Support Vector Machines for Short-Horizon Cryptocurrency Trading

Summary

The study uses historical prices and technical indicators to classify short-term moves in Bitcoin, Ethereum, and Litecoin. It compares classification methods on their ability to identify upward and downward movements over a one-hour horizon, reporting performance on positive and negative predictive value. It then maps forecasts to trading decisions and evaluates those decisions with a backtester designed to represent trading conditions.

Support vector machines produce the most profitable strategies among the methods considered, and the reported strategies outperform the market on average for all three assets during the stated evaluation period beginning in January 2018. These findings are limited to the selected cryptocurrencies, historical sample, indicators, forecast horizon, and backtesting assumptions. The description does not provide details such as transaction-cost settings, model specifications, or risk-adjusted performance, so the reported results alone do not establish how the strategy would perform in other periods or live trading.

Key ideas

• The study builds technical indicators from cryptocurrency price history to forecast one-hour price direction.
• It evaluates classifiers using measures of positive and negative predictive value.
• Forecasts are converted into trading decisions and assessed with a trading-oriented backtester.
• Support vector machines lead the tested methods in reported strategy profitability.
• The reported market outperformance is specific to Bitcoin, Ethereum, Litecoin, and the tested historical period.

Tags

# Cryptocurrency Price Prediction and Trading Strategies Using Support Vector Machines


# Cryptocurrency Price Prediction and Trading Strategies Using Support Vector Machines









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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.