Multi-Factor Inception: What to Do with All of These Features?

TL;DR

This paper introduces Multi-Factor Inception Networks (MFIN), an end-to-end deep learning framework that automatically learns features from cryptocurrency data to optimize trading strategies with higher Sharpe ratios, outperforming traditional methods.

Contribution

The paper presents MFIN, a novel deep learning model extending Deep Inception Networks to incorporate multiple factors for systematic cryptocurrency trading, demonstrating improved performance over traditional strategies.

Findings

MFIN achieves higher Sharpe ratios than rule-based strategies.

MFIN maintains consistent returns in 2022-2023.

MFIN learns uncorrelated, effective trading features.

Abstract

Cryptocurrency trading represents a nascent field of research, with growing adoption in industry. Aided by its decentralised nature, many metrics describing cryptocurrencies are accessible with a simple Google search and update frequently, usually at least on a daily basis. This presents a promising opportunity for data-driven systematic trading research, where limited historical data can be augmented with additional features, such as hashrate or Google Trends. However, one question naturally arises: how to effectively select and process these features? In this paper, we introduce Multi-Factor Inception Networks (MFIN), an end-to-end framework for systematic trading with multiple assets and factors. MFINs extend Deep Inception Networks (DIN) to operate in a multi-factor context. Similar to DINs, MFIN models automatically learn features from returns data and output position sizes that optimise portfolio Sharpe ratio. Compared to a range of rule-based momentum and reversion strategies, MFINs learn an uncorrelated, higher-Sharpe strategy that is not captured by traditional, hand-crafted factors. In particular, MFIN models continue to achieve consistent returns over the most recent years (2022-2023), where traditional strategies and the wider cryptocurrency market have underperformed.