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Comment by ilitirit | Hacker News Reader

As someone who works in this industry, the biggest issues are (besides like stuff like match-fixing and illegal internal trading/leaks):

1. Highly domain-specific legacy pricing model running on... Excel spreadsheets. These present huge performance bottle-necks and aren't easy to migrate.

2. Reliance on 3rd Party data. Did a scout just send incorrect data? Did their device disconnect? Did they rollback the wrong information? Is the source lagging behind the TV/online feed? GGs. Suspend the game and refund bets.

The tooling really isn't that important. Scalable software can be written in any software, and while some languages do make things simpler, often you can just also find a suitable paradigm (or middleware) that can provide similar advantages. Having said that, we do make extensive use of RabbitMQ (which is written in Erlang) in our environment. I suppose this is also just an example of my previous point though...

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1. I don't know what you are talking about. The major betting exchanges all support real time trading, they have market makers and are arbitraged against one another.

The only issue is with low popularity events. Noone cares about them, there is low liquidity and volume, it's hard to price them.

I think ilitirit is talking about bookmakers, not exchanges.

There are companies that do a combination of things: Real-time trading (manual, semi, auto), pricing (model-driven, or sourced), risk-management, etc. They can either provide their own prices using models, or apply prices from partners. The business is usually separated into discrete units which feed into each other, and outward to partners/customers.

Yes, exchanges support real-time trading, but not every company has all their models running in modern paradigms. It's not uncommon to find companies that run multiple VMs for the sole purpose of... running Excel calculations. Yes, this is exactly what it sounds like. A server that has a several, multi-meg Excel files in memory with external APIs that plug values into cells and read outputs. As I said, within the grand scheme of things, for sports who still run on legacy models, this is a major bottleneck. Remember, many of these companies were started in the 90s, and that's when these models were created. The people who created them are usually swallowed up by the financial industry and now come with a hefty price tag. So the question for the business is "Do we spend millions recreating these proven models and run the risk of them failing? Or do we simply just add another VM?"

At my company things have slowly started changing, but the new models (written in F#) still need a lot of work.

The bookmakers are far behind in technology but it's not critical to their business. They only price once a day and have large spread (fat margins).

I agree that the finance industry is competing for talents of quants and developers. Anyone who works in betting will find out that finance has better work conditions and better pays.

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