用组合模型预测次日涨跌，收益比基准高20%。
Composing Ensembles of Instrument-Model Pairs for Optimizing Profitability in Algorithmic Trading
- 构建两层组合模型，整合多种仪器-模型对
- 在多资产上回测，收益比基准提升20%
- 适合量化交易研究者和算法交易开发者
金融市场具有非线性与复杂性，各类资产在买卖双方之间交易，各方力求最大化投资回报率（ROI）。由于个股新闻、公司信息、公众情绪及全球经济状况等多种因素影响，预测市场趋势极具挑战。本文提出一种针对金融工具的日度价格方向预测系统，解决短期价格变动预测难题。引入一种新型两层组合集成架构，通过网格搜索优化，用于预测次日价格将上涨或下跌。该策略在多种金融工具和时间周期上进行了回测，相比基准标准投资策略，收益提升20%。
原文摘要 · Abstract (English)
Financial markets are nonlinear with complexity, where different types of assets are traded between buyers and sellers, each having a view to maximize their Return on Investment (ROI). Forecasting market trends is a challenging task since various factors like stock-specific news, company profiles, public sentiments, and global economic conditions influence them. This paper describes a daily price directional predictive system of financial instruments, addressing the difficulty of predicting short-term price movements. This paper will introduce the development of a novel trading system methodology by proposing a two-layer Composing Ensembles architecture, optimized through grid search, to predict whether the price will rise or fall the next day. This strategy was back-tested on a wide range of financial instruments and time frames, demonstrating an improvement of 20% over the benchmark, representing a standard investment strategy.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。