Data science skill for physical commodity trading

Hello guys, 

I am currently interning in a medium-sized commodity trading company in a niche product. My goal is to eventually join a leading trading house and create my way to a trader position. I would really much enjoy starting by operations, as I am acquiring experience in this field. Unfortunately, though I see that those are skills highly appreciated, I don't have proper programming skills at the moment, even though I did learn the very basis of Python.

With a profile like mine, would you recommend starting a programming boot camp focussing on data science(like The Wagon) once my internship is over or not taking time off and learning on the side with open classrooms-like classes? 

Any advice will be very much appreciated, and if you have any other ideas on how to boost my profile for this career path, I would also be extremely grateful. 

Comments (20)

rudyha, what's your opinion? Comment below:

I'd get somewhat familiar with data science and machine learning regardless of what field you go down. My work has nothing to do with DS, but I find the things I learned about statistics somewhat useful. As you mentioned, you're seeing the job descriptions become more and more data-oriented, that's not going to change.

  • 1
DeltaDecay, what's your opinion? Comment below:

Data analysis is always good since you can also apply to Trading Analyst roles. Operations is great way into commodities but be careful to not get stuck in Ops since for you to move on someone has to be willing to move in and people usually do not want to move into Ops.

the main way to boost would be through showing that you are willing to work extra compared to your colleagues. Ops roles are not always filled in with the most competitive people. So standing out as an operator willing to go the extra mile helps. Try to optimise the value chain as much as possible and take the small risks that you can take in Ops roles.

Also network hard with the traders. try to make their life easier for them and keep mentioning about how you'd like to get into their desk. Like people should know that is where you want to go. 

However, at the end of the day the number of seats are limited and it also depends on your luck if you can move into a seat.

Abel Tiffauges, what's your opinion? Comment below:

Thank you for your message and this precious insight. I am a bit aware of the operation trap, and it is always good to be reminded that in this position, one has always to be on his a-game. According to this would you recommend developing the DS skills and trying from the analyst perspective? My curriculum may somewhat be in accordance with an operation starting point, but my degrees are not related to any quantitative field, do you think that intense and deep formation as one provided in a Boot Camp would compensate? 

  • 1
DeltaDecay, what's your opinion? Comment below:

Just pick up python and run with it. Implement projects in your free time. Not sure where you are based out of and what kind of ops you will be doing but for example if you're doing vessel ops you can create simple tools to read vessels and their capacity from a file to create the max cap of products that you can load based on density. this can easily be done in excel but then by using python you can add layers on top of it and get it more and more complex. You can for example analyse freight and demurrage rates to see on which trade routes you should put which vessels in your fleet. 

Trust me most ops guys have got nil programming experience and doing stuff like these will make you stand out. Also if not trading then Quants can also be a role. Also commodity companies are implementing data science teams to implement other interesting projects to drive trading decisions. Rotterdam has given a very good overview below on what such roles entail.

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Rotterdam, what's your opinion? Comment below:

I would say yes, learning the ins and outs of data science would be a good way to differentiate yourself from others. I started out on the typical route to trader - trading analyst to ops - then moved into a role trading a niche product but then slowly over time started to provide more and more data analysis to the trading group and company at large. In an organization where trading is key to how the business makes money it is a very valuable skill set to be able to crunch data while also having an intimate understanding of how the company actually makes money. Typically people are really good at either data science or trading so being able to bring those two skillsets together is very valuable. Once I gained the reputation as the guy that knew the ins and outs of data science and how that data impacted trading decisions and the companies bottom line my stock at the company has soared. I now consistently meet with the CEO, COO, head of trading etc to talk about my findings. It has gotten to the point where I have been able to pick what I want to do within the company as I have been asked a couple times to take on a trading role (but have so far declined to keep focusing on the data science). Who knows, maybe one day I will take a trading role again if it comes up but the point here is that being able to provide those data insights has opened many doors for me that otherwise would not have been options.

Also, slightly unrelated to your question but the data science stuff beats the hell out of the trader lifestyle as well. You get roughly the same comp as traders but don't have to be glued to your phone and computer at all times managing the minutiae. Data science projects tend to be much more on your own time and allow you to step away from the desk for a week here and there when you want. That is the main reason I have kept going down this path.

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Abel Tiffauges, what's your opinion? Comment below:

Thank you for your detailed answer and for sharing your experience. Your message frankly motivates me to explore more this field, and carry on on this genius interest. Would you thus recommend to, once the internship is over, step back, follow a boot camp for two months, and then explore new opportunities within the trading house with this new set of skills? Or would you advise continuing with the job or starting to look immediately for new opportunities and develop this skillset autonomously through certifications and alike schemes? 

  • 1
neink, what's your opinion? Comment below:

I was going to reply ''data science will not make you a commodity trader'' but your story is a much more positive and inspirational example. It's a very useful skill provided you love crunching numbers and can focus on that all day. You clearly add value to the company, possible more than most of the traders in it, know your shit and like what you do.

