Develop, test, and implement commodities trading strategies
Commodities are physical goods that are used in the production of other goods and economic services. Goods that are commodities exhibit common characteristics such as a lack of differentiation and fungibility.
Common commodities include:
- Agricultural products – corn, soybean, wheat
- Energy products – WTI crude oil, Brent crude oil, natural gas
- Precious metals – gold, silver, platinum
- Industrial metals – copper, aluminum, tin
- Soft commodities – coffee, cocoa, sugar
Commodities comprise a significant portion of production costs for industrial organizations. As such, companies seek to control their costs and manage financial risk by employing commodities trading strategies. Commodities are traded in the spot market or packaged as derivatives and traded over the counter or on exchanges. Managed futures funds and commodities trading advisors (CTAs) are active investors in this asset class.
A practical implementation approach involves modeling, building, and testing commodities trading strategies using data gathered from datafeeds and databases. An effective workflow enables you to:
- Set up and calibrate custom commodities derivatives pricing applications
- Build, test, and optimize custom trading strategies
- Apply machine learning techniques to enhance strategies
- Create dynamic portfolio management and asset allocation techniques
- Manage an automated commodities trading order workflow
For more information, see MATLAB® and toolboxes for finance, statistics, optimization, and trading.
Examples and How To
See also: energy trading, algorithmic trading, financial risk management