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

ArXiv ID: 2307.13832 “View on arXiv”

Authors: Unknown

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.

Keywords: Multi-Factor Inception Networks, Systematic Trading, Deep Learning, Sharpe Ratio Optimization, Feature Selection, Cryptocurrency

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.5/10
• Quadrant: Holy Grail
• Why: The paper uses advanced machine learning concepts like Deep Inception Networks and multi-factor models, which involve non-trivial mathematics and neural network architectures, scoring high on math complexity. It demonstrates strong empirical rigor through a detailed backtesting framework on real cryptocurrency data, employing robust validation methods like expanding-window splits, transaction cost adjustments, and volatility scaling to ensure realistic, implementable results.

  flowchart TD
    A["Research Goal<br>Effective Feature Utilization<br>in Crypto Trading"] --> B["Data Inputs<br>Price Returns + Additional Features<br>e.g., Hashrate, Google Trends"]
    B --> C["Methodology<br>Multi-Factor Inception Network MFIN<br>End-to-End Deep Learning"]
    C --> D["Computational Process<br>Deep Inception Architecture<br>Auto-feature learning & Sharpe Optimization"]
    D --> E["Key Findings<br>Higher Sharpe Ratio & Uncorrelated Strategy"]
    E --> F["Outcome<br>Consistent Returns 2022-2023<br>Outperforms Traditional Benchmarks"]