商品期货市场在全球金融市场中具有重要地位，为生产商、消费者和投资者提供了价格发现、风险管理和投资机会。商品期货价格的波动受多种因素影响，如市场供需、宏观经济、政策变化以及投资者情绪等。因此，准确预测商品期货的收益率对于制定有效的投资策略和风险管理至关重要。

本文使用线性模型和机器学习模型预测多种商品期货的收益率，采用线性模型作为基准方法，使用随机森林（RF）和 LambdaMART 模型来填补线性模型在收益率预测上的不足。为了进一步提高预测准确性，我们还将模型集成，结合多个模型的预测结果以提高整体预测表现。

为了增加实用性，本文基于收益率的预测结果，制定对应的投资策略。由于预测出的收益率是当日买入，持仓五天的收益，故本文在制定投资策略时以 T+5为基础，设置每天开仓一次，每次开仓做多预测收益率最高的 5个期货，做空预测收益率最高的 5 个期货。同时做空五日前预测收益率最高的 5 个期货，做多五日前预测收益率最高的 5 个期货。通过对比单一模型和集成模型的预测结果，我们可以更好地评估投资组合的收益，从而确定最佳的投资策略，以达到最佳的投资效果。

通过分析预测结果和各种投资策略的收益情况，我们可以对各种算法进行客观评估，并结合模型的特点和本文的数据，确定最佳的投资策略，以期为股民和相关机构提供有效的参考建议。

Commodity futures markets play an important role in the global financial market, providing price discovery, risk management, and investment opportunities for producers, consumers, and investors. The volatility of commodity futures prices is nfluenced by various factors, such as market supply and demand, macroeconomics, policy changes, and investor sentiment. Therefore, accurate prediction of commodity futures returns is crucial for formulating effective investment strategies and risk management.

This article uses linear and machine learning models to predict the returns of multiple commodity futures, using the linear model as a baseline method and employing the Random Forest (RF) and LambdaMART models to fill in the gaps of the linear model in return prediction. To further improve the prediction accuracy, we also integrate the models and combine the prediction results of multiple models to enhance the overall prediction performance.

To increase practicality, this article formulates corresponding investment strategies based on the predicted returns. Since the predicted returns are the returns obtained by buying and holding for five days, this article sets T+5 as the basis for formulating investment strategies, opening positions once a day and going long on the top 5 futures with the highest predicted returns, and going short on the top 5 futures with the highest predicted returns. At the same time, short the top 5 futures with the highest predicted returns five days before and go long on the top 5 futures with the highest predicted returns five days before. We predict the returns using single models and integrated models, and provide strategy evaluations based on investment portfolio returns, selecting the optimal investment portfolio strategy as the final result.

Combining the prediction results and the performance of corresponding investment strategies, this article provides a reasonable evaluation of different 4 algorithms, considering the characteristics of the models and the data used in this article, and proposes feasible investment strategies and targeted investment strategies. We formulate reasonable, scientific, and feasible investment strategies, providing scientific reference opinions for investors and relevant institutions.