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用于加密货币投资组合交易的多因子 Inception 网络

文章 arXiv papers · 作者: Tom Liu et al.
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总结

本文提出多因子 Inception 网络（MFIN），这是一种利用价格收益和其他高频更新信号交易多种加密货币的框架。为弥补加密资产历史数据有限的问题，文中提出加入网络算力和搜索热度等因子。MFIN扩展了 Deep Inception 网络，使其能够同时处理多个因子和资产。

该模型从收益中学习表征，并生成投资组合头寸规模，目标是最大化夏普比率。论文将其结果与基于规则的动量和均值回归策略进行比较，报告称，MFIN产生的策略夏普比率更高、相关性更低，也不同于传统的人工构造因子。论文还报告称，在2022–2023期间，该策略仍取得收益，而对比策略和更广泛的加密市场表现不佳。摘要未提供详细的实验设置、数值表现指标或交易成本分析，因此不足以评估其稳健性或实际交易可行性。

核心观点

  • MFIN扩展 Deep Inception 网络，使其能在一个交易框架中处理多个资产和因子。
  • 该方法的动因是加密市场指标更新频繁，而许多资产的历史数据较短。
  • 模型从收益中学习，并选择投资组合头寸规模以优化夏普比率。
  • 论文报告称，其策略与基于规则的动量和均值回归方法相关性较低。
  • 摘要称收益在2022–2023期间持续，但未提供具体数据或实施细节。

标签

加密资产机器学习动量均值回归投资组合构建
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# Multi-Factor Inception: What to Do with All of These Features?


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









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.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。