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LSTM动量信号与股票投资组合构建及交易

文章 arXiv papers · 作者: Hsiang-Hui Liu et al.
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总结

NoxTrader 使用历史US股票价格和成交量数据构建特征，包括收益、周度价格和月度价格动量。该系统应用 LSTM 模型预测收益并捕捉持续的价格趋势，在执行过程中动态更新模型。预测分数用于定制回测系统中的投资组合管理；文中称预测值的相关性范围为 0.65 至 0.75。

作者报告称，筛选预测数据后，初始投资收益从 -60% 变为 325%。他们还将预测离散程度与观察到的市场数据进行比较。这些是报告的结果，但摘录没有说明测试时期、资产范围、成本、基准、风险敞口或筛选流程。因此，仅凭所提供的信息无法独立评估这些绩效主张。

核心观点

  • 该系统从 US 股票数据中构造收益、周度价格和月度价格动量特征。
  • LSTM 模型预测收益，并在交易过程中更新以适应市场趋势。
  • 该系统在定制回测框架中使用预测分数管理投资组合。
  • 文中报告了预测相关性和筛选后的大幅改善，但省略了评估稳健性所需的细节。

标签

股票美国市场动量机器学习回测投资组合构建
全文
# NoxTrader: LSTM-Based Stock Return Momentum Prediction for Quantitative Trading


# NoxTrader: LSTM-Based Stock Return Momentum Prediction for Quantitative Trading









We introduce NoxTrader, a sophisticated system designed for portfolio construction and trading execution with the primary objective of achieving profitable outcomes in the stock market, specifically aiming to generate moderate to long-term profits. The underlying learning process of NoxTrader is rooted in the assimilation of valuable insights derived from historical trading data, particularly focusing on time-series analysis due to the nature of the dataset employed. In our approach, we utilize price and volume data of US stock market for feature engineering to generate effective features, including Return Momentum, Week Price Momentum, and Month Price Momentum. We choose the Long Short-Term Memory (LSTM)model to capture continuous price trends and implement dynamic model updates during the trading execution process, enabling the model to continuously adapt to the current market trends. Notably, we have developed a comprehensive trading backtesting system - NoxTrader, which allows us to manage portfolios based on predictive scores and utilize custom evaluation metrics to conduct a thorough assessment of our trading performance. Our rigorous feature engineering and careful selection of prediction targets enable us to generate prediction data with an impressive correlation range between 0.65 and 0.75. Finally, we monitor the dispersion of our prediction data and perform a comparative analysis against actual market data. Through the use of filtering techniques, we improved the initial -60% investment return to 325%.

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。