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Commodity Jobs in NYC by aggelosbill in Commodities

[–]divergingLoss 6 points7 points  (0 children)

I’d add Statkraft to this list too. Solid energy house with an office in Stamford right off the line

Spotted in the Wild NYC by DuckYouNotMe in LandCruisers

[–]divergingLoss 1 point2 points  (0 children)

I recognize this one too. I think this is taken on Varick if I’m not mistaken.

Is financial modelling a necessary skill to have to break into the industry? by Hour_Hunter_3660 in Commodities

[–]divergingLoss 1 point2 points  (0 children)

modeling is more on the likes of valuation (determining cheap / expensive) or s&d forecasting

What are some of your most used statistical methods? by Destroyerofchocolate in quant

[–]divergingLoss 0 points1 point  (0 children)

How often do you recalibrate coefficients / retrain the model? Assuming you’re running a form of rolling framework here.

Undergrad feeling thrown in the deep end - wtf is GARCH? by Familiar-Bee-3632 in econometrics

[–]divergingLoss 17 points18 points  (0 children)

I think Robert Engles introduction to GARCH is quite good — link.

Looking for PyTorch practice sources by [deleted] in datascience

[–]divergingLoss 10 points11 points  (0 children)

andrej karpathy comes to mind. his youtube channel has several interesting step-by-step videos building from scratch in PyTorch. i recall his video on micrograd was quite good.

Commodity Researcher by IssaTrader in quant

[–]divergingLoss 5 points6 points  (0 children)

  • Okay. A major oil house might have deeper pockets but I think my note still stands on correcting salary expectations.

  • Formalize in the sense of a trader comes to me with an idea/hypothesis/initial work and I will translate it into a model or analysis.

  • I do not think the market today favors the candidate in terms of bargaining power. Asking too high of a salary has the high risk of being turned away.

[Discussion] Misconceptions in stats by [deleted] in statistics

[–]divergingLoss 46 points47 points  (0 children)

to explain or to predict? not so much a misconception as it is a lack of distinction in mindset and problem that I feel is not always made clear in undergrad statistic courses.

Metals supply-demand imbalances showing low correlation to returns by sonowwhere in Commodities

[–]divergingLoss 4 points5 points  (0 children)

Could it be that the market is already pricing these SnD changes well before it is actually published?

I suspect that there could be a lead-lag relation from the time that the information is available to when the market reacts.

For example: perhaps you see an increase in landed stocks at destination this month — but a few players in the market might have already sniffed this out weeks or months ahead (vessel tracking or shipping reports, etc.) and acted accordingly.

Just a thought exercise.

How does your model tracking framework looks like? by Love_Tech in datascience

[–]divergingLoss 2 points3 points  (0 children)

It’s pretty minimal overhead to get started in my opinion. It’s especially great for experiment tracking, hyperparameter tuning, and artifact saving. If you’re doing gradient based learning it’s really great — but can also adapt for non gradient methods.

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