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Added Apr 21, 2020

Watch Now: Predicting Flows, Floating Storage Trends, and Covid-19 Effects with Python SDK

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The Vortexa Data Science & Analyst team delved deep into the Python SDK on the 21st April, demonstrating how the software works and showing how it can be utilised in multitude of ways, in particular with floating storage trends and predicting flows.



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Key Takeaways:


Ease of Use of the Python SDK:

  • Making the SDK more accessible to users and showing how the data is displayed in an easy to read format.
  • There are hundreds of columns available to use when retrieving cargo and vessel movements with loading and unloading activities, allowing for in-depth analysis for users.

Ease of Integration:

  • The real value of the SDK is being able to blend different data sets and identify trade-able signals in the data
  • Allows data and the user to be nimble and adaptable

Easy to Automate:

  • Once set up, there is practically no human intervention needed to run the workflows. Users can run the same notebook again in the future

Calendar Spread vs Floating Storage:

  • Main drivers of floating storage is contango between prompt futures prices vs later term future prices with the spread between costs of oil delivered now vs in the future.
  • We discovered a statistically significant causality between calendar spreads and floating storage up to 100 days ahead in the future
  • From the other side, it can be discerned that floating storage does not cause variation or influence in calendar spread.

Spot & Future Prices vs. Floating Storage:

  • Spot and Future prices roughly in line with each other in terms of floating storage, showing a strong correlation.
  • There is significant evidence for spot prices causing floating storage in the short term (up to 10 days), however no evidence of causality further out. Spot prices are linked to calendar spread in the prompt, so when we have a market shock or big supply overhang it tends to impact the short term spreads the hardest.
  • Beyond 60 days we see evidence that crude oil floating storage levels do influence spot prices with a lag of 2-3 months.


Overall, we can see that there is almost a circular feedback loop whereby a strong price signal in terms of large calendar spreads incentivizes large volumes going into storage, and as they build up it drives market sentiment and influences spot prices further down the line as a result of the additional inventory available in the market.

Additionally we can determine that floating storage may have significant predictive value were one looking to predict spot crude prices 2-3 months ahead.

The SDK Notebook is now available and publicly accessible HERE.



More useful resources you may be interested in:

Free report: How much excess crude supply can floating storage absorb?

Webinar: Floating Storage Analysis (March 26, 2020)


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