Cryptocurrency Trading-Pair Forecasting, Using Machine Learning and Deep Learning Technique
37 Pages Posted: 18 Jun 2020
Date Written: March 5, 2020
With the volume of activities associated with trading, it has become a very tedious task. The advent of the algorithmic trading has brought with it some positive change such as reduced latency and increase in liquidity in the Financial Market. The Algorithmic Trading also came with some high demands for the technological know-how and the resources to run it. This has put the retail trader in a seemingly disadvantaged position as these algo-programs are carefully guided secrets by those that have access to it.
Crypto-currency can no longer be ignored as the concept is forming the bedrock for future transactions. Although highly publicized, the concept of these smart contracts is not really known.
Looking into the future where the cryptocurrencies dominates over the traditional currencies, it has become imperative to give the individual trader/ retailer an additional tool to demystify the “black-Box” of the trading crypto-pairs with algorithmic trading strategy. The techniques employed are: Long Short-Term Memory (LSTM), Auto-regressive integrated moving average (ARIMA), Moving Average (MA), Cumulative Moving Average (CMA), and Artificial Neural Networks (ANN).
The models performance will be measured via correlation, Mean Percentage Error (MPE), Percentage Error (MAPE), Mean Square Error (RMSE) standard deviation and Sharpe ratio (for the trading models).
Keywords: Crypto-currency, Long Short-Term Memory (LSTM), Autoregressive integrated moving average (ARIMA), Moving Average (MA), Artificial Neural Networks (ANN), Mean Percentage Error (MPE), Percentage Error (MAPE), Mean Square Error (RMSE) standard deviation and Sharpe ratio.
JEL Classification: C10, G11, G15
Suggested Citation: Suggested Citation