Trading Under Uncertainty: A Distribution-Based Strategy for Futures Markets Using FutureQuant Transformer

ArXiv ID: 2505.05595 “View on arXiv”

Authors: Wenhao Guo, Yuda Wang, Zeqiao Huang, Changjiang Zhang, Shumin ma

Abstract

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.

Keywords: attention mechanisms, transformer models, limit order books, futures trading, price forecasting, Futures

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 5.5/10
  • Quadrant: Holy Grail
  • Why: The paper introduces advanced deep learning models like Transformers and quantile regression with mathematical formulations, but also reports specific backtested results (0.1193% gain) on futures data using real factors, indicating moderate empirical rigor.
  flowchart TD
    A["Research Goal: Predict<br>Futures Price Range & Volatility"] --> B["Key Inputs: LOB Data &<br>Technical Indicators RSI/ATR/Bollinger"]
    B --> C["Methodology: FutureQuant Transformer<br>using Attention Mechanisms"]
    C --> D["Computational Process:<br>Learn intricate market patterns"]
    D --> E["Key Outcomes: Range Forecasting<br>Improved Risk Management"]
    E --> F["Final Result: 0.1193% Avg Gain<br>per 30-min trade vs SOTA"]