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

TL;DR

The paper presents the FutureQuant Transformer, a novel attention-based model that predicts price ranges and volatility in futures markets, leading to improved trading strategies and risk management.

Contribution

It introduces the FutureQuant Transformer, a new distribution-based model that outperforms existing methods in futures trading by capturing complex market patterns and predicting price ranges.

Findings

Achieved an average gain of 0.1193% per 30-minute trade.

Outperformed state-of-the-art models in predictive accuracy.

Enhanced risk management through distribution-based forecasting.

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.