Commodity analysts and traders are telling us that they need better oil storage data. Traditional oil storage data is often out of date, limited to a few locations, has variable accuracy and can be expensive. At Ryelore Ai, we can measure oil storage anywhere anytime using satellite imagery and our in-house AI algorithms.
Here are four ways that traders and analysts can use Ryelore Ai oil storage data for any global geography or area.
- Relationship with oil price: An obvious relation between oil storage and oil prices would be worthwhile to research further. It will be especially interesting for traders using futures and swaps on for example the S&P GSCI Crude Oil Index, as they might be able to better anticipate price movements based on changes in storage. Research could focus on how many days the oil storage numbers lead (or lag) other official numbers.
- When the correlation breaks: Understanding the correlation between the oil index and oil storage well, can prove interesting for other reasons. When the usual correlation breaks, it’s most likely the price will convert to fair value after a while. Storage numbers are not impacted by liquidity and/or market sentiment, so can prove a solid baseline for where the oil price should be. Any patterns breaking away from a usual correlation can therefore provide trading opportunities.
- Relationship with macro-economic indicators: The link between oil storage numbers and macro economic indicators for the country of origin is another route for research. Especially for heavy oil exporters, knowing how much oil there is in storage and how this is moving can prove to be predictive for macro economic conditions, and perhaps even FX.
- Stock price changes in oil value chain: Another line of research can focus on the fluctuation of stock prices for companies in the oil value chain. For example, it would be helpful to explore how fluctuations in oil storage numbers relate to changes in stock prices and/or cash flow for oil refineries, drilling companies and oil retailers.