I glanced, and I found this handbook shallow and - in some areas - even bad advice.
E.g. If I ever see a monetary value stored in something else than integers I'm going to run away screaming (thank you Rust decimals represented as JSON floats). It's always integers unless you have a VERY good reason to do otherwise (though exported view can be in anything, even in weird bitcoded formats).
FX exchange. Resolution of FX isn't a point-in-time thing, things like buyer rate-in-time, seller rate-in-time, agreement, agreement tolerance, agreed upon resolution timestamp come in the effect.
Immutability - that's why you want to have event sourcing everywhere that touches money:
# Resolved stream
A -> B -> E
# Actual stream
A0 -> Edit(A0, A) -> B -> C -> D -> Rollback(B) -> E
Though in the end Fintech != Fintech. I worked at Fintech where money was treated like a baggage, and in other where money was a central point of everything.
> E.g. If I ever see a monetary value stored in something else than integers I'm going to run away screaming (thank you Rust decimals represented as JSON floats). It's always integers unless you have a VERY good reason to do otherwise (though exported view can be in anything, even in weird bitcoded formats).
That really overstates the issue. Whole domains of finance run just fine on doubles.
If you're doing Monte Carlo options pricing over interest rate paths, and you're interested in the risk metrics, like durations, convexity, vega, and so on, no one cares what your rounding convention is. doubles are just fine, thank you. How are you going to force `exp(-rt)cashflow` to be an integer? Or the normal CDF?
Yes, there are domains where ints make sense. But it's certainly not universal, you just need to make the right engineering choice.
It can be really frustrating viewing threads like this sometimes. I've not once seen an interest rate swap priced in anything other than float/double, and that's relatively simple even compared to some of the crazy instruments out there.
Like, sure, probably don't use floats for everything, but what are the odds that your greeks are gonna be nicely expressable as simple rationals?
Quick browse over Stripe API (i.e. not a simplistic payment provider) will show exactly otherwise.
Metrics/durations/convexity/vega aren't monetary value, these are calculation values and I'm fine with them being whatever. If you're calculating averages to make decisions based on this I don't care if you're counting dolars or tomatoes.
Monetary value is $4.50 and it's representation of actual money passed through system. In practice this value is almost never undergoing any operations except for addition and subtraction. Any multiplication/divison etc. is left to specialized systems that return results wrapped in 800 pages of standarization documents, regulations and so on; systems where term "bit flip" is not a joke, but a risk.
As I wrote fintech != fintech, and I know many organizations aren't at that level of scrutiny, but a huge domain of people dealing with others' people money know that charging someone 1 cent too much is a tens of thousands of problems coming their way.
What's your preferred solution to dealing with FX conversions?
My issue with using integers everywhere is that FX conversions (or other rates) always come into play, and at that point I'm forced to use something else anyway (e.g. arbitrary precision decimals).
I never had to deal with that. At one place it was so complicated that specialized system took reconciliation over (it got baggage full of context data including exchange agreement details, with boundaries and time-regions plan).
In not-a-fintech we just went with doubles and rounded up (worst case - we get a cent more of a customer).
Though if I had to design today for that, I'd look for non-string serializable decimal, so not "10.123 == (10, 123)" but more like (10123, 3) and serialize in JSON as a value and precision separately.
Yet that's only cause I saw Decimal(10,123) sent in JSON as "10.123" which JSON reader red as 10.122 float and inserted to "1012" to database.
I worked on a trading system dealing with transaction limits expressed in USD but trading in many currencies.
FX rates and prices were expressed in fixed point because it was more efficient to handle in FPGA. A lot of thought went in to making the system accurate enough based on real FX rates while falling on the right side of the limit
I suggest reading the comment I was replying to since it contextualises the answer quite well. Hard to find absolutes in real life outside of thermodynamics.
> you're interested in the risk metrics, like durations, convexity, vega, and so on, no one cares what your rounding convention is. doubles are just fine, thank you.
Doubles are not just fine, but required there. If you compute risk, as you allude to, by finite differences, then you need the precision of floats rather than rounding to integer cents prematurely.
It doesn't matter for quant finance to use floats because approximate is perfectly fine. Tracking the exact amount to accounting standards is the job of the brokerage team not the quant devs. Floats are fine for modeling as long as the numerical drift is accounted for.
What are you referring to with the integers/floats comment? The article says clearly that the rule of thumb is not to use floats and that they’re “almost never” a good idea, that they cause unpredictable precision loss, and recommends integer or BigDecimal types in multiple places. Are you also talking about rationals? So what is the bad advice here, exactly?
For FX, it seems like you’re reinforcing what the handbook says, that there’s no canonical rate. Aside from that, it’s talking about post-resolution records and you’re talking about how to resolve, no? That’s valid nuance of a separate goal, and it’s a fine goal of yours, but doesn’t seem like a demonstration of something missing or wrong.
The article appears to make the very same point about immutability? What are you saying that’s different?
With integers/floats, he's saying it's not opinionated enough. Anything other than integers with minor-unit precision, unless you have a very good reason, is a bad idea. So "floating point is almost a bad idea" doesn't go far enough, and the other alternatives are presented somewhat equally.
The FX critique is saying that it's glossing over a lot of the complexity. I'd say the same is true for the treatment of DE ledgers, and it borders on bad advice (e.g. "Balance is never stored. It’s derived from the movements of money.")
> Anything other that integers with minor-unit precision, unless you have a very good reason, is a bad idea.
The article clearly communicates this sentiment, no? What else needs to be said? How much further does it need to go, and why?
It might be a mistake for either us or the handbook to be absolute or dogmatic about floats. It’s not a sin to mention that they exist, and it’s a fact that some people in fintech use them for some reasons that have a defensible engineering position and well considered tradeoffs. I’ve been on the side of assuming people don’t use floats for money and then been surprised when I bumped into people here on HN who report using floats in finance routinely.
BTW, is your quote “almost a bad idea” a typo? There’s a world of difference between ‘almost a bad idea’ and ‘almost always a bad idea’. The actual words in the article, if we’re quoting the article, are: “almost never a good idea” in reference to using floating point types.
> it’s glossing over a lot of the complexity.
Of course it is, that’s a good thing. It’s not pretending to be a spec or rules, it’s an introduction and general principles. The article is already introducing new complexities that people outside of fintech might not be aware of. But do we really have to mention ALL complexity? The biggest problem with Wikipedia is that it’s overrun by nuance and complexity, so much that you often can’t read an article on a topic without already being an expert on that topic. This is why experts are often bad teachers. Being unable to gloss over some complexity is not good for learning and doesn’t make a good environment for newcomers. Let’s allow people to write for non-experts and make room for learning. We don’t have to avoid glossing over some of the complexity; it’s useful to get the general direction and gist correct while leaving out some of the detail.
> it borders on bad advice
Be specific. What’s wrong? Note that contributions are invited.
"Monetary value must be stored in integers" is the much stronger statement that the article doesn't make. Obviously there are exceptions, but you're going to need a much longer side discussion in order to justify why you're using floats.
It's like saying "don't write your own crypto algorithm". Of course write your own crypto algorithm, that's how you learn about cryptography. But you'd never put your homegrown cryptographic algorithm into production until after several PhDs worth of understanding of cryptography has been put into it by many other people.
>"Monetary value must be stored in integers" is the much stronger statement that the article doesn't make. Obviously there are exceptions, but you're going to need a much longer side discussion in order to justify why you're using floats.
It doesn't make that statement because arbitrary precision decimals exist. Binary float is not the only and not the most plausible alternative to using integers.
For me quickly scanning over the article, the fact that floating points were even presented as a possibility was an immediate red flag. And I pretty much stopped taking the rest of it seriously.
> It's always integers unless you have a VERY good reason to do otherwise
I don't really agree. This seems like one of those outdated "greybeard rules" which people love to cargo cult, but to me it just comes with its own set of trade-offs, like now you have to think about exponents everywhere and getting them wrong comes with orders-of-magnitude consequences.
If you have a modern DB and your languages handle its decimals well then I'd use them. You can translate as needed for imports and exports, but the core of your system is always sound. Switch to the more basic formats if and only if there was some perf problem with decimals, which for most fintechs will be a VERY long time with modern DBs.
What really pushes me towards decimals is the consequence of getting something wrong. Relying on comparing raw ints, with variable exponents baked in, can lead to catastrophic errors with multi-order-of-magnitude gaps if that implicit exponent isn't carried along correctly. It's a massive footgun always waiting to happen. Decimals avoid that whole class of problem. So the goal is to maximise the "safe" decimals as source of truth everywhere you can, and have very well-tested "in and out" paths for any producers or consumers with different representation preferences. To me, that's good system design. And when, not if, you're manually inspecting DB records to track down a bug, having everything normalized in a glanceable, obvious format like decimals will let you recognise errors faster and more intuitively.
This goes extra for crypto, especially stablecoins, where one USD stablecoin might be exp-6 and another might be exp-9. And you're representing them in the same DB! Off by a factor of a thousand if you miss that exponent! Decimalize that immediately says I.
> thank you Rust decimals represented as JSON floats
What do you mean? JSON doesn’t have floats, it has numbers, and how they’re used after being parsed is not part of the spec.
> If I ever see a monetary value stored in something else than integers I'm going to run away screaming
That’s good, then we’ll likely not be working on the same system :) I consider running from “amounts as integer” systems these days (but usually unfortunately can’t). In an idealized codebase that only seasoned financial programmers are allowed to touch, it can go well, but such a system is usually either overly exclusive or risks becoming brittle.
> JSON doesn’t have floats, it has numbers, and how they’re used after being parsed is not part of the spec.
I think that's the problem they were trying to describe. Without a formal spec, systems won't agree on how to handle floats. JS engines treat numbers as 53 bit signed floats, so passing a well defined decimal there through JSON means losing precision at the edges.
Money stored in integers gets around the issue by simple virtue of not really needing more than 53 bits to accurately represent the values anyone is going to encounter.
There are downsides like all the extra math or functions to handle doing the math everywhere money is manipulated or displayed, but this is the sort of thing where static typing is really helpful, and isn't too hard for juniors to understand that they should always use money functions to work with money data.
With I need to represent monetary amounts through JSON, I encode it as a base 10 string and wrap it in quotes so that the JavaScript engines treat it as a string. Conversion back to int happens after the string has been parsed and validated.
I do this with HTTP GET and POST form requests as well. In HTTP, everything is a string (even if that string is JSON).
I'm sure there is something I don't know here, but how is working with integers "brittle"? The only issue I see is rounding down by default, not sure if that is even an issue or not. At any rate, it seems a lot less brittle than floats or bigdecimal style number classes.
The brittleness comes from the fact that the number of implied decimal digits per currency isn't always well-defined across all stakeholders and systems.
If you're only working in a single currency, there's usually no issue.
Yes, definitely always include the number of digits, but at your system boundary you still have to pray that whoever you're working with isn't silently dropping that number and re-deriving it from their own, almost-4217-compliant currency database.
Redundancy can be great, but it's not a panacea, since it's not guaranteed to be used in an optimal way.
There are pretty trivial ways to use binary floating point values that don't result in 0.1 + 0.2 producing 0.30000...4 and it saddens me when this topic comes up and people go to such extreme lengths to recreate a second hand buggy reimplementation of a subset of floating point numbers to do it.
By not thinking in terms of strict rules and dogmas and instead focusing on the actual problem you want to solve.
If you want to guarantee that adding cents together results in an exact value without any loss of precision, and you also want a tiny memory footprint and very high performance, then use a binary floating point to represent cents, just as you would use an int.
