Journal Article

FuturesNet: Capturing Patterns of Price Fluctuations in Domestic Futures Trading

Pan, Qingyi, Sun, Suyu, Yang, Pei, Zhang, Jingyi

2024

Overview

Futures trading analysis plays a pivotal role in the development of macroeconomic policies and corporate strategy planning. High-frequency futures data, typically presented as time series, contain valuable historical patterns. To address challenges such as non-stationary in modeling futures prices, we propose a novel architecture called FuturesNet, which uses an InceptionTime module to capture the short-term fluctuations between ask and bid orders, as well as a long-short-term-memory (LSTM) module with skip connections to capture long-term temporal dependencies. We evaluated the performance of FuturesNet using datasets numbered 50, 300, and 500 from the domestic financial market. The comprehensive experimental results show that FuturesNet outperforms other competitive baselines in most settings. Additionally, we conducted ablation studies to interpret the behaviors of FuturesNet. Our code and collected futures datasets are released.

• Publisher: MDPI AG
• Subject: Ablation / Datasets / Deep learning / Domestic markets / Economic forecasting / Electronic trading systems / Financial analysis / Financial markets / Forecasts and trends / Futures market / Futures trading / Global economy / High frequency trading / Machine learning / Modules / Neural networks / Prices / Securities markets / Statistical methods / Stochastic models / Strategic planning (Business) / Trends / Volatility