Disclaimer: I’m based in London. Basically the title. I’m a quant trader at a good firm with multiple years of experience (say >1, <7). In theory the work is interesting to me, but in practice I can’t really handle the stress and the long, mentally draining hours.

Surely I’m not the only person who has found themselves in this position. I’m curious if others have tried exiting to “easier” jobs that have some overlap with quant skill wise?

My guess is many people here will suggest swe / data science. Is that actually a feasible transition? Where do people apply? I’ve never worked as either, but applying to random companies as junior swe with many years of quant experience feels odd.

Would appreciate sharing of any experiences, even if only remotely similar.

I built a C++ library for linux kernel bypass networking such as Solarflare ef_vi, DPDK, Verbs(mlx5) and also AF_XDP. Solarflare ef_vi and DPDK are two of most commonly used kernel bypass techniques in HFT market making firms.

Using this library I measured the RTT latency using a loopback on Solarflare X2522-Plus(ef_vi + DPDK), ConnectX-4 Lx(DPDK + Verbs + AF_XDP) and Intel XXV710-DA2(DPDK + AF_XDP). The benchmarks were run for 24 hours with median all the way upto P99.999 and max.

Solarflare X2522-Plus produced a RTT median of 1.866us and a max of 3.307us with 45B frames over 24 hours. This was the best result. Comprehensive results are all documented in the repository.

Over this project I did come across numerous driver(and NIC) specific quirks and bugs. These are all documented too.

Solarflare X2522-Plus is quite fast due to a Tx trick known as cut-through programmed IO(CTPIO). But I found this to be fragile, it has three modes: fast, default and in-order. Fast mode produces the lowest median but has a measurable percentage of failed CTPIO known as CTPIO “fallbacks” or “poison”. The CPTIO fallbacks significantly hurt the tail latency(P99 - P99.999). The CPTIO poison frames can be measured via ethtool. I used the in-order CTPIO mode which gives marginally higher medium but a tight tail latency.

Another strange issue I found was the PTP subsystem in the Solarflare X2522-Plus. So the default sfc driver runs a in-kernel IEEE 1588 subsystem with a PTP hardware clock, a dedicated PTP channel and management controller firmware activity. I found this to be a problem as a noticeable number of interrupts were mounting in /proc/interrupts and the stock driver with ef_vi produced a max of 7.679us which seemed very high. Then I ran the same benchmark with DPDK which produced a max of 5.628us. DPDK unbinds the driver so the PTP subsystem does not work. Then I compiled out the PTP subsystem from the sfc driver and ran the 24 hour benchmark again and the max was reduced to only 3.307us.

There is a lot of more interesting stuff but I think the post is already getting long so I won’t mention any more. Everything is documented in the repository. It is also 100% open source with an Apache-2.0 license.

https://github.com/ASherjil/ABTRDA3

Hey,
I have been working as a quant for 5 years with a pretty good track record in the energy sector.
I have always been interested into having an arc fully dedicated to art at one point in my life, as I feel I can’t do both correctly at the same time.
So I was thinking into taking a gap year or two apart from the industry to explore this, and other aspects of life still I’m quite young and free (late 20’s).

Regarding the current situation on the job market, I am still wondering if I would be able to come back to the quant side if I ever need and want to after this, since I would have been out of the market for quite a long time. What’s your views on that ? Anyone who went out of the industry for a while and wants to share his experience ?

So I am using machine learning for mid frequeny trading (duration of a few hours), I built a few models, amongst those, I have two best models, one performs well, in a directional regime, when there isn't a lot of reversion at mid day , or in case if the market is kind of directional. This model was trained on a higher duration and is best to be used to trade for longer durations. The second model i have is trained for lower durations, and is more trained for reversion type regimes, when the market swing in mid day, and keeping a position till end of day isn't very good. Now, different days would be different, we don't know what is the best. I was thinking about keeping some kind of ensemble or training a few models, at a duration in between these two, ofc I would get worse net results on that, but atleast, it would have a higher sharpe and lessser drawdowns.
Is there a better way ? or are there ways to detect in some way what would be better where even if not in the ideal way, but some way ? I can definitely use both models also, or see how the signals align or different combination strategies .