Also the question of how it's better depends on your use case and my argument is not that one representation is universally better than another, it's that money is used for such a diverse range of use cases that you need to actually understand what you're doing, what the goal is, what the potential issues are etc... in order to pick the right representation for your use case. At my trading firm we have three different Money classes optimized for three different use-cases (with functions to allow interoperability between them).
For the use case where the representation needs to use little memory, is fast, and needs to be used to perform complex financial calculations at an enormous scale, then you use binary floating point where a value of 1.0d = 0.000001 dollars. This gives you exact precision when working with cents, and lets you perform all of the usual financial computations that most quantitative applications need to perform with excellent performance.
We also have a use case optimized for I/O, where no calculations are expected to be performed but the data will be transmitted over a network or to/from a database etc...
If this isn't your domain, maybe you're just writing a GUI application/web app, then by all means use a big decimal or use an integer... I don't know, but it's very sad seeing how many people who are presumably professionals don't take just the bare minimum amount of time to think things through and instead just reason in terms of strict dogmas.
> you use binary floating point where a value of 1.0d = 0.000001 dollars
Is that actually better than just defining one dollar as 1.0? In my (pretty limited) understanding of floats, this doesn't seem to add any precision or prevent any edge cases.
Depends on your use case, once again. If your use case requires that arithmetic involving cents is exact, then yes defining 1.0d = 1 cent guarantees that, just as using the int value 1 = 1 cent guarantees that. Floating point numbers can represent and perform exact arithmetic on integer values, no loss of precision.
The original example was being concerned that 10 cents plus 20 cents should equal 30 cents exactly, not 30.000000...2 cents. Well if you set 1.0d = 1 cent, you get that exact result. In fact for any arithmetic performed on values greater than or equal to 1 cent, you are guaranteed to get exact results just like you get exact results using an int to represent cents.
Now if your use case does not require exact arithmetic on cents, then by all means stick to using 1.0d = 1 dollar. Evaluate your use case and decide on an appropriate scale to use by default.
Where the benefit of floating point kicks in, for certain use cases, is that you can continue to use the same representation with operations that don't work well with fixed precision, like interest rate calculations, financial derivatives, or more advanced financial modelling techniques and you get these operations at significant orders of magnitude faster than you would using decimal representations.
And I'm not talking like 20% faster or 50% faster... the difference between using a binary floating point representation versus decimal ranges from 10x to 100x, and the difference between using a double versus a big decimal is on the order of 1000x.
In order words, a financial report that can be generated in 1 second using binary floats can end up taking almost 2 minutes using a decimal representation, and 16 minutes using a big decimal.
All of the data we control (in the database, in our apis, etc) is integer cents. When we have to interface with a system that represents money using JSON numbers as dollars.cents, we parse or serialize it into an arbitrary precision decimal type. Hasn't been much of a problem.
An integer for the value (scaled by number of decimals) and an integer value for the number of decimals. Different systems may use different values, even for the same currency or asset.
So all the people who write financial software and avoid floats don't know what they're doing? That's would end up being just about everyone. You should teach them, you know so much more than they do.
You can't do everything you need with an integer. There are values you might want to display or calculate with that are smaller than cents. In some places you'll need things like BigDecimal, which are immune to floating point errors in most cases.
It's also safe to return decimal values for displaying values.
Its a wrapper around an `Integer` where you declare the scale in the type. So if you use `Fixed E2` as your type then `MkFixed 1` is 1 cent. If you did `Fixed E3` as your type then `MkFixed 1` is 0.1 cent. In both cases it is entirely an integer encoding.
If you're calculating stock gains, taxes, etc. You'll want to do it on BigDecimal on line items, then sum it up. Cents could lose precision leading to a large difference on millions of line items.
If you're doing static payments or displaying a number, it doesn't matter if it's float amounts or integer cents since you're not doing any calculations anyway.
Stripe uses cents mostly because it's the most foolproof way that won't cause massive issues as a public API.
But the price of some unit of a commodity may well be expressed in smaller fractions to allow for small price changes.
You could argue that a price is not really an amount that anyone can actually pay and that's true. But it seems complex and error prone to store unit prices and amounts using different scaling factors.
That was the first thing that popped out and made me distrust the whole wiki; there's only One Right Way to store money (as integers[1], as you said) and it should have been explicit about that.
You can also use fixed-point if whatever you're using supports it but it's still technically integers.
Word of advice to anyone considering the "minor-units precision" strategy for representing monetary amounts: Don't (or at least, don't use it as an interchange/API data format).
It seems like a clever idea (fast integer math, no rounding problems for addition and subtraction), but it'll bite you incredibly hard if you ever stumble upon an edge case such as working with a partner that has a different implied number of digits for a given currency. This is especially relevant for stablecoins, which often have a different number of implied decimal digits than the "fiat" currency they represent.
Also, consider representing amounts as a string type in JSON-based APIs. JSON does not specify decimal precision, so you (and all your users/vendors) will always have to make sure your parser/serializer doesn't internally lose precision by going via floating point. This can get ugly fast, and while a string seems conceptually less neat, it completely bypasses that problem. (Some will call this an anti-pattern [1], but I'd rather not fight this particular battle for ideological purity on the shoulders of my users or shareholders.)
The only real correct solution here is to send mantissa and exponent as two separate integers. It's trivial to convert between exponents for whatever math you want, it can be as correct as you want, and is unambiguous.
In the HFT space you save some wire space if you can commit to a consistent exponent for some {slice} up front (think instrument/tick-size/asset-class/exchange/feed/server/whatever/...) such that you only need to send the mantissa and your clients can have a hard coded exponent. However, in similar spaces it's often worth the extra uint32 to send a on-the-wire exponent such that things _can_ change and you aren't hamstrung later by earlier "we only need cents now!" design choices when, e.g., you suddenly need to support bitcoin/... prices to full precision. (your users will thank you when they don't have to coordinate a breaking change when you want to adjust your fixed exponent)
That’s quite a bit slower to process. At least if you’re converting to integers to do the calculations and the calculations would be quite a bit slower if you kept the big decimal type
I guess I’m coming at it from an optimizing market data provider perspective once you preallocate memory the next thing to optimize is the string decimal conversion if the feed isnt binary encoded
It might be your least concern, and that's fine but it's not the least concern for many people who need to process large volumes of transactions.
Money, even within fintech, is a concept used across a wide variety of domains, and you can't assume that what concerns you is what concerns everyone else relying on it elsewhere.
No, standard floating point implementations have higher precision for smaller numbers than larger. So for example, in a 32bit float, there are far more numbers between 0-1 than there are between 1,000,000 and 1,000,001. For 32bit floats, you start lowing whole integers with relatively small numbers.
Integers have a consistent precision across the entire number line.
Having done HFT / low-latency in C++ with a browser based (read: JavaScript) management front-end: Go ahead and use integer cents everyone. It’s practically an industry standard and it works just fine. Anything else is a worse compromise.
It is fine as long as you don’t cross any edge cases (crypto, or more recently stuff like AI token pricing) and don’t forget to account for third party quirks (e.g. Stripe’s zero-decimal currencies: https://docs.stripe.com/currencies#zero-decimal).
JPY not having any minor units is arguably not a “third party quirk” but just how the currency works. The same goes for various three decimal digit currencies.
I mean yeah, but it does make things more complicated. Currencies change to 0-decimal, but some systems still expect 2-decimal representation for backcompat reasons (like ISK and UGX in Stripe).
(To clarify some more, we’re in agreement here. My point was mainly that “just storing cents” seems like an easy solution and it might seem to be working well – until it’s not :-)
If someone sells you 12345.55 EUR vs USD at a rate of 1.12345, how many EUR do you think you end up with? Do you think all market participants even agree? What if the rate is 1.123456?
For added fun, you can introduce division. Some systems will allow you to sell 12345.55 USD to buy EUR at a rate of 1.12345.
The article’s “no lost data” tenet is not really viable when this sort of division is involved. Are you going to track your account balance is a rational number with an absolutely immense denominator forever?
> If someone sells you 12345.55 EUR vs USD at a rate of 1.12345, how many EUR do you think you end up with?
1. If someone sells me 12345.55 EUR, I hope to end up with 12345.55 EUR.
2. That's the point though. They will sell you a certain amount of EUR at a certain dollar price. This, in turn, implies a rate (which might well have more than 5 digits behind the decimal). This is ideally close to the quoted rate, sure. But what counts is the actual EUR amount and the actual USD amount, not what rate was quoted or with how many digits.
You store the sums on either end, the currencies, the exchange rate and the final sum? No one has .0000145 cents in their account. Rounding occurs in the real world.
> You store the sums on either end, the currencies, the exchange rate and the final sum?
There is a remarkable amount of disagreement as to whether one should do one’s back office work based on the price or based on the quantity of the counter currency.
> No one has .0000145 cents in their account. Rounding occurs in the real world.
Indeed. But you either need to convince all parties to agree to round the same way or you need to accept small errors
My experience in consumer banking says that every instrument specifies the precision of the calculation, how and when rounding happens, and slew of
little details.
So, yes, everyone has to understand how all their partners are doing rounding and summing.
In certain areas of the institutional finance world, everyone seems to accept that everyone's math is allowed to differ by a few cents, and they tally up the errors and move on with their lives.
I would, however, by quite surprised if my personal bank account did this.
Agree with this, working from HFT to payments to account management in the past.
You can have the blockchain team be an expert in converting integer cents, or the forex team be an expert in sub-cent conversions. You don't want to require _every team_ to have expertise in float math, by default.
When performance isn't a concern, I largely agree! Not every financial system can use big decimal as their base, though, too. And HFT isn't the only place in the financial sector where this performance concern might pop up.
You provide different handling strategies for different currencies. You also sort currency data alongside your amount. There is nothing complicated or edge-case here.
This works for USD, JPY or $MEMECOIN and it scales very well.
> but it'll bite you incredibly hard if you ever stumble upon an edge case such as working with a partner that has a different implied number of digits for a given currency
Why would that be a problem? You just transform the values when interacting with their API.
Sure, you can do that if you can absolutely guarantee that everyone will always respect that separation and there will never be ambiguity between your internal and some partner's representation – even during incidents, even during low-level CSV-to-DB ETLs during incidents ("just one time, I promise, we don't have time to build the proper adapter, but look how similar their and our formats are").
You'll have to decide when and how to round. Keeping individual billing items at high precision and rounding after summing them up can work; defining and documenting a rounding policy (or complying with whatever's legally required in your jurisdiction/domain) and rounding each individual billed item can as well.
Let's say I operate with a 4 decimal expectation and your API expects 6, is there any way to reconcile that outside of documentation and or metadata ? (which would be the same issue I guess whatever representation is used ?)
Still, even if you do: Chances that your users are just going to assume you're conforming to ISO 4217, some national standard, or your competitor that they're already integrated with are pretty high, so I wouldn't take the chance. Pick something that doesn't have to be documented instead.
I think I’m agreeing with you whole-heartedly if I say that article’s conclusion is at best extreme and unrealistic when it says “the parsers need to be fixed. They should support extracting any numeric type we want from JSON numbers and at any precision.”
This sounds like an unreasonable position to take. “Any” is an unachievable standard that could require an unlimited engineering budget with no demonstrable value in practice.
It is good to identify the lack of a standard, and to talk about what parsers do in practice, and good to discuss the gaps and unmet use-cases. It would be a good idea to suggest that there should be a more reasonable standard, perhaps. It’s just not a good idea to demand that everyone support “any” possibility when no one really needs that, no one knows what it means, and it’s not actually possible to achieve.
