用注意力机制预测期货价格波动范围，提升交易收益与风控能力。
Trading Under Uncertainty: A Distribution-Based Strategy for Futures Markets Using FutureQuant Transformer
- 基于注意力机制的FutureQuant Transformer模型，捕捉复杂市场模式
- 每30分钟交易平均收益达0.1193%，优于现有模型
- 适合量化交易者和对冲基金，关注波动率与风险控制
在传统期货交易中，海量数据和实时订单簿（LOB）等变量使价格预测复杂化。我们提出FutureQuant Transformer模型，利用注意力机制应对这些挑战。不同于仅做点预测的传统模型，该模型擅长预测未来价格的区间与波动性，为交易策略提供更丰富的信息。其解析复杂市场模式的能力显著提升决策质量，在简单算法（结合RSI、ATR、布林带等因子）下，实现每30分钟交易平均收益0.1193%，超越现有先进模型，大幅改善风险管理，标志着期货市场预测分析的重大进展。
原文摘要 · Abstract (English)
In the complex landscape of traditional futures trading, where vast data and variables like real-time Limit Order Books (LOB) complicate price predictions, we introduce the FutureQuant Transformer model, leveraging attention mechanisms to navigate these challenges. Unlike conventional models focused on point predictions, the FutureQuant model excels in forecasting the range and volatility of future prices, thus offering richer insights for trading strategies. Its ability to parse and learn from intricate market patterns allows for enhanced decision-making, significantly improving risk management and achieving a notable average gain of 0.1193% per 30-minute trade over state-of-the-art models with a simple algorithm using factors such as RSI, ATR, and Bollinger Bands. This innovation marks a substantial leap forward in predictive analytics within the volatile domain of futures trading.
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