changing the number of epochs. Adding an additional layer did not seem to imrpove model performance. I ran each model with a time window of 1 through 10 to see which performed the best. A window value of 1 performed the best. Running the models with higher window values resulted in more loss and made the models less accurate over time. Adjusting the epochs from an initial value of 10 to higher numbers improved both models. I settled on 50 epochs since every jump by 10 from the initial 10 epochs brought diminshing returns in terms of MSE improvement.

Closing Thoughts

Using the FNG as a predictor of future Bitcoin prices does not seem to be a good fit for the LSTM recurrent neural network. I am curious if any machine learning model using the FNG would perform well. Perhaps the nature of public sentiment about a particular cryptocurrency doesn't lend itself to predicting future values of that cryptocurrency over time. Using past closing prices to predict future prices does seem to be a good fit for the LSTM model. Increasing the epochs only improves performance, particularly closer to the end of the time series. This makes sense because more epochs give the model more chances to process the data and learn from it.