What do you recommend instead? Standard floating-point ("float"/"double"), fixed-point arithmetic with thousandths (or smaller) of the minor unit, arbitrary-precision decimal numbers, or something else entirely?
I think what matters most is your database and API representation, as well as having consistent and well-defined rounding rules.
I largely agree with TFA: Round explicitly and consistently whenever you cross a boundary, i.e. database persistence and internal API calls.
Use whatever works for your required business case internally (i.e. inside of procedures calculating some function of one or more input amounts). This can be regular old floats/doubles if you absolutely know what you're doing, or BigDecimal if you aren't and would rather suffer slightly slower performance than having to talk to an auditor about IEEE 754 rounding modes, or even minor-amount integers (yes, even though I just said to not use them – but you'll want to ABSOLUTELY NEVER leak them outside of your system, including your data/analytics pipeline, which might have different ideas about financial amounts than your business logic implementing a nice custom monetary type).
How is that better than {“amount”: “10.00”} (which also bypasses all potential floating point parsing issues that your or your counterparty’s JSON library might have)?
String and two-field exponent/mantissa representations are mostly the same in terms of semantics, yes. Making it two separate fields makes it less likely it would be put into `parseFloat`, but after doing some research I think strings are more popular in JSON [1, 2], so probably I’d stick to that as well.
Because it's much more explicit. Computers are fast, engineering is expensive. You usually never want to optimize prematurely when dealing with monetary amounts.
Except that now you have a new problem: Opinionated theorists that haven’t been part of a nasty “oh no, we accidentally considered some amounts as 10x/100x/1000x larger/smaller than expected” incident in their career yet…
You'll definitely have to throw it away at some point.
The art is in making those points well-defined and rare enough to not cause large discrepancies, but frequent enough to avoid ballooning arbitrary-precision numbers across databases and services that might not be able to handle them.
Floating point value stored multiplied by 10^8. That gives you a huge integer, but it's extremely accurate, especially for US denominated currencies. Easily transformed into floating point numbers for reporting/etc.
What is with this Twitter esque style of discussion? Post some vague comment with no real stake in the ground, but just reply to follow ups asking for clarifications about the right way. It's exhausting. Why not put all that effort into the initial comment?
If there were a simple one-size-fits-all solution to these problems, there wouldn't be a need for a handbook, nor for a discussion, would there?
I can't design everybody's systems here, but I was hoping that sharing some war stories that have cost me days or weeks of work might sensitize somebody to a few non-obvious footguns.
Nice. The book contains a bunch of good information that could already be found elsewhere but collecting it is quite practical. I highly suggest to read Kleppmann's Designing Data-Intensive Applications. The first edition was very good, a second one came out recently.
I was CTO of a FinTech where I built the whole software stack from scratch: the lessons in the book are mostly correct. I say mostly, because as always, there is a lot of "it depends" to take into consideration for your particular project. For example, I chose to not use event-sourcing to avoid the whole state computation issue. A standard append-only audit trail can do the job.
You can't guarantee exactly-once delivery but you can construct effectively-once processing, and that is what you really want.
Store every request and response : absolutely, and not only when consuming APIs, but when collecting any information from the outside world (and, if you can, also log every intermediate transformation step within your perimeter). Content-adressed buckets + a relational table are great for this.
The text also does not mention anything about data lineage. What happens if a vendor updates some data mid-day that you absolutely need to be aware of? You need to be able to account for that, while also re-playing computations that used the old values and get the same result. It's not a particularly hard problem to solve, but it takes some thought.
Why content-addressed storage? I'm assuming this is mostly for auditability after the fact? Wouldn't it make sense to use some business identifier to be able to refer it back easily?
You will need a combination of the two. The filename should contain a compound business identifier, essentially a tuple of appropriate facts, as well as a (truncated) hash of the content.
And yes, the reason is auditability but also bare necessity.
Imagine vendor A publishes fresh files every day, but you don't exactly know when. And suppose that the vendor can (and does) republish some of them at any time, with or without notice. You need to watch those files for changes and update your own sources accordingly.
For the collection and dedup, you can construct a tuple of meta-information that will give you a good idea if the file is the same (name, time last modified, file size, etc). Then only if you're still unsure do you download the file and hash it, compare with what you already have, and make a decision. (What if the file name changed slightly and now you must re-categorize the data in the file, or what if a single row was added or removed, etc).
It's also useful when auditing (tamper-proof) or when re-running or debugging calculations that used the data in question. If a user used the data available at 9AM, which was unfortunately incomplete and led to issues, having a full data lineage really helps. You can trivially re-run the computation with the information available at that time to see what happened. Or you can mark some results as stale, etc.
There are different ways to do this and one is keeping track of your content-adressed files in a relational table and give each row a unique identifier, so that you can tell that this particular row in your AAPL OHLCV data comes from this particular file which was fetched at this particular time etc, etc. Pair that ID with a timestamp and when doing computations you can query for "the latest" AAPL data while storing the exact file ID (time is fuzzy, content hash is not). Look into temporal tables and the like.
As a programmer, what I feel when I see fintech programmers each speaking from their own different experiences and perspectives is that it makes me wonder what it really means to be good at programming.
What user xlii said about not storing monetary amounts as floats is a common IEEE 754 issue. And while it's true that financial tracking should be done through immutable logs or event-based records, I don't think every surrounding service needs to be built with event sourcing. I think it's enough to apply it only to core logic like ledgers, settlements, orders, and executions. Looking at xlii's comment, it seems like a technique that only becomes viable when the modeling is successful.
User lxgr's comment points out that it's a minor-unit issue. If JSON numbers are parsed as floats by the language or parser, precision can be lost. Usually people send values with a separate decimal places field. However, I've heard that in HFT, they don't do that because the overhead itself is too costly.
And antonymoose's comment aligns with what many books say. That's why designs like this are common in FX or API contexts. It feels like protocol design, doesn't it?
Putting it all together, everyone's right within their own domain. While I think it'd be great to have someone like xlii as my senior programmer, I also feel like I wouldn't be able to design such a complex system myself. In that sense, everyone's statements are valid, and it's interesting to see how opinions diverge depending on the domain. Is this what expertise looks like
Looking at all this, it seems like you can roughly infer where a programmer is coming from based on their experience. Sometimes programming doesn't feel like finding the right answer, but more like choosing a worldview
Watching how programmers model their domains on HN is always fascinating. Sometimes I click on their profiles and add their domain knowledge to my own personal wiki, thinking I might use it someday
Fintech/finance is a very big industry and there are a lot of sub-domains. A HFT system programmer thinks about how to colocate their prod machines in NYSE and use zero alloc techniques to ingest marketdata as fast as possible, while a crypto wallet app developer spend more time designing cool UI to attract users and working out all the L1/L2 quirks. And I'm sure they have very different answers to whether to use integers and how many digits to preserve
> However, I've heard that in HFT, they don't do that because the overhead itself is too costly.
I've worked at several HFTs and 2 had independently settled on 64bit signed fixed point with a implied 10^9 scaling factor between internal systems.
However in-process one just used 'double' for all FX conversions, scaling etc. With 15 digits of precision and careful rounding choices it's fine.
Dealing with the outside world you'd obviously just convert as cheaply as possible - and there are some crazy fast algorithms doing fast binary float -> correctly rounded decimal conversions these days
A good programmer is just a bad programmer who’s learned not to make the same mistake twice. That’s part of expertise and why it differs too, everyone has made a different set of mistakes, domain-coded.
It's very true. As a merchant acquirer, everything is BigDecimal, only the auth path needs real performance, and FX occurs at a fixed rate per-day which is easy to reconcile.
It's fun reading all the different ways fintech has to adapt their systems for their specific niche.
I think most of this applies to software engineering generally, not just fintech.
For example the parts talking of retries, idempotency, event ordering, etc. This applies to all systems that require any degree of accuracy, even if no money is directly involved. I've seen so many systems built on the assumption that "we can always retry", but you can only retry if you fail cleanly in the first place, and if the downstream system offers the same level of idempotency that you think it does. Quite often these are not put to the test.
That's putting it politely. Honestly, I think this "handbook" was mostly written by an LLM.
For example, in the immutability section we have this:
"Separating PII from financial data lets you honor erasure without losing the financial history you’re obliged to keep."
In a financial organisation the two go hand-in-hand for obvious KYC/AML reasons.
Keeping the financial data whilst trashing the customer names, addresses etc. instantly on-demand before the expiry of the relevant time periods is going to leave your entire organisation with a very bad day in the office if a $lawful_body comes knocking for the data to trace a crime.
People going to work in a Fintech should not be relying on a random "Handbook" written by an unknown person in an unknown jurisdiction.
People going to work in a Fintech should only ever work in accordance with their employer's internal handbooks/guidelines/etc which will have been written in conjunction with their firm's lawyers and compliance people to ensure it complies with the laws and reporting requirements in the jurisdiction(s) in which their employer operates.
> Keeping the financial data whilst trashing the customer names, addresses etc. instantly on-demand before the expiry of the relevant time periods [...]
Where does TFA recommend that?
As I see it, it recommends separating PII data you'll eventually have to delete from that you'd probably want to keep forever (including data factoring into your accounting equations/invariants), so that you can delete the former after the relevant recordkeeping periods have elapsed.
> People going to work in a Fintech should not be relying on a "Handbook" written by an unknown person in an unknown jurisdiction.
Sure, but they should also not blindly ignore any ideas and practices presented, or avoid looking beyond their own organization. Ideally, they'll then try to reconcile what they saw with their own knowledge and local regulations etc.
> People going to work in a Fintech should only ever work in accordance with their employer's internal handbooks/guidelines/etc which will have been written in conjunction with their firm's lawyers and compliance people to ensure it complies with the laws and reporting requirements in the jurisdiction(s) in which their employer operates.
Sure, in a world in with only perfect and error-free organizations, that seems like a reasonable approach. But how does one get there without having a conversation such as this one?
Unless its your job to architect stuff, in a financial firm you don't go looking around for ideas and practices.
You comply with your employer's practices end of story.
If you like looking up ideas and other people's practices then a heavily regulated environment is probably not the place for you.
> how does one get there without having a conversation
"having a conversation" about new ideas/practices in a regulated firm will involve lawyers and the compliance department.
More than likely that "conversation" will be above most people's pay grade. So you're better off just not wasting your time and adhering to your employer's existing practices.
And for everyone else, its an expensive and high-friction conversation to have if you want to change existing practices.
"Not thinking, just complying" isn't the panacea for good outcomes you make it out to be. You definitely want to limit the amount of excitement, but I've seen many issues caused by legacy formats and practices as well.
> You comply with your employer's practices end of story.
What if you're the employer ("first engineer" etc.), and there are no practices yet? Fintech almost by definition sometimes includes doing things from scratch because some existing solution or incumbent organization isn't working that well anymore.
> Unless its your job to architect stuff
Which seems to be the target audience/scenario for TFA.
> What if you're the employer ("first engineer" etc.), and there are no practices yet?
In that scenario the practices will still come first. You're not going to be doing any coding or systems engineering until you've got compliance signed off. You're going to be spending lots of time with lawyers and compliance people.
> Fintech almost by definition sometimes includes doing things from scratch
Yes, but cut through the noise of the typical Fintech fancy website and app and you're still staring straight down the barrel of spending 80% of your time on regulatory compliance.
