Detecting Commodity Forward Price Anomaly Using Deep Learning Autoencoder

Forward price curves play such an important role in commodity trading and risk management. These prices directly impact MtM P&L, risk measures such as VaR, which is used as a basis for trading and management decision-making.

And it’s a large dataset. Considering the number of commodities, forward months, and time snapshots, the total number of forward prices may easily climb up to tens of thousands a day. And consider historical data for analysis and VaR. It’s not uncommon for a trading firm to use hundreds of thousands of prices (and derived quantities) for daily closing.

Here comes the problem of data validation. These data comes from various sources, using various means including auto interface, excel upload and manual input, and there are many reasons you have incorrect data in your database ranging from hardware failure to human laziness. Continue Reading in LinkedIn