Multi-dimensional modeling to accurately predict commodity market trends

TSAI

1. Research background and market challenges

Against the backdrop of accelerating global economic integration, commodity markets, as the foundation of the real economy, have price fluctuations that directly affect the operating benefits of upstream and downstream enterprises in the industrial chain and the asset allocation decisions of investors. However, the commodity market is affected by multiple factors such as supply and demand, geopolitical conflicts, seasonal cycles, and monetary policy adjustments, presenting a high degree of complexity and uncertainty. Traditional analysis methods rely on empirical judgment and linear models, which are difficult to effectively capture market dynamics. Investors are in urgent need of scientific and quantitative analysis tools to achieve accurate predictions.

2. Technical architecture and data system of the TSAI platform

The commodity return prediction system built by the TSAI platform is centered on machine learning algorithm clusters and multi-source heterogeneous data fusion. On the technical level, the platform integrates algorithms such as random forest (RF), gradient boosting tree (GBDT), long short-term memory network (LSTM), etc., and achieves complementary advantages through an integrated learning framework; on the data level, a dynamic database covering 50+ categories such as global energy, metals, and agricultural products is built, with real-time access to:

• Fundamental data: OPEC crude oil production, LME metal inventory, CBOT agricultural product planting area, etc.
• Macroeconomic data: global GDP growth rate, CPI index, central bank interest rate decision
• Geopolitical data: the impact of the Russian-Ukrainian conflict on natural gas supply, and the dynamics of Sino-US trade policies
• Meteorological data: the impact of El Niño on crop growth

3. Model construction and empirical analysis

The platform adopts a four-step modeling process of data preprocessing - feature engineering - model training - backtesting optimization:

1. Data cleaning: remove outliers through the 3σ principle, and use multiple filling methods to handle missing data
2. Feature extraction: construct a 128-dimensional feature vector containing lagged price series, inventory consumption ratio, basis, etc.
3. Model training: use Grid Search to optimize hyperparameters, LSTM The model hidden layer is set to 3 layers, with 128 neurons
4. Backtest verification: In the out-of-sample data test from 2018 to 2024, the WTI crude oil price forecast MAE is $2.3/barrel, and the London copper forecast accuracy is 78%

4. Application value and market feedback

In the fourth quarter of 2024, the platform successfully predicted the rise in crude oil prices caused by OPEC's production cuts, prompting customers to arrange long positions in crude oil futures in advance, with an average position yield of 27%; in the field of agricultural products, based on meteorological data and supply and demand models, the Brazilian soybean production cuts were accurately predicted, and the relevant investment portfolio had a quarterly return of more than 21%, and the excess return was significantly better than the industry benchmark.