However, on the other hand, it will not make you a trader as other skills are more important. We have in our company, and I assume most other big companies, people that do what Rotterdam does. Every trader listens to them, because traders don't have the time (and personally speaking, can't be bothered) to do that.

In conclusion, you can make a career in commodities if you like it and are good at it, probably not as a trader but with the possibility to make as much.

Never discuss with idiots, first they drag you at their level, then they beat you with experience.

  • 2
Rotterdam, what's your opinion? Comment below:

I definitely agree with that. In order to leverage data science skills to become a trader you also have to have all the attributes that a typical trader does. It is just a way to set yourself apart from all the other people at the company that have that trading skill set if that is your end goal. I probably should have made that call out in my original post.

Rotterdam, what's your opinion? Comment below:

Everyone obviously learns differently but the bootcamps or anything like that have never worked for me. I just can't get myself to stay with them long enough and the skills I do learn from them I struggle to actually implement since every problem ends up having a slightly different solution.

My recommendation would be to get a job that is somehow related to trading and start from day one looking for problems or places where the companies current strategy could use improvement. For example, I started out as an analyst on a gasoline trading desk and quickly realized that if my trader had a clear and concise way to look at inventories within that pipeline system and how they compare to historical levels, with corresponding basis numbers, then he would have better insight into how a certain change in inventories might impact basis in certain markets (this required me to learn a lot of data engineering skills plus truly understand regressions and the statistics driving it). So I went into the system and pulled historical inventory levels and historical prices across the nation and started digging into the data to try and see if there was any discernible relationship between the two. Setting the end goal and then figuring out the skills needed to solve the problem was a much more efficient learning method for me. Once you have the problem that needs to be solved it is just a lot of Google and reading through textbooks to try and figure out what you need to do to solve the problem. Every project seems to end up requiring me to learn a new skill that then builds on the previous ones.

After that gig I ended up moving into the ag space and realized that if the traders had insight into certain crop specifications leading up to harvest a week or two before the rest of the market did then we would be able to monetize that in a pretty big way. So I decided to build a model to predict that specific specification that was really important to us. This required me to pull historical weather data for certain areas of the country and see if there was a relationship with the crop spec I was interested in (this sharpened those data engineering skills and required me to start learning multivariate regressions and eventually some machine learning algorithms).

The takeaway here is that it wasn't my certifications (I don't have any) that provided value. It was bringing the traders a statistically verified solution to their problems. Solve problems for people and you will be seen as valuable and the only way to be able to solve problems is to start solving them.

Abel Tiffauges, what's your opinion? Comment below:

Thank you very much for your detailed answer and for the time you offered to provide such insight. I will definitely try to look for opportunities where automatization can provide an edge. As you said, a target to reach is needed, and it is sometimes easy to forget that. There is already some precious tool implemented in my company for freight rates and P&L calculation, but I am sure that I can find other aspects that may be improved. But first, maybe I need to complete more simple projects to get back on my learning progression. Thanks again for your help. 

oil_quant, what's your opinion? Comment below:

I would not stop at data science, as raw technical ability is often wasted without a guided purpose.  You should aim for a convergence of skills.

There are very few people in commodities who really understand the market dynamics of a product, the data that is available for it, and how to creatively program things to use that data to predict market dynamics.

A market analyst who is fluent in data analytics can have 10x the impact of an equivalently knowledgeable market analyst who is stuck with buggy excel spreadsheets, unmaintainable copy and paste processes, and an artificially constrained vision of what is possible.  And they can have 100x the impact of a pure technical person who has no idea how a market works and wastes their time on doomed IT projects.

For an analyst in commodities who understands markets and programming, the pay can be highly lucrative, the work very interesting because you are exploring new ground as so few people are doing it, and the lifestyle very self-controlled as you manage your own projects.  There are not really big bureaucracies of such people, so the there is a very high degree of independence and it is easy to stand out if you are good.

itachi, what's your opinion? Comment below:

A trader in the physical trading industry hardly does data analysis on his own. He has to spend most of his time talking to people be in internally with regional and global teams or with external people(brokers, companies & other traders ). However, that being said it's not they do not do data analysis they just find it very hard to squeeze time for it. Two things happen in such case either the company has a separate research wing that takes care of research or the trading team themselves do basic research and not go into advanced studies. To delegate research to the team and ask the right questions a good trader always knows what sort of analysis he wants. This is where understanding of data science, visualisation and programming comes to use. A trader with 15 plus exp who can tear apart any research and properly delegate the research along with his normal routine job instils much more confidence than anyone else. To sum it up as you go higher up in the ladder in core trading the lesser you will do prog and data science, that being said when you and I reach that stage, things will run more on data than today and pretty much everyone will know these things. Then it might not give us an edge but not knowing it def will hurt in the long term.

  • Analyst 3+ in PE - LBOs

Lots of boot camps and programs to build skills in this area.  I know 3 ppl who have 3x their salary so I'd say go for it!

WinnerWinner, what's your opinion? Comment below:

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