I would argue the real issue was the lack of an automated system (or multiple automated systems) that would hit the kill switch if the trading activity didn’t look right.

I think attributing blame to a single place can be difficult in complex situations.

It's a set of failures in a system that all had to happen for the failure to occur.

https://en.wikipedia.org/wiki/Swiss_cheese_model

Yes definitely, one has to assume that from time to time, bugs will reach the prod servers, no amount of tests and code review can completely prevent that.

Hopefully the kill switch system is reasonably easy to code review and test :-)

But how would you even start to define something as stochastic as trading activity as “not looking right”?

I’ve had to fill out forms for new algorithms / quant strategies with questions like:

- how many orders per minute do you expect to create?

- how many orders per minute do you expect to cancel/amend?

- what’s your max per-ticker position?

- what’s your max strategy-level GMV/NMV?

Etc.

Any one of those questions can be used to set up killswitches.

[edited for formatting]

Sure, but there is always the possibility that then you shut down trading when things _arent_ broken.

There are always two error rates.

Defining behavior is great for retrospective analysis but would you really feel comfortable putting hard cuts into production based on the answers to those questions? I’m genuinely asking, because IME I wouldn’t be.

That last nine in a trading system uptime has exponentially low value unless you have customers who care quite a lot.

Seriously, suppose you have a truly awesome system making $100B per year of revenue. If you unnecessarily shut down 0.1% of the time, that’s only $100M per year lost, and an 0.1% unnecessary shutdown rate seems pretty high.

> That last nine in a trading system uptime has exponentially low value

IME that last 9 is where all the action happens

> unless you have customers who care quite a lot

All customers care about their trades. I’ve worked with these systems. You can’t treat smaller traders as less-than.

> only $100M

How far removed from the problem do you have to be to think one hundred million dollars is not going to effect anyone?

> How far removed from the problem do you have to be to think one hundred million dollars is not going to effect anyone?

If $10b is at risk, $100m is not a lot for an insurance policy.

Not all automated trading systems have customers.

A way to add limits when being clueless:

Estimate what a real human can do in a day, and use that as the limits. Verify that the system behaves ok for some time, then scale up the desired trading volume and limits, observe, scale, repeat.

But you don't do it by making a (bad) guess up front and then just leaving it at that.

"If we lose 100 million dollars in 20 minutes" seems like a good one.

spamming the market with orders for one

Define “spamming”, then? High frequency traders would probably look a lot like spammers.

There are always two error rates.

There's definitely more to this story. Why was there a fixed number of "flags" so that they needed to be reused? I wish there was a true technical explanation.

I can only think that it was some kind of fixed binary blob of 1/0 flags where all the positions had been used umpteen times over the years and nobody wanted to mess with the system to replace it with something better.

this is what stood out to me reading the story. i wonder if there was a reason why they opted for this, however half-baked.

it reads less to me like a case for devops as it does a case for better practices at every stage of development. how arrogant or willfully ignorant do you have to be to operate like this considering what’s at stake?

They probably already had a bitfield of feature flags, maybe it was a 16-bit integer and full, and someone notices "hey this one is old, we can reuse it and not have to change the datatype"

ah, yeah—hadn’t considered that!

There are multiple real root issues here. Missing manual kill switch is also one of them.