Commodity Data Quant Interview Guide
Commodity data quant interview guide covering data sources, revisions, seasonality, units, location, missingness, and examples.
Candidates working with inventories, production, shipping, weather, and futures data.
Commodity data is heterogeneous
Relevant data may include futures prices, inventories, production, imports, exports, weather, shipping, outages, and physical flow estimates.
Units and locations matter
Barrels, tons, MMBtu, megawatts, grades, hubs, delivery points, and time zones must be aligned before comparing series or spreads.
Concrete example
An inventory surprise can have different meaning depending on season, demand, refinery runs, imports, exports, and prior market expectations.
Revisions and timing create pitfalls
Reports may be delayed, revised, or released at specific times. Backtests must use the data available when decisions would be made.
Common mistakes
Candidates often treat vendor data as clean. Commodity data needs unit checks, timestamp checks, location checks, and missingness analysis.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.