How to Build a Grain Price Index for Futures Trading With Gro

09 March 2020

Users of the Gro Intelligence API are able to construct a grain price index based on DTN’s data from locations across the US, a valuable resource for financial traders predicting trends in futures prices. To add even more value, users can combine the DTN information with other data in Gro to crop-weight the index, giving greater weight to high-producing areas.

Gro’s API can distill DTN’s extensive network of local grain prices, which includes data from over 4,000 elevators and processing facilities, into a single daily price. Indexes can be created for either cash prices, which are based on physical commodity transactions, or basis prices, the difference between cash and futures prices. Either can be used to track localized supply and demand conditions in real time.

Crop-weighted indexes can be valuable to users interested in national market trends. The methodology can also be used for any set of counties, to allow grain handlers, for example, to build their own custom crop-weighted price series to identify regional price disparities.

With these indexes in hand, financial traders can build models that may predict trends in futures prices. For example, if the weighted average basis price begins to fall in the country’s biggest producing areas ahead of harvest, it may be an early signal that the crop will come in larger than the market anticipates.

The chart shows production-weighted corn basis price indexes, calculated using Gro’s API. The blue line is an average of all yellow corn price reporting facilities in US Corn Belt states. The green line is an average of all yellow corn reporting facilities in Iowa. Both indexes are weighted using county-level production data reported by USDA NASS.

To construct a national, crop-weighted basis price index, we first calculate a simple average price for each county in the United States. For example, a soybean price index contains daily price data from over 1,500 locations. Gro’s platform is uniquely built so that users can pull the prices for every DTN station in a given county with a single database call.

Then, using county-level production data reported by USDA’s National Agricultural Statistics Service, we compute each county’s share of total US production. Finally, we create a national weighted average price by applying the county weights to the daily price series. Like all of Gro’s crop-weighted indicators, this exercise can be applied to any set of custom regions in the US.

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