Try as you might there are only so many ways you can re-invent the wheel for dealing with hard-facts legislation.
Please show me the regulation that tells me whether to use big decimals or integers in my internal monetary amount representation. Regulations usually care about outcomes (sometimes high, sometimes low level); they often don't tell you how to technically achieve them.
And if your lawyers and compliance people are actually telling you that you can absolutely not do any financial processing yourself, that the only possible way to be compliant is to license <incumbent product xyz> (unfortunately only available in COBOL) etc., you might not actually be working in a fintech, or at least not in the kind this guide seems to be targeted to.
Frankly, this kind of attitude is exactly why banking and payments is as fossilized as it is in some countries, and why fintech is eating their lunch in many cases. There has to be a balance between trying new things and doing what everybody else is already doing.
This sounds great until your counterparties, banks, the government, customers and others complain bitterly that your monetary amounts are off.
Your expectations of course are not unusual because so many developers work on systems where users are not the customers, but the product.
Facebook, Insta, Google all fuck up results very regularly, but what are you gonna do when they do?
Now imagine your bank occasionally losing deposits, your account balance going up and down a percent or two every day, the IRS fining you for tax evasion because your Cool Fintech Rounding does not match generally accepted accounting rules.
Well, in case of a bank it can. And it happens on a daily basis. In fact thats how most private bank money enters the system in the first place either when a bank makes a loan or when it buys any other asset like e.g. a corporate bond (you can conceptualize a loan as an asset purchase as well, the bank buys a promise to pay from the borrower which is recorded as an asset of the bank). Both are a balance sheet extension from a double-entry bookkeeping point of view.
The 4 operations double-entry bookkeeping allows are:
1. balance sheet extension (e.g. making a loan)
2. balance sheet reduction (e.g. loan repayment)
3. asset swap (e.g. a bank buying a government bond with central bank reserves)
4. liability swap (e.g. transferring money from one account to another)
So it may be more accurate to just say that every event that affects the balance sheet must be one of those 4 operations.
Also see "Money creation in the modern economy" - Bank of England Quarterly Bulletin 2014 Q1
> Well, in case of a bank it can. And it happens on a daily basis.
You mean it happens when a bank borrows money from a central bank, right?
Not every balance sheet expansion constitutes the creation of money, e.g. when you put money on your bank account, the bank's balance sheet grows, but the money is not newly created.
> You mean it happens when a bank borrows money from a central bank, right?
No I mean in the moment the bank grants a loan. The money the borrower receives is new money, its not taken from anywhere else. But at the same time the bank records the credit as an asset which makes it a balance sheet extension.
> when you put money on your bank account, the bank's balance sheet grows
That's true, you hand over cash to the bank which becomes its asset and your account balance is marked up for the same amount. Not all money in circulation is accounted on a bank balance sheet, but most of it is bank deposits. cash is an exception, it's public money, not private bank money. When you "put money in a bank account", i.e. you bring cash to the bank you are essentially selling your public government money to the bank and it pays you with its own private bank money (which it "creates" as well).
But this is only a micro-view of a single event. In the aggregate money is created when either the government runs a deficit or when banks make loans and money is destroyed when taxes are paid or loans are repaid.
Well, that's not money created out of nowhere. The balance sheet extension creates money and an equivalent liability. That's the point of the statement, not money supply and its regulation.
Fair enough, just wanted to point out that banks originate money as part of the lending process. You could maybe call that "money out of asset purchases" whatever that means and yes its a valid balance sheet operation. I guess you would agree that a bank needs no prior savings or deposits in order to make a loan, i.e. it doesn't need to have money that it "lends out"?
Or let me ask you this: What would in your opinion be a valid example of "money out of nowhere"?
some engineers and average person sees the entire fiat money as something that needs to be "earn" or get returns from investments only. they dont see how it's literally created as free and out of nothing.
Very true but also understandable because that's how most households perceive it from their micro-economic standpoint. But its very deceptive because it suffers from a fallacy of composition. If households want to earn more than they spend (i.e. save), some other entity must spend more than they earn. This can be somebody taking a mortgage or the government running a deficit. Debt and savings are mirror images of the same thing, two sides of the same coin and we use double-entry book keeping to track them.
It is very insightful to view money as a creature of accounting for debtor-creditor relations rather than thinking of it as some kind of commodity.
I just wish the financial industry itself had known about these when the core banking systems and financial communication protocols of the 60s and 70s were invented that are still being used to this day...
Many of these predate the widespread knowledge of idempotency, so often idempotency keys are hacked together by joining various, hopefully globally unique fields, except that they never quite are. (You can look behind the curtain sometimes, e.g. when your bank does not let you transfer the same amount to the same recipient account on the same calendar day.)
I've spent many hours explaining how idempotency is supposed to work, and why it's important. Most teams understand the need for it, but very few thought about it up front.
A Plaid balance check is NOT a guarantee that the ACH debit you're about to submit will go through.
I don't care if the balance is one million, before that ACH can process, every single dollar can be (a) wired out, (b) cleared out by yesterday's ACHs (bills, autopay, whatever) and checks, or (c) spent at debit/ATM.
I probably shouldn't tell you why I know that some fintechs don't address this.
All this debate about floats or ints or decimals, obscures the fact “reconciliation” is an essential part of dealing with money - essentially some separate implementation of the money movement that detects when your rounding strategy or floats end up creating or destroying money, but all the accounts balance at then of the day.
I have just left a fintech company after 5 years and I can say after reading this, it looks legit to me (not AI slop as someone asked). These are the same sort of lessons I learned during my time in the industry.
I would recommend anyone starting in fintech to take some time to understand accounting principles and the ledger in a bit more depth than just debits vs credits - this is likely what is most unfamiliar to programmers.
Also financial software is very data-heavy and I learned more about databases in my time working in fintech than the 15 years before that. I think going into a bit more detail about even the basics (indexes) will save a lot of headaches.
> I would recommend anyone starting in fintech to take some time to understand accounting principles and the ledger in a bit more depth than just debits vs credits
Any good resources you would recommend to learn more about this?
I've seen a few attempts at "accounting for programmers" guides, some of which I came across while browsing HN (if you search you'll find plenty). The one I used to send new developers was one such guide[1]. They also have another on building a ledger[2]. These are a good start and I wish I could recommend a proper textbook that goes into more detail - detail that I wish I had known early on - but I learned mostly as I went along.
> I would recommend anyone starting in fintech to take some time to understand accounting principles and the ledger in a bit more depth than just debits vs credits
Probably good advice (for everyone in fintech, not just programmers) considering the absolute disaster that happened at Synapse. Kind of wild nobody has gone to jail for that.
Chunks of good advice, tossed and blended into a soup of verbiage generated by a helpful LLM. It sounds smooth and resists being read at the same time. I hope we can get to the point where actually good, helpful prose is generated more often than not.
I really enjoyed this. This year I joined a fintech company, coming from a non-financial domain. This is the kind of information that I had to learn from experience, other devs, and random blogs/articles. The fact that this is a centralized location is awesome! I wish I knew about this when I was onboarding.
Anyone know of resources like this but for capital markets? Things that would allow engineers new to trading equities, options, FX, bonds, and commodities to learn about different flows, market structure, common architectures, and other things that normally you learn from years of experience.
Equities and (listed) commodities are relatively easy to get a handle on but it genuinely takes months / years even at the frontline to understand how fixed income and FX works because its still almost entirely an OTC market. There is more central clearing than ever before but e.g. if I (say) buy a bond, fund it using a repo, swap my loot back some other currency, quite a lot of this could easily still be relying on humans pressing buttons and wiring money around.
To learn how and why these things are traded, however, read this book, the only (good) truly beginners guide to fixed income:
Even with equities, things are not entirely trivial.
A few examples: Some stocks trade in one currency, but pay dividends in a different currency. Some stocks go ex-div before the dividend amount has been determined (e.g. in Japan). Stocks trade on certain days, and trades settle on certain days, and they might be subject to different holiday calendars. Corporate actions are not entirely trivial, either (with rights issues, you need to issue two temporary securities for accounting purposes, etc.).
I'm founding a fintech startup and have been thinking about these things a lot. It is very helpful to have some general validation and guidance to make sure I'm not making huge mistakes.
I really wanted event sourcing, but thought it gets complicated when events have complex validation rules. I ended up with a mix of state and audit logs which works ok for now.
Huh, that's strange, where did all comments to this post go? I remember seeing three, then a few more the day after. Were they bots? They seemed legit recommendations, like the Pricing Money book.
I don’t work in fintech (yet) but I’ve studied finance recently and quite a lot of these pieces of advice are just intuitive when you know the business domain. Learning the “customer” of your software helps too
"Fintech" is extremely broad, and most of what gets called "Fintech" is really communication. Communication between firms, between traders, between systems, between ledgers, etc. There is no industry-wide "right" way to program anything, because the right way is ultimately whatever way the other party you're communicating with will understand.
If you're dealing with a party who tracks currency in cents, then tracking currency with more precision than that is going to lead to rounding disagreements. Vice versa if you deal in cents but they deal in tenths of cents. And so on for all the other advice in this document.
> Webhooks are the most common way to receive signals from external systems, but processing them safely is not trivial
I see webhooks documented all the time, but I have yet to use them in practice, nor have my customers requested them. Is the above not true, or are they widely used in some sectors and not others?
In payment gateway integration, webhooks are usually considered a single source of truth for updating the payment status, with status check api as a fallback.
They are indeed everywhere, but it's possible you don't have a need for them. For example, every time you buy something online using Stripe, the seller receives a webhook indicating that a purchase was completed.
I receive webhooks all the time as ack that something was processed/ or a notification of the status of some sort of thing in an external system that we don't control.
The whole avoid floats thing just isn't true. I have 20years experience in fintech and most of it used doubles. Excel uses doubles. Your frontend will use doubles. All your db supports doubles. Your stdlib knows how to parse doubles. Json uses doubles (not in theory but in practice). Many ERP system uses doubles
The thing for working with currency with doubles is that you have to keep in mind that it can hold 15 digits of precision in total. As long as your numbers don't use more digits than that, like 123456789.01 or 123.456789, you can have perfect decimal precision in your financial math. You just have to always round the result to within 15 digits of precision after each computation, and before each comparison. That's what excel does.
The biggest advantage of doubles is that 1) they're widely supported and 2) you can mix different precision in your system, which will appear if you do international finance or advanced financial products. Some accounting require precision up to the thousandth, some need to be rounded to multiples of 0.25. So at the end of the day you'll never use basic math but some specialised accounting math library and that library can perfectly use float as a backend.
It may be a good idea to start the book with a really short "About the author" to state exactly this and your work experience. Otherwise looks well written to me, good job! :)
Native English speaker. I scanned it and IMO there's a slight overuse/misuse of hyphens. Maybe the AI tool could be asked to identify and correct? (The hyphens might be triggering people to think it's AI, too).
They mostly need replacing with a full stop or a colon.
E.g.
"In practice this means storing the amount as an integer in its smallest unit - €12.34 becomes 1234"
->
"In practice, this means storing the amount as an integer in its smallest unit: €12.34 becomes 1234."
or
"In practice, this means storing the amount as an integer in its smallest unit (e.g., €12.34 becomes 1234)"
It's a misuse because in this context the hyphen could be mis-construed as a negative amount, and this causes the reader to stop and carefully re-read the content to ensure that they're not misinterpreting it. That's not where you want to be spending your reader's attention.
Skimmed it and based on my experience in fintech, it looks good, accurately represents the real world. I guess there’s still a chance it is AI generated but it doesn’t seem like vacuous slop, it has substance!
