Cryptocurrency Price Prediction and Trading Strategies Using Support Vector Machines
This addresses the problem of volatile cryptocurrency trading for investors, but it is incremental as it applies existing methods to a new domain.
The paper tackled short-term price prediction for cryptocurrencies by developing technical indicators and testing classification methods, finding that support vector machines yielded the most profitable trading strategies, outperforming the market for Bitcoin, Ethereum, and Litecoin over 22 months.
Few assets in financial history have been as notoriously volatile as cryptocurrencies. While the long term outlook for this asset class remains unclear, we are successful in making short term price predictions for several major crypto assets. Using historical data from July 2015 to November 2019, we develop a large number of technical indicators to capture patterns in the cryptocurrency market. We then test various classification methods to forecast short-term future price movements based on these indicators. On both PPV and NPV metrics, our classifiers do well in identifying up and down market moves over the next 1 hour. Beyond evaluating classification accuracy, we also develop a strategy for translating 1-hour-ahead class predictions into trading decisions, along with a backtester that simulates trading in a realistic environment. We find that support vector machines yield the most profitable trading strategies, which outperform the market on average for Bitcoin, Ethereum and Litecoin over the past 22 months, since January 2018.