AgFlow forward curves differ from what is found elsewhere on the market.

Regular forward curves — based on forward contracts — depending on the trade volume of their commodity.


Typically, PRAs have improved these by relying on market analysts’ professional judgment, allowing them to fill some data gaps.


AgFlow goes even further. Our forward curves are an aggregation of cash and futures quotes that we complete with synthetic data.


First, the data is preprocessed using a simple IQR method to filter outliers

Then, quotes data are aggregated using time interpolation, and Spearman correlated futures to have the most negligible error (or incertitude) possible

Once aggregated, the data passes through a Machine Learning algorithm to

- Understand the dataset
- Create artificial data points predicted from training
- Test the datasets
- Back-test the curves every day to refit the data and improve the model


It then produces the best forward curves potential, which are perfected even further with the backtesting.

Did this answer your question?