Whilst I wouldn't say anything in it requires years of experience to know, this would be helpful for someone who hasn't considered anything about monetary systems. It doesn't read like slop, but I could be wrong but even so it all seems fairly reasonable (I've only fully read about 50% before realising there's nothing new here for me, and then skimmed to rest).
I just published Fintech Engineering Handbook distilled from 6 years of tears, sweat and swears.
It’s a free ~25-page resource with various hints and patterns around handling money.
Tell me what you think!
other than that, peruse the commits on the source [1], or wait for the author to respond.
I glanced, and I found this handbook shallow and - in some areas - even bad advice.
E.g. If I ever see a monetary value stored in something else than integers I'm going to run away screaming (thank you Rust decimals represented as JSON floats). It's always integers unless you have a VERY good reason to do otherwise (though exported view can be in anything, even in weird bitcoded formats).
FX exchange. Resolution of FX isn't a point-in-time thing, things like buyer rate-in-time, seller rate-in-time, agreement, agreement tolerance, agreed upon resolution timestamp come in the effect.
Immutability - that's why you want to have event sourcing everywhere that touches money:
Though in the end Fintech != Fintech. I worked at Fintech where money was treated like a baggage, and in other where money was a central point of everything.> E.g. If I ever see a monetary value stored in something else than integers I'm going to run away screaming (thank you Rust decimals represented as JSON floats). It's always integers unless you have a VERY good reason to do otherwise (though exported view can be in anything, even in weird bitcoded formats).
That really overstates the issue. Whole domains of finance run just fine on doubles.
If you're doing Monte Carlo options pricing over interest rate paths, and you're interested in the risk metrics, like durations, convexity, vega, and so on, no one cares what your rounding convention is. doubles are just fine, thank you. How are you going to force `exp(-rt)cashflow` to be an integer? Or the normal CDF?
Yes, there are domains where ints make sense. But it's certainly not universal, you just need to make the right engineering choice.
It can be really frustrating viewing threads like this sometimes. I've not once seen an interest rate swap priced in anything other than float/double, and that's relatively simple even compared to some of the crazy instruments out there.
Like, sure, probably don't use floats for everything, but what are the odds that your greeks are gonna be nicely expressable as simple rationals?
Basically whenever you are acting as a custodian e.g., a bank. Then you have to be super careful.
No one cares if you’re mortgage calculator is off by a penny
Quick browse over Stripe API (i.e. not a simplistic payment provider) will show exactly otherwise.
Metrics/durations/convexity/vega aren't monetary value, these are calculation values and I'm fine with them being whatever. If you're calculating averages to make decisions based on this I don't care if you're counting dolars or tomatoes.
Monetary value is $4.50 and it's representation of actual money passed through system. In practice this value is almost never undergoing any operations except for addition and subtraction. Any multiplication/divison etc. is left to specialized systems that return results wrapped in 800 pages of standarization documents, regulations and so on; systems where term "bit flip" is not a joke, but a risk.
As I wrote fintech != fintech, and I know many organizations aren't at that level of scrutiny, but a huge domain of people dealing with others' people money know that charging someone 1 cent too much is a tens of thousands of problems coming their way.
What's your preferred solution to dealing with FX conversions?
My issue with using integers everywhere is that FX conversions (or other rates) always come into play, and at that point I'm forced to use something else anyway (e.g. arbitrary precision decimals).
I never had to deal with that. At one place it was so complicated that specialized system took reconciliation over (it got baggage full of context data including exchange agreement details, with boundaries and time-regions plan).
In not-a-fintech we just went with doubles and rounded up (worst case - we get a cent more of a customer).
Though if I had to design today for that, I'd look for non-string serializable decimal, so not "10.123 == (10, 123)" but more like (10123, 3) and serialize in JSON as a value and precision separately.
Yet that's only cause I saw Decimal(10,123) sent in JSON as "10.123" which JSON reader red as 10.122 float and inserted to "1012" to database.
I worked on a trading system dealing with transaction limits expressed in USD but trading in many currencies.
FX rates and prices were expressed in fixed point because it was more efficient to handle in FPGA. A lot of thought went in to making the system accurate enough based on real FX rates while falling on the right side of the limit
I love how this sensible take is followed by a tornado of comments that boils down to "NEVER use floats JUST BECAUSE".
Never use floats for financial calculations. Because hard won experience.
"Floats" are simulations of Real Numbers, and Reals are uncountable. Not what you want for finance, where everything is counted.
I suggest reading the comment I was replying to since it contextualises the answer quite well. Hard to find absolutes in real life outside of thermodynamics.
> you're interested in the risk metrics, like durations, convexity, vega, and so on, no one cares what your rounding convention is. doubles are just fine, thank you.
Doubles are not just fine, but required there. If you compute risk, as you allude to, by finite differences, then you need the precision of floats rather than rounding to integer cents prematurely.
Rule of thumb here is that investments will work fine using doubles, but transfers need to run on int or fixed point.
It doesn't matter for quant finance to use floats because approximate is perfectly fine. Tracking the exact amount to accounting standards is the job of the brokerage team not the quant devs. Floats are fine for modeling as long as the numerical drift is accounted for.
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What are you referring to with the integers/floats comment? The article says clearly that the rule of thumb is not to use floats and that they’re “almost never” a good idea, that they cause unpredictable precision loss, and recommends integer or BigDecimal types in multiple places. Are you also talking about rationals? So what is the bad advice here, exactly?
For FX, it seems like you’re reinforcing what the handbook says, that there’s no canonical rate. Aside from that, it’s talking about post-resolution records and you’re talking about how to resolve, no? That’s valid nuance of a separate goal, and it’s a fine goal of yours, but doesn’t seem like a demonstration of something missing or wrong.
The article appears to make the very same point about immutability? What are you saying that’s different?
With integers/floats, he's saying it's not opinionated enough. Anything other than integers with minor-unit precision, unless you have a very good reason, is a bad idea. So "floating point is almost a bad idea" doesn't go far enough, and the other alternatives are presented somewhat equally.
The FX critique is saying that it's glossing over a lot of the complexity. I'd say the same is true for the treatment of DE ledgers, and it borders on bad advice (e.g. "Balance is never stored. It’s derived from the movements of money.")
> Anything other that integers with minor-unit precision, unless you have a very good reason, is a bad idea.
The article clearly communicates this sentiment, no? What else needs to be said? How much further does it need to go, and why?
It might be a mistake for either us or the handbook to be absolute or dogmatic about floats. It’s not a sin to mention that they exist, and it’s a fact that some people in fintech use them for some reasons that have a defensible engineering position and well considered tradeoffs. I’ve been on the side of assuming people don’t use floats for money and then been surprised when I bumped into people here on HN who report using floats in finance routinely.
BTW, is your quote “almost a bad idea” a typo? There’s a world of difference between ‘almost a bad idea’ and ‘almost always a bad idea’. The actual words in the article, if we’re quoting the article, are: “almost never a good idea” in reference to using floating point types.
> it’s glossing over a lot of the complexity.
Of course it is, that’s a good thing. It’s not pretending to be a spec or rules, it’s an introduction and general principles. The article is already introducing new complexities that people outside of fintech might not be aware of. But do we really have to mention ALL complexity? The biggest problem with Wikipedia is that it’s overrun by nuance and complexity, so much that you often can’t read an article on a topic without already being an expert on that topic. This is why experts are often bad teachers. Being unable to gloss over some complexity is not good for learning and doesn’t make a good environment for newcomers. Let’s allow people to write for non-experts and make room for learning. We don’t have to avoid glossing over some of the complexity; it’s useful to get the general direction and gist correct while leaving out some of the detail.
> it borders on bad advice
Be specific. What’s wrong? Note that contributions are invited.
"Monetary value must be stored in integers" is the much stronger statement that the article doesn't make. Obviously there are exceptions, but you're going to need a much longer side discussion in order to justify why you're using floats.
It's like saying "don't write your own crypto algorithm". Of course write your own crypto algorithm, that's how you learn about cryptography. But you'd never put your homegrown cryptographic algorithm into production until after several PhDs worth of understanding of cryptography has been put into it by many other people.
>"Monetary value must be stored in integers" is the much stronger statement that the article doesn't make. Obviously there are exceptions, but you're going to need a much longer side discussion in order to justify why you're using floats.
It doesn't make that statement because arbitrary precision decimals exist. Binary float is not the only and not the most plausible alternative to using integers.
If you render a number using JavaScript it has been temporarily stored as a doublr. The article was likely allowing for that common use case.
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For me quickly scanning over the article, the fact that floating points were even presented as a possibility was an immediate red flag. And I pretty much stopped taking the rest of it seriously.
> it borders on bad advice (e.g. "Balance is never stored. It’s derived from the movements of money.")
Don't think that's bad advice, in particular as the article clarifies:
> Derived state can be cached. Balances and projections can be cached or snapshotted for performance.
Of course you can cache or snapshot. But don't mistake that with storing the golden source.
> It's always integers unless you have a VERY good reason to do otherwise
I don't really agree. This seems like one of those outdated "greybeard rules" which people love to cargo cult, but to me it just comes with its own set of trade-offs, like now you have to think about exponents everywhere and getting them wrong comes with orders-of-magnitude consequences.
If you have a modern DB and your languages handle its decimals well then I'd use them. You can translate as needed for imports and exports, but the core of your system is always sound. Switch to the more basic formats if and only if there was some perf problem with decimals, which for most fintechs will be a VERY long time with modern DBs.
What really pushes me towards decimals is the consequence of getting something wrong. Relying on comparing raw ints, with variable exponents baked in, can lead to catastrophic errors with multi-order-of-magnitude gaps if that implicit exponent isn't carried along correctly. It's a massive footgun always waiting to happen. Decimals avoid that whole class of problem. So the goal is to maximise the "safe" decimals as source of truth everywhere you can, and have very well-tested "in and out" paths for any producers or consumers with different representation preferences. To me, that's good system design. And when, not if, you're manually inspecting DB records to track down a bug, having everything normalized in a glanceable, obvious format like decimals will let you recognise errors faster and more intuitively.
This goes extra for crypto, especially stablecoins, where one USD stablecoin might be exp-6 and another might be exp-9. And you're representing them in the same DB! Off by a factor of a thousand if you miss that exponent! Decimalize that immediately says I.
[flagged]
> thank you Rust decimals represented as JSON floats
What do you mean? JSON doesn’t have floats, it has numbers, and how they’re used after being parsed is not part of the spec.
> If I ever see a monetary value stored in something else than integers I'm going to run away screaming
That’s good, then we’ll likely not be working on the same system :) I consider running from “amounts as integer” systems these days (but usually unfortunately can’t). In an idealized codebase that only seasoned financial programmers are allowed to touch, it can go well, but such a system is usually either overly exclusive or risks becoming brittle.
> JSON doesn’t have floats, it has numbers, and how they’re used after being parsed is not part of the spec.
I think that's the problem they were trying to describe. Without a formal spec, systems won't agree on how to handle floats. JS engines treat numbers as 53 bit signed floats, so passing a well defined decimal there through JSON means losing precision at the edges.
Money stored in integers gets around the issue by simple virtue of not really needing more than 53 bits to accurately represent the values anyone is going to encounter.
There are downsides like all the extra math or functions to handle doing the math everywhere money is manipulated or displayed, but this is the sort of thing where static typing is really helpful, and isn't too hard for juniors to understand that they should always use money functions to work with money data.
