SSRScientific and Social Research2661-43322981-9946Bio-Byword Scientific Publishing Pty. Ltd.10.26689/ssr.v8i3.14479ArticleResearch on Silver Futures Trading Strategy Based on Support Vector Machinehttps://artdesignp.com/journal/SSR/8/3/10.26689/ssr.v8i3.14479LiQingxian,ZhangChuan2026832026-04-15With the continuous development of financial markets, silver futures trading has become increasingly significant in the investment sector. In recent years, the application of machine learning techniques in finance has provided novel perspectives for futures trading. This paper focuses on silver futures and proposes an effective trading strategy framework based on machine learning. The research proceeds through the following steps: Background Analysis, Model Selection, Model Training and Evaluation, and Strategy Implementation. Experimental results demonstrate that machine learning-based strategies can significantly enhance trading returns while mitigating risks. This research provides a reference framework for developing strategies in other financial derivatives markets.

Ma G, Bouri E, Xu Y, et al., 2025, The “Night Effect” of Intraday Trading: Evidence from Chinese Gold and Silver Futures Markets. Global Finance Journal, 2025(64): 101084–101084.

Parente M, Rizzuti L, 2026, Trading Strategy for Bitcoin and Ethereum by Neural Network Model. Soft Computing, prepublish, 1–12.

Chu K, 2026, Research on Artificial Intelligence Driven Quantitative Investment and Risk Control for Complex Markets. China Strategic Emerging Industries, 2026(3): 95–97.

Wang MZ, 2025, Application Research on Quantitative Investment Strategy Formulation and Dynamic Adjustment under the Background of Machine Learning. Computer Knowledge and Technology, 21(36): 111–113.

Lin WC, Hong YM, Wang C, et al., 2025, Reliability Evaluation of Neural Networks in Financial Forecasting: An Analysis Framework Based on Model Stability. Journal of Econometrics, preprint, 1–21. https://link.cnki.net/urlid/10.1738.F.20250625.1320.008

Yang JT, 2025, Research on Financial Market Prediction Based on Deep Learning Image Recognition Technology, thesis, University of Science and Technology of China.

Guo J, Shum HY, 2025, Large Investment Model. Frontiers of Information Technology & Electronic Engineering, 26(10): 1771–1792.

Bustarviejo J, Calzón B C, 2026, Multimodal Information Fusion for Financial Forecasting via Cross-attention and Calibrated Uncertainty. Machine Learning with Applications,2026(23): 100840.

Liang JH, Liu DN, Shi LJ, et al., 2026, Research on the Design of Flexible Capacity Market Mechanism and Optimization of Trading Strategies for New Entities Participating. Power Grid Technology, preprint, 1–18. https://doi.org/10.13335/j.1000-3673.pst.2025.1497

Xu SJ, Liu XY, Li YS, 2026, Research on Behavioral Asset Pricing Mechanism and Market Temptation under Expected Feedback Mode. Journal of Management Engineering, preprint, 1–14. https://doi.org/10.13587/j.cnki.jieem.2026.04.009

Xie M, Lu Y, Wei D, et al., 2026, Bilateral Negotiation and Trading Strategy between New Energy and Users Based on Dual Interactive Active Game Theory. Power System Automation, preprint, 1–25, https://link.cnki.net/urlid/32.1180.TP.20260116.1433.002

Chen XY, 2025, A Review of Data Prediction Methods of Artificial Intelligence in the Financial Field. Computer Science, 52(12): 271–284.

Wang Q, 2025, Research on Predicting Customer Churn in Banks under the Background of Financial Technology: Based on the DNRM-CSO Model, thesis, Central South University of Forestry and Technology.

Nahar SA, Iqra K, Farah N, et al., 2023, Volatility Forecasting of Crude Oil, Gold, and Silver Futures: A Case of Pakistan Mercantile Exchange. WSEAS Transactions on Business and Economics, 2023(20): 2276–2288.

He M, Huang D, Hou X, et al., 2026, The Motivations and Influencing Factors of Share Pledging: An Analysis of Pledging and Trading Strategies. Research in International Business and Finance, 2026(84): 103265.