How to think about fitting and automation of my job: Think about fitting as a budget. Unlike an actual budget there is not a hard limit and you can’t 100% know where it lies but you can get a very good estimate of where it roughly lies from testing OOS and comparing the drop off in performance. The more data you have the larger the budget. Every search method delivers a different level of reward (measured by OOS improvement in X metrics- ie R2) for a usage of your fitting budget. A cheap LLM with a bad framework for example might give a bad return, an expensive LLM prompted well gives a solid return and the best still remains the human mind. Despite what people think, you can mass scale the human mind and this is what worldquant does (see WQ BRAIN) and why they have thousands of consultants in 3rd world countries who see the reward of *up to 120k* as an enormous sum of money (it’s nothing to scoff at for the average joe but given quant talent market rates it’s a great deal for WQ cost wise) and many researchers cranking out alphas on top of that. This is how they have millions of alphas. Other firms have this setup too - 2sigma being one. Basically this is mass search using the currents best value for budget (which is human capital). Value for budget is not always the entire equation. It’s also about what you can compute. Human compute is expensive and finding high quality humans is hard. Alpha in itself. For MFT/LFT strategies, human approach is the optimal (except for maybe very high turnover MFT strategies, where it borders on HFT but I’d say that’s more hybrid). This is because the budget is small so the number of alphas you can search is within the bound of what’s reasonable for a human. For HFT you often hit limits where even computing the alphas you come up with can get extremely expensive due to the huge amount of data. Fitting becomes less of an issue and we cannot really hope to achieve much success manually searching the space because assuming we can afford a lot of servers to let the LLMs test to their hearts contents we will be outcompeted by the LLMs despite producing better alphas on avg just through sheer volume. With LLMs now existing, instead of needing to brute force solutions as we did in the past, we are now seeing the non-human side of things get very competitive and is now one of the go-to solutions for research on the HFT side (not near enough for MFT yet, not as long as humans create better alphas than ai on average). Brute force search was already a big thing in HFT before LLMs but because they were less efficient than LLMs the need for compute to find a given level of alpha was much higher and hence much more expensive and unreachable. It was also less flexible and limited to the transforms given- not to mention there are still *some* overfitting risks in HFT. It’s much less significant but still you can hit it if you try hard enough and go heavy on a local regime. Now with LLMs it’s becoming quite reachable compute cost wise for smaller firms who have 5 figs ish to spend on compute. So we will see from here what drives edge and I think it’ll be framework and such. I will caveat this with the one weakness of LLMs, creativity. Human and machine are orthogonal in their discovery in many ways so optimal is likely both. Both search different spaces in my experience as someone who spends a lot of time doing this.

Trade count is fairly indistinguishable as a feature from volume I find. Very little information that’s orthogonal from this that you can’t get from volume. You can do more interesting transforms like only volume from round retail sizes (10, 20, 50, 100 USD etc) but this is a different thing.

It follows an auto regression process - you can fit a recursive AR (X) model to forecast funding out. It’s fairly easy and beats an EWMA of funding. It’s not the biggest of questions to answer in quant finance but nonetheless it’s an easy incremental boost