> JS engines treat numbers as 53 bit signed floats
Please do not use Javascript for finance applications. Just do not do it
Save it for user interface elements and dancing whizzimagigs, where it will do less harm.
Financial applications need user interface elements too, and doing these poorly has caused quite some [1] harm in the past...
[1] https://financialregulation.linklaters.com/post/102j8af/the-...
Excel does the same thing, and has been used to track money for decades. Somehow, we're all still alive.
With I need to represent monetary amounts through JSON, I encode it as a base 10 string and wrap it in quotes so that the JavaScript engines treat it as a string. Conversion back to int happens after the string has been parsed and validated.
I do this with HTTP GET and POST form requests as well. In HTTP, everything is a string (even if that string is JSON).
I'm sure there is something I don't know here, but how is working with integers "brittle"? The only issue I see is rounding down by default, not sure if that is even an issue or not. At any rate, it seems a lot less brittle than floats or bigdecimal style number classes.
The brittleness comes from the fact that the number of implied decimal digits per currency isn't always well-defined across all stakeholders and systems.
If you're only working in a single currency, there's usually no issue.
As a general rule, you always include the currency code (EUR, SEK, USD etc.) and if possible also the amount of decimals, when using minor units.
Currency codes can be found in ISO 4217.
Yes, definitely always include the number of digits, but at your system boundary you still have to pray that whoever you're working with isn't silently dropping that number and re-deriving it from their own, almost-4217-compliant currency database.
Redundancy can be great, but it's not a panacea, since it's not guaranteed to be used in an optimal way.
How you expose you present your data to third parties is not how you represent it internally.
Ideally it’s not, but I’ve seen it leak so often that I’d rather be safe than sorry.
ISO 4217 also defines the number of significant digits after the decimal separator if Wikipedia is to be believed.
> I consider running from “amounts as integer” systems these days (but usually unfortunately can’t).
In the context of Fintech, how do you otherwise resolve floating point rounding issues if not representing amounts with integers?
Native decimal types, if your system has them. Many languages and databases used in financial contexts do.
But native decimal libraries are almost always floating point.
Do people not know what a floating point number is?
That’s alright, the important part here is that they’re decimal, not binary. You don’t want 0.1 + 0.2 to equal 0.300…004.
There are pretty trivial ways to use binary floating point values that don't result in 0.1 + 0.2 producing 0.30000...4 and it saddens me when this topic comes up and people go to such extreme lengths to recreate a second hand buggy reimplementation of a subset of floating point numbers to do it.
> There are pretty trivial ways to use binary floating point values that don't result in 0.1 + 0.2 producing 0.30000...4
Not across all architectures and operating systems there are not
Listen to those who have done this. Use integers for finance.
Yes across all architectures and OS's that use IEEE 754 floating points.
What are those pretty trivial ways? And how are they better than storing 10 cents + 20 cents = 30 cents?
By not thinking in terms of strict rules and dogmas and instead focusing on the actual problem you want to solve.
If you want to guarantee that adding cents together results in an exact value without any loss of precision, and you also want a tiny memory footprint and very high performance, then use a binary floating point to represent cents, just as you would use an int.
Also the question of how it's better depends on your use case and my argument is not that one representation is universally better than another, it's that money is used for such a diverse range of use cases that you need to actually understand what you're doing, what the goal is, what the potential issues are etc... in order to pick the right representation for your use case. At my trading firm we have three different Money classes optimized for three different use-cases (with functions to allow interoperability between them).
For the use case where the representation needs to use little memory, is fast, and needs to be used to perform complex financial calculations at an enormous scale, then you use binary floating point where a value of 1.0d = 0.000001 dollars. This gives you exact precision when working with cents, and lets you perform all of the usual financial computations that most quantitative applications need to perform with excellent performance.
We also have a use case optimized for I/O, where no calculations are expected to be performed but the data will be transmitted over a network or to/from a database etc...
If this isn't your domain, maybe you're just writing a GUI application/web app, then by all means use a big decimal or use an integer... I don't know, but it's very sad seeing how many people who are presumably professionals don't take just the bare minimum amount of time to think things through and instead just reason in terms of strict dogmas.
> you use binary floating point where a value of 1.0d = 0.000001 dollars
Is that actually better than just defining one dollar as 1.0? In my (pretty limited) understanding of floats, this doesn't seem to add any precision or prevent any edge cases.
Depends on your use case, once again. If your use case requires that arithmetic involving cents is exact, then yes defining 1.0d = 1 cent guarantees that, just as using the int value 1 = 1 cent guarantees that. Floating point numbers can represent and perform exact arithmetic on integer values, no loss of precision.
The original example was being concerned that 10 cents plus 20 cents should equal 30 cents exactly, not 30.000000...2 cents. Well if you set 1.0d = 1 cent, you get that exact result. In fact for any arithmetic performed on values greater than or equal to 1 cent, you are guaranteed to get exact results just like you get exact results using an int to represent cents.
Now if your use case does not require exact arithmetic on cents, then by all means stick to using 1.0d = 1 dollar. Evaluate your use case and decide on an appropriate scale to use by default.
Where the benefit of floating point kicks in, for certain use cases, is that you can continue to use the same representation with operations that don't work well with fixed precision, like interest rate calculations, financial derivatives, or more advanced financial modelling techniques and you get these operations at significant orders of magnitude faster than you would using decimal representations.
And I'm not talking like 20% faster or 50% faster... the difference between using a binary floating point representation versus decimal ranges from 10x to 100x, and the difference between using a double versus a big decimal is on the order of 1000x.
In order words, a financial report that can be generated in 1 second using binary floats can end up taking almost 2 minutes using a decimal representation, and 16 minutes using a big decimal.
Aren't decimal types BCD coded?
All of the data we control (in the database, in our apis, etc) is integer cents. When we have to interface with a system that represents money using JSON numbers as dollars.cents, we parse or serialize it into an arbitrary precision decimal type. Hasn't been much of a problem.
This started out fine for us except that int32s were a bad idea and upgrading was painful
That's definitely valid, for b2c applications though a lot of the time you will never deal with numbers of that magnitude.
An integer for the value (scaled by number of decimals) and an integer value for the number of decimals. Different systems may use different values, even for the same currency or asset.
What exactly do you think a floating point number is?
Usually that, but in binary, which causes lots of headaches for financial math.
For those who don't understand what they're doing, yes. But those people end up having headaches and causing trouble no matter what.
So all the people who write financial software and avoid floats don't know what they're doing? That's would end up being just about everyone. You should teach them, you know so much more than they do.
You can't do everything you need with an integer. There are values you might want to display or calculate with that are smaller than cents. In some places you'll need things like BigDecimal, which are immune to floating point errors in most cases.
It's also safe to return decimal values for displaying values.
The integer `1` can mean whatever you want, it doesn't need to be a cent. Haskell's `Fixed` type is a good example of this:
https://hackage-content.haskell.org/package/base-4.22.0.0/do...
Its a wrapper around an `Integer` where you declare the scale in the type. So if you use `Fixed E2` as your type then `MkFixed 1` is 1 cent. If you did `Fixed E3` as your type then `MkFixed 1` is 0.1 cent. In both cases it is entirely an integer encoding.
But it's a good idea to keep those values distinct from actual money. You can't pay a fraction of a cent to anyone.
If you're calculating stock gains, taxes, etc. You'll want to do it on BigDecimal on line items, then sum it up. Cents could lose precision leading to a large difference on millions of line items.
If you're doing static payments or displaying a number, it doesn't matter if it's float amounts or integer cents since you're not doing any calculations anyway.
Stripe uses cents mostly because it's the most foolproof way that won't cause massive issues as a public API.
But the price of some unit of a commodity may well be expressed in smaller fractions to allow for small price changes.
You could argue that a price is not really an amount that anyone can actually pay and that's true. But it seems complex and error prone to store unit prices and amounts using different scaling factors.
That was the first thing that popped out and made me distrust the whole wiki; there's only One Right Way to store money (as integers[1], as you said) and it should have been explicit about that.
You can also use fixed-point if whatever you're using supports it but it's still technically integers.
Word of advice to anyone considering the "minor-units precision" strategy for representing monetary amounts: Don't (or at least, don't use it as an interchange/API data format).
It seems like a clever idea (fast integer math, no rounding problems for addition and subtraction), but it'll bite you incredibly hard if you ever stumble upon an edge case such as working with a partner that has a different implied number of digits for a given currency. This is especially relevant for stablecoins, which often have a different number of implied decimal digits than the "fiat" currency they represent.
Also, consider representing amounts as a string type in JSON-based APIs. JSON does not specify decimal precision, so you (and all your users/vendors) will always have to make sure your parser/serializer doesn't internally lose precision by going via floating point. This can get ugly fast, and while a string seems conceptually less neat, it completely bypasses that problem. (Some will call this an anti-pattern [1], but I'd rather not fight this particular battle for ideological purity on the shoulders of my users or shareholders.)
[1] https://blog.json-everything.net/posts/numbers-are-numbers-n...
The only real correct solution here is to send mantissa and exponent as two separate integers. It's trivial to convert between exponents for whatever math you want, it can be as correct as you want, and is unambiguous.
In the HFT space you save some wire space if you can commit to a consistent exponent for some {slice} up front (think instrument/tick-size/asset-class/exchange/feed/server/whatever/...) such that you only need to send the mantissa and your clients can have a hard coded exponent. However, in similar spaces it's often worth the extra uint32 to send a on-the-wire exponent such that things _can_ change and you aren't hamstrung later by earlier "we only need cents now!" design choices when, e.g., you suddenly need to support bitcoin/... prices to full precision. (your users will thank you when they don't have to coordinate a breaking change when you want to adjust your fixed exponent)
> The only real correct solution here is to send mantissa and exponent as two separate integers.
That’s essentially the same thing as a String-serialized big decimal, just less readable, no?
That’s quite a bit slower to process. At least if you’re converting to integers to do the calculations and the calculations would be quite a bit slower if you kept the big decimal type
True, but this is usually your least concern when you're dealing with monetary amounts/math.
I guess I’m coming at it from an optimizing market data provider perspective once you preallocate memory the next thing to optimize is the string decimal conversion if the feed isnt binary encoded
It might be your least concern, and that's fine but it's not the least concern for many people who need to process large volumes of transactions.
Money, even within fintech, is a concept used across a wide variety of domains, and you can't assume that what concerns you is what concerns everyone else relying on it elsewhere.
They specifically mentioned HFT so I suspect they care a lot about processing speed
If you do that though aren't you just reinventing floating-point?
No, standard floating point implementations have higher precision for smaller numbers than larger. So for example, in a 32bit float, there are far more numbers between 0-1 than there are between 1,000,000 and 1,000,001. For 32bit floats, you start lowing whole integers with relatively small numbers.
Integers have a consistent precision across the entire number line.
No, because you're doing decimal floating point, which eliminates the rounding errors of binary floating point.
They're both floating point.
https://en.wikipedia.org/wiki/Decimal128_floating-point_form...
What noitpmeder described is just floating point.
Having done HFT / low-latency in C++ with a browser based (read: JavaScript) management front-end: Go ahead and use integer cents everyone. It’s practically an industry standard and it works just fine. Anything else is a worse compromise.
It is fine as long as you don’t cross any edge cases (crypto, or more recently stuff like AI token pricing) and don’t forget to account for third party quirks (e.g. Stripe’s zero-decimal currencies: https://docs.stripe.com/currencies#zero-decimal).
