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QuantNet 将市场趋势迁移至股票交易策略

文章 arXiv papers · 作者: Adriano Koshiyama et al.
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总结

QuantNet 是一种用于构建跨多个股票市场交易策略的学习架构。每个市场都有自己的编码器和解码器：编码器表示本地市场数据，共享的全局模型处理跨市场信息，解码器则结合本地和全局信号，生成针对特定市场的策略。该设计旨在捕捉可迁移模式，同时让每个市场的模型适应自身特征。

论文使用涵盖 3,103 项资产、横跨 58 个股票市场的历史数据评估该方法。研究报告称，其夏普比率和卡尔玛比率均高于表现最佳的基准，以及不使用迁移学习的版本。摘要未说明评估时期、交易成本或实现细节，因此仅凭这些比较无法确定该策略在实盘或其他环境中的表现。

核心观点

  • QuantNet 将各市场专属的编码器和解码器与跨市场共享的全局模型相结合。
  • 全局组件旨在从多个市场中学习共同的交易模式。
  • 市场专属参数可使策略适应本地数据。
  • 研究报告称，相比基准和不使用迁移的版本，夏普比率和卡尔玛比率有所改善。
  • 摘要未说明成本、评估时期或实盘交易结果。

标签

股票机器学习统计学回测
全文
# QuantNet: Transferring Learning Across Systematic Trading Strategies


# QuantNet: Transferring Learning Across Systematic Trading Strategies









Systematic financial trading strategies account for over 80% of trade volume in equities and a large chunk of the foreign exchange market. In spite of the availability of data from multiple markets, current approaches in trading rely mainly on learning trading strategies per individual market. In this paper, we take a step towards developing fully end-to-end global trading strategies that leverage systematic trends to produce superior market-specific trading strategies. We introduce QuantNet: an architecture that learns market-agnostic trends and use these to learn superior market-specific trading strategies. Each market-specific model is composed of an encoder-decoder pair. The encoder transforms market-specific data into an abstract latent representation that is processed by a global model shared by all markets, while the decoder learns a market-specific trading strategy based on both local and global information from the market-specific encoder and the global model. QuantNet uses recent advances in transfer and meta-learning, where market-specific parameters are free to specialize on the problem at hand, whilst market-agnostic parameters are driven to capture signals from all markets. By integrating over idiosyncratic market data we can learn general transferable dynamics, avoiding the problem of overfitting to produce strategies with superior returns. We evaluate QuantNet on historical data across 3103 assets in 58 global equity markets. Against the top performing baseline, QuantNet yielded 51% higher Sharpe and 69% Calmar ratios. In addition we show the benefits of our approach over the non-transfer learning variant, with improvements of 15% and 41% in Sharpe and Calmar ratios. Code available in appendix.

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。