It's a tough question because most of what I learned was on the job and I don't really remember books specifically on the topic. It also depends on what product and market you are trading, and on what channels (on exchange, over the counter etc.)
I would say it starts with knowing the product you trade and their dynamics inside out. How to make a price for it, what dynamics can change that price etc. It's also a good idea to understand related instruments as typically you would use them for price formation and hedging [1][2].
The second bit is to understand electronic trading infrastructure and the market microstructure such as how orderbooks work, and some trading algos [3]
If you're also interested in the infrastructure behind related to low latency, you can read about FPGAs, Exchange co-location etc. There is a fascinating blog on the network infrastructure [4] and the author (Alexandre Laumonier) also wrote a few books on that topic.
A lot of market-making is bespoke and proprietary. For example your pricing model, hedging model, etc. It's also constantly changing depending on events (e.g. the elections) or market conditions (correlations changing). But a good place to start is to use some simple pricing model such as making a blend of other prices in other venues, or for derivatives making up the price based on the underlying price and other elements which vary depending on what the derivative is.
While you would not be able to gain direct market access to, say, the NYSE, a lot of crypto platforms offer APIs for users to build trading bots as a training ground. For example Bitmex, or Deribit. But before doing this I recommend understanding the contract mechanics inside out. For example, the perpetual futures have a very particular mechanism which means they are priced and traded differently than normal futures you'd read about in the Hull book.
I have a few friends who built bots and make small markets on random cryptos for fun. In this particular case, you'd have to think a bit harder on how you hedge your positions (e.g. can you sell an illiquid coin and hedge with a liquid one for cheap?). One way of doing this may be to look at correlations between coins to find the best hedge. Another consideration for crypto is the fee structure which you'd need to account for in your price.
I don't know if it's a good idea to start doing this in crypto besides for fun though, mostly because it's a business that requires scale to derive a meaningful profit. But as an exercise it's definitely fun.
[1]Options, Futures, and Other Derivatives (J. Hull)
[2]The handbook of fixed income securities (Frank J. Fabozzi, Mc Graw-Hill Gb)
also, if it isn't maintained by the company that made it, then it is a good sign that they are no longer using it. it suggests that there is a better solution elsewhere.
As an MLE who comes from backend web dev, I have flip-flopped on notebooks. I initially felt that everything should be in a python script. But I see the utility in notebooks now.
For notebooks in an ML pipeline, I find that data issues are usually where things fail. Being able to run code "up to" a certain cell and create plots is invaluable. Creating reports by creating a data frame and displaying it as a cell is also super-handy.
You say, "dial some logic in", which is begging the wrong question (in my experience, at least). The logic in ML is usually very strait forward. It's about the data coming into your process and how your models are interacting with it.
I agree completely with this.
Papermill output is a notebook - that is the log file.
You can double click on it, it opens in 1-2 seconds and you can see visually how far your notebook progressed and any plots you added for debugging.
Underrated comment. At my place of work, I find this to be a huge part of the MLE job. Everyone knows R but none of the cloud tools have great R support.
How would that work in a public school setting? Seems like there are too many kids and not enough teachers/time to implement this across all subjects.
If you are right, and oral exams become the best way to evaluate student learning, then I could see smaller private schools becoming more popular as a correlation.
