Application of supervised learning models in the Chinese futures market

Paper: arXiv 2303.04581

Authors: Fuquan Tang

Abstract

Based on the characteristics of the Chinese futures market, this paper builds a supervised learning model to predict the trend of futures prices and then designs a trading strategy based on the prediction results. The Precision, Recall and F1-score of the classification problem show that our model can meet the accuracy requirements for the classification of futures price movements in terms of test data. The backtest results show that our trading system has an upward trending return curve with low capital retracement.

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 5.5/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper applies standard supervised learning classification models (Precision, Recall, F1-score) to Chinese futures price movements with basic backtesting. It lacks deep mathematical innovation or extensive empirical robustness checks, positioning it as a practical, low-math application paper.

Research Flowchart

  flowchart TD
    A[Research Goal: Predict Chinese Futures Trends & Design Trading Strategy] --> B[Data Inputs: Chinese Futures Market Historical Data]
    B --> C[Methodology: Supervised Learning Classification Models]
    C --> D[Model Training & Evaluation: Accuracy, Precision, Recall, F1-Score]
    D --> E[Strategy Design & Backtesting System]
    E --> F[Outcomes: Upward Return Curve & Low Capital Retracement]