JPY not having any minor units is arguably not a “third party quirk” but just how the currency works. The same goes for various three decimal digit currencies.
I mean yeah, but it does make things more complicated. Currencies change to 0-decimal, but some systems still expect 2-decimal representation for backcompat reasons (like ISK and UGX in Stripe).
(To clarify some more, we’re in agreement here. My point was mainly that “just storing cents” seems like an easy solution and it might seem to be working well – until it’s not :-)
If someone sells you 12345.55 EUR vs USD at a rate of 1.12345, how many EUR do you think you end up with? Do you think all market participants even agree? What if the rate is 1.123456?
For added fun, you can introduce division. Some systems will allow you to sell 12345.55 USD to buy EUR at a rate of 1.12345.
The article’s “no lost data” tenet is not really viable when this sort of division is involved. Are you going to track your account balance is a rational number with an absolutely immense denominator forever?
Of course market participants agree?
Exchanges have calculation rules for every type of mark and payment and will always specify rounding.
> If someone sells you 12345.55 EUR vs USD at a rate of 1.12345, how many EUR do you think you end up with?
1. If someone sells me 12345.55 EUR, I hope to end up with 12345.55 EUR.
2. That's the point though. They will sell you a certain amount of EUR at a certain dollar price. This, in turn, implies a rate (which might well have more than 5 digits behind the decimal). This is ideally close to the quoted rate, sure. But what counts is the actual EUR amount and the actual USD amount, not what rate was quoted or with how many digits.
You store the sums on either end, the currencies, the exchange rate and the final sum? No one has .0000145 cents in their account. Rounding occurs in the real world.
> You store the sums on either end, the currencies, the exchange rate and the final sum?
There is a remarkable amount of disagreement as to whether one should do one’s back office work based on the price or based on the quantity of the counter currency.
> No one has .0000145 cents in their account. Rounding occurs in the real world.
Indeed. But you either need to convince all parties to agree to round the same way or you need to accept small errors
My experience in consumer banking says that every instrument specifies the precision of the calculation, how and when rounding happens, and slew of little details.
So, yes, everyone has to understand how all their partners are doing rounding and summing.
In certain areas of the institutional finance world, everyone seems to accept that everyone's math is allowed to differ by a few cents, and they tally up the errors and move on with their lives.
I would, however, by quite surprised if my personal bank account did this.
When the bank owes you money, they round down.
When you owe the bank money, they round up.
Unless you work for the bank, in which case your annual salary is divided by 12 and rounded up for a monthly figure.
The only one who has to “agree” is the exchange. And it should be covered by the TOS
Agree with this, working from HFT to payments to account management in the past.
You can have the blockchain team be an expert in converting integer cents, or the forex team be an expert in sub-cent conversions. You don't want to require _every team_ to have expertise in float math, by default.
Big decimals are widely available and don’t require any expertise but avoid many of the footguns of implied decimal integers.
BigDecimal should be used by almost everyone except for HFT since they're really slow.
When performance isn't a concern, I largely agree! Not every financial system can use big decimal as their base, though, too. And HFT isn't the only place in the financial sector where this performance concern might pop up.
Imagine advising someone who explicitly said they work in HFT to use big decimals.
If you’re only trading in USD and other two-decimal currencies it can work fine, yes. For anything else, it’s much worse as also detailed in TFA.
You provide different handling strategies for different currencies. You also sort currency data alongside your amount. There is nothing complicated or edge-case here.
This works for USD, JPY or $MEMECOIN and it scales very well.
> but it'll bite you incredibly hard if you ever stumble upon an edge case such as working with a partner that has a different implied number of digits for a given currency
Why would that be a problem? You just transform the values when interacting with their API.
Exactly, model is in integers and representation can be 1⃣3⃣ or whatever, that's why model-view separation exist.
Sure, you can do that if you can absolutely guarantee that everyone will always respect that separation and there will never be ambiguity between your internal and some partner's representation – even during incidents, even during low-level CSV-to-DB ETLs during incidents ("just one time, I promise, we don't have time to build the proper adapter, but look how similar their and our formats are").
Customer was charged $0.995 after fees, how to represent in your data model with integer cents?
You'll have to decide when and how to round. Keeping individual billing items at high precision and rounding after summing them up can work; defining and documenting a rounding policy (or complying with whatever's legally required in your jurisdiction/domain) and rounding each individual billed item can as well.
Currency: USD Amount: 99500 Decimals: 5
Congrats m8 you invented floating point
Then you have no idea how floating point works. (Hint: its in the name)
You use 1/1000th or 1/10000th or whatever you need. You do not need “cents”.
How is the client going to pay that? They can't be charged half a cent.
Round it up
Charge $0.995
Refund $1.00
Repeat
Charge $0.995
Charge Actual $1.00
Refund $1.000
Alternately
Charge $0.995
ERROR CHARGE AMOUNT MUST BE ROUNDED TO NEAREST CENT
In my scenario your payment gateway added a $0.005 fee. You told it $0.99.
Because a lot of the time there won’t be any error when you’re wrong, just silent data loss.
I’ve seen bugs like this in prod systems. The notional value of the error tends to make the people concerned anything but silent.
Sure, but are all your (and your users' and vendors') engineers and LLM agents going to remember that? When in doubt, always be explicit.
I'm curious how you handle that.
Let's say I operate with a 4 decimal expectation and your API expects 6, is there any way to reconcile that outside of documentation and or metadata ? (which would be the same issue I guess whatever representation is used ?)
Yeah, you need to document it.
Still, even if you do: Chances that your users are just going to assume you're conforming to ISO 4217, some national standard, or your competitor that they're already integrated with are pretty high, so I wouldn't take the chance. Pick something that doesn't have to be documented instead.
I think I’m agreeing with you whole-heartedly if I say that article’s conclusion is at best extreme and unrealistic when it says “the parsers need to be fixed. They should support extracting any numeric type we want from JSON numbers and at any precision.”
This sounds like an unreasonable position to take. “Any” is an unachievable standard that could require an unlimited engineering budget with no demonstrable value in practice.
It is good to identify the lack of a standard, and to talk about what parsers do in practice, and good to discuss the gaps and unmet use-cases. It would be a good idea to suggest that there should be a more reasonable standard, perhaps. It’s just not a good idea to demand that everyone support “any” possibility when no one really needs that, no one knows what it means, and it’s not actually possible to achieve.
What do you recommend instead? Standard floating-point ("float"/"double"), fixed-point arithmetic with thousandths (or smaller) of the minor unit, arbitrary-precision decimal numbers, or something else entirely?
I think what matters most is your database and API representation, as well as having consistent and well-defined rounding rules.
I largely agree with TFA: Round explicitly and consistently whenever you cross a boundary, i.e. database persistence and internal API calls.
Use whatever works for your required business case internally (i.e. inside of procedures calculating some function of one or more input amounts). This can be regular old floats/doubles if you absolutely know what you're doing, or BigDecimal if you aren't and would rather suffer slightly slower performance than having to talk to an auditor about IEEE 754 rounding modes, or even minor-amount integers (yes, even though I just said to not use them – but you'll want to ABSOLUTELY NEVER leak them outside of your system, including your data/analytics pipeline, which might have different ideas about financial amounts than your business logic implementing a nice custom monetary type).
A string type. As parent says: it completely bypasses the problem. Save the numbers between double quotes and be done with it.
Storing numbers as arrays of u8? That doesn't make sense
For JSON serialization, which doesn't support fixed-point precision it does.
Floating-point precision has too many gotchas for being suitable to store Decimal types, especially for the Currency use case.
Surely it does:
How is that better than {“amount”: “10.00”} (which also bypasses all potential floating point parsing issues that your or your counterparty’s JSON library might have)?
It is explicit about the fact that that number of decimal places is part of the data.
The semantics for your string “10.00” are complex - is it considered equal to “10”? To “10.000”? To “10.001”?
A user interacting with an API that uses such a string might make all sorts of assumptions about what it supports.
A user interacting with an API that has an explicit decimal places concept is being told ‘decimals matter! They can vary! Here be dragons!’
> The semantics for your string “10.00” are complex - is it considered equal to “10”?
Yes, but "10 USD" would be a non-canonical representation and you probably serialized incorrectly.
> To “10.000”?
Yes, but same caveat as above applies.
> To “10.001”?
Obviously not, and any system you'd ever want to use in a financial context will tell you so.
String and two-field exponent/mantissa representations are mostly the same in terms of semantics, yes. Making it two separate fields makes it less likely it would be put into `parseFloat`, but after doing some research I think strings are more popular in JSON [1, 2], so probably I’d stick to that as well.
[1]: https://msgspec.dev/supported-types#decimal
[2]: e.g. https://getlago.com/docs/api-reference/fees/fee-object#schem..., although they still use `amount_cents` for all currencies as the base rate
It makes a lot of sense if you value correctness over performance.
Why not store them in unary then?
Unary is exactly as expressive as decimal or binary for integers, but somewhat less efficient, so why would you?
idk, why would you store integers as ASCII strings? It's somewhat less efficient.
Because it's much more explicit. Computers are fast, engineering is expensive. You usually never want to optimize prematurely when dealing with monetary amounts.
Even more explicit would be a PNG of the dollar bills. I suggest that.
Except that now you have a new problem: Opinionated theorists that haven’t been part of a nasty “oh no, we accidentally considered some amounts as 10x/100x/1000x larger/smaller than expected” incident in their career yet…
Do not throw away any precision in finance/money computation, regardless what/ how you are doing it.
In C# e.g., there is type decimal for those computations.
You'll definitely have to throw it away at some point.
The art is in making those points well-defined and rare enough to not cause large discrepancies, but frequent enough to avoid ballooning arbitrary-precision numbers across databases and services that might not be able to handle them.
I really like that phrasing! Would you mind if I steal in some form if I decide to review this part of the book?
Not at all, and thanks for writing all of this up!
Floating point value stored multiplied by 10^8. That gives you a huge integer, but it's extremely accurate, especially for US denominated currencies. Easily transformed into floating point numbers for reporting/etc.
What is with this Twitter esque style of discussion? Post some vague comment with no real stake in the ground, but just reply to follow ups asking for clarifications about the right way. It's exhausting. Why not put all that effort into the initial comment?
Vague-posting seems to becoming more popular
If there were a simple one-size-fits-all solution to these problems, there wouldn't be a need for a handbook, nor for a discussion, would there?
I can't design everybody's systems here, but I was hoping that sharing some war stories that have cost me days or weeks of work might sensitize somebody to a few non-obvious footguns.
That strawman is so large it would even scare away a human
Nice. The book contains a bunch of good information that could already be found elsewhere but collecting it is quite practical. I highly suggest to read Kleppmann's Designing Data-Intensive Applications. The first edition was very good, a second one came out recently.
I was CTO of a FinTech where I built the whole software stack from scratch: the lessons in the book are mostly correct. I say mostly, because as always, there is a lot of "it depends" to take into consideration for your particular project. For example, I chose to not use event-sourcing to avoid the whole state computation issue. A standard append-only audit trail can do the job.
You can't guarantee exactly-once delivery but you can construct effectively-once processing, and that is what you really want.
Store every request and response : absolutely, and not only when consuming APIs, but when collecting any information from the outside world (and, if you can, also log every intermediate transformation step within your perimeter). Content-adressed buckets + a relational table are great for this.
The text also does not mention anything about data lineage. What happens if a vendor updates some data mid-day that you absolutely need to be aware of? You need to be able to account for that, while also re-playing computations that used the old values and get the same result. It's not a particularly hard problem to solve, but it takes some thought.
Why content-addressed storage? I'm assuming this is mostly for auditability after the fact? Wouldn't it make sense to use some business identifier to be able to refer it back easily?
You will need a combination of the two. The filename should contain a compound business identifier, essentially a tuple of appropriate facts, as well as a (truncated) hash of the content.
And yes, the reason is auditability but also bare necessity.
Imagine vendor A publishes fresh files every day, but you don't exactly know when. And suppose that the vendor can (and does) republish some of them at any time, with or without notice. You need to watch those files for changes and update your own sources accordingly.
For the collection and dedup, you can construct a tuple of meta-information that will give you a good idea if the file is the same (name, time last modified, file size, etc). Then only if you're still unsure do you download the file and hash it, compare with what you already have, and make a decision. (What if the file name changed slightly and now you must re-categorize the data in the file, or what if a single row was added or removed, etc).
It's also useful when auditing (tamper-proof) or when re-running or debugging calculations that used the data in question. If a user used the data available at 9AM, which was unfortunately incomplete and led to issues, having a full data lineage really helps. You can trivially re-run the computation with the information available at that time to see what happened. Or you can mark some results as stale, etc.
There are different ways to do this and one is keeping track of your content-adressed files in a relational table and give each row a unique identifier, so that you can tell that this particular row in your AAPL OHLCV data comes from this particular file which was fetched at this particular time etc, etc. Pair that ID with a timestamp and when doing computations you can query for "the latest" AAPL data while storing the exact file ID (time is fuzzy, content hash is not). Look into temporal tables and the like.
As a programmer, what I feel when I see fintech programmers each speaking from their own different experiences and perspectives is that it makes me wonder what it really means to be good at programming.
What user xlii said about not storing monetary amounts as floats is a common IEEE 754 issue. And while it's true that financial tracking should be done through immutable logs or event-based records, I don't think every surrounding service needs to be built with event sourcing. I think it's enough to apply it only to core logic like ledgers, settlements, orders, and executions. Looking at xlii's comment, it seems like a technique that only becomes viable when the modeling is successful.
User lxgr's comment points out that it's a minor-unit issue. If JSON numbers are parsed as floats by the language or parser, precision can be lost. Usually people send values with a separate decimal places field. However, I've heard that in HFT, they don't do that because the overhead itself is too costly.
And antonymoose's comment aligns with what many books say. That's why designs like this are common in FX or API contexts. It feels like protocol design, doesn't it?
Putting it all together, everyone's right within their own domain. While I think it'd be great to have someone like xlii as my senior programmer, I also feel like I wouldn't be able to design such a complex system myself. In that sense, everyone's statements are valid, and it's interesting to see how opinions diverge depending on the domain. Is this what expertise looks like
Looking at all this, it seems like you can roughly infer where a programmer is coming from based on their experience. Sometimes programming doesn't feel like finding the right answer, but more like choosing a worldview
Watching how programmers model their domains on HN is always fascinating. Sometimes I click on their profiles and add their domain knowledge to my own personal wiki, thinking I might use it someday
Fintech/finance is a very big industry and there are a lot of sub-domains. A HFT system programmer thinks about how to colocate their prod machines in NYSE and use zero alloc techniques to ingest marketdata as fast as possible, while a crypto wallet app developer spend more time designing cool UI to attract users and working out all the L1/L2 quirks. And I'm sure they have very different answers to whether to use integers and how many digits to preserve
> However, I've heard that in HFT, they don't do that because the overhead itself is too costly.
I've worked at several HFTs and 2 had independently settled on 64bit signed fixed point with a implied 10^9 scaling factor between internal systems.
However in-process one just used 'double' for all FX conversions, scaling etc. With 15 digits of precision and careful rounding choices it's fine.
Dealing with the outside world you'd obviously just convert as cheaply as possible - and there are some crazy fast algorithms doing fast binary float -> correctly rounded decimal conversions these days
A good programmer is just a bad programmer who’s learned not to make the same mistake twice. That’s part of expertise and why it differs too, everyone has made a different set of mistakes, domain-coded.
It's very true. As a merchant acquirer, everything is BigDecimal, only the auth path needs real performance, and FX occurs at a fixed rate per-day which is easy to reconcile.
It's fun reading all the different ways fintech has to adapt their systems for their specific niche.
I would like to hear more about this wiki you have.
I think most of this applies to software engineering generally, not just fintech.
For example the parts talking of retries, idempotency, event ordering, etc. This applies to all systems that require any degree of accuracy, even if no money is directly involved. I've seen so many systems built on the assumption that "we can always retry", but you can only retry if you fail cleanly in the first place, and if the downstream system offers the same level of idempotency that you think it does. Quite often these are not put to the test.
I agree. Very little in here specifically applies to fintech except the ledgering and rounding parts, which are pretty light.
I would prefer to read a defense of something more radical like "database per account." Something that has unique tradeoffs within fintech.
Also, the main advice I would give to fintech engineers/founders is to take risk and compliance seriously from day one.
Financial systems are based around trust. If you don't provably mitigate risks you will lose trust and, eventually, your entire business.
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> even bad advice
That's putting it politely. Honestly, I think this "handbook" was mostly written by an LLM.
For example, in the immutability section we have this:
In a financial organisation the two go hand-in-hand for obvious KYC/AML reasons.Keeping the financial data whilst trashing the customer names, addresses etc. instantly on-demand before the expiry of the relevant time periods is going to leave your entire organisation with a very bad day in the office if a $lawful_body comes knocking for the data to trace a crime.
People going to work in a Fintech should not be relying on a random "Handbook" written by an unknown person in an unknown jurisdiction.
People going to work in a Fintech should only ever work in accordance with their employer's internal handbooks/guidelines/etc which will have been written in conjunction with their firm's lawyers and compliance people to ensure it complies with the laws and reporting requirements in the jurisdiction(s) in which their employer operates.
> Keeping the financial data whilst trashing the customer names, addresses etc. instantly on-demand before the expiry of the relevant time periods [...]
Where does TFA recommend that?
As I see it, it recommends separating PII data you'll eventually have to delete from that you'd probably want to keep forever (including data factoring into your accounting equations/invariants), so that you can delete the former after the relevant recordkeeping periods have elapsed.
> People going to work in a Fintech should not be relying on a "Handbook" written by an unknown person in an unknown jurisdiction.
Sure, but they should also not blindly ignore any ideas and practices presented, or avoid looking beyond their own organization. Ideally, they'll then try to reconcile what they saw with their own knowledge and local regulations etc.
> People going to work in a Fintech should only ever work in accordance with their employer's internal handbooks/guidelines/etc which will have been written in conjunction with their firm's lawyers and compliance people to ensure it complies with the laws and reporting requirements in the jurisdiction(s) in which their employer operates.
Sure, in a world in with only perfect and error-free organizations, that seems like a reasonable approach. But how does one get there without having a conversation such as this one?
> any ideas and practices presented
Unless its your job to architect stuff, in a financial firm you don't go looking around for ideas and practices.
You comply with your employer's practices end of story.
If you like looking up ideas and other people's practices then a heavily regulated environment is probably not the place for you.
> how does one get there without having a conversation
"having a conversation" about new ideas/practices in a regulated firm will involve lawyers and the compliance department.
More than likely that "conversation" will be above most people's pay grade. So you're better off just not wasting your time and adhering to your employer's existing practices.
And for everyone else, its an expensive and high-friction conversation to have if you want to change existing practices.
"Not thinking, just complying" isn't the panacea for good outcomes you make it out to be. You definitely want to limit the amount of excitement, but I've seen many issues caused by legacy formats and practices as well.
> You comply with your employer's practices end of story.
What if you're the employer ("first engineer" etc.), and there are no practices yet? Fintech almost by definition sometimes includes doing things from scratch because some existing solution or incumbent organization isn't working that well anymore.
> Unless its your job to architect stuff
Which seems to be the target audience/scenario for TFA.
> What if you're the employer ("first engineer" etc.), and there are no practices yet?
In that scenario the practices will still come first. You're not going to be doing any coding or systems engineering until you've got compliance signed off. You're going to be spending lots of time with lawyers and compliance people.
> Fintech almost by definition sometimes includes doing things from scratch
Yes, but cut through the noise of the typical Fintech fancy website and app and you're still staring straight down the barrel of spending 80% of your time on regulatory compliance.
Try as you might there are only so many ways you can re-invent the wheel for dealing with hard-facts legislation.
Please show me the regulation that tells me whether to use big decimals or integers in my internal monetary amount representation. Regulations usually care about outcomes (sometimes high, sometimes low level); they often don't tell you how to technically achieve them.
And if your lawyers and compliance people are actually telling you that you can absolutely not do any financial processing yourself, that the only possible way to be compliant is to license <incumbent product xyz> (unfortunately only available in COBOL) etc., you might not actually be working in a fintech, or at least not in the kind this guide seems to be targeted to.
Frankly, this kind of attitude is exactly why banking and payments is as fossilized as it is in some countries, and why fintech is eating their lunch in many cases. There has to be a balance between trying new things and doing what everybody else is already doing.
This sounds great until your counterparties, banks, the government, customers and others complain bitterly that your monetary amounts are off.
Your expectations of course are not unusual because so many developers work on systems where users are not the customers, but the product.
Facebook, Insta, Google all fuck up results very regularly, but what are you gonna do when they do?
Now imagine your bank occasionally losing deposits, your account balance going up and down a percent or two every day, the IRS fining you for tax evasion because your Cool Fintech Rounding does not match generally accepted accounting rules.
> Money can’t be created out of nowhere
Well, in case of a bank it can. And it happens on a daily basis. In fact thats how most private bank money enters the system in the first place either when a bank makes a loan or when it buys any other asset like e.g. a corporate bond (you can conceptualize a loan as an asset purchase as well, the bank buys a promise to pay from the borrower which is recorded as an asset of the bank). Both are a balance sheet extension from a double-entry bookkeeping point of view.
The 4 operations double-entry bookkeeping allows are:
1. balance sheet extension (e.g. making a loan) 2. balance sheet reduction (e.g. loan repayment) 3. asset swap (e.g. a bank buying a government bond with central bank reserves) 4. liability swap (e.g. transferring money from one account to another)
So it may be more accurate to just say that every event that affects the balance sheet must be one of those 4 operations.
Also see "Money creation in the modern economy" - Bank of England Quarterly Bulletin 2014 Q1
> Well, in case of a bank it can. And it happens on a daily basis.
You mean it happens when a bank borrows money from a central bank, right?
Not every balance sheet expansion constitutes the creation of money, e.g. when you put money on your bank account, the bank's balance sheet grows, but the money is not newly created.
> You mean it happens when a bank borrows money from a central bank, right?
No I mean in the moment the bank grants a loan. The money the borrower receives is new money, its not taken from anywhere else. But at the same time the bank records the credit as an asset which makes it a balance sheet extension.
> when you put money on your bank account, the bank's balance sheet grows
That's true, you hand over cash to the bank which becomes its asset and your account balance is marked up for the same amount. Not all money in circulation is accounted on a bank balance sheet, but most of it is bank deposits. cash is an exception, it's public money, not private bank money. When you "put money in a bank account", i.e. you bring cash to the bank you are essentially selling your public government money to the bank and it pays you with its own private bank money (which it "creates" as well).
But this is only a micro-view of a single event. In the aggregate money is created when either the government runs a deficit or when banks make loans and money is destroyed when taxes are paid or loans are repaid.
Well, that's not money created out of nowhere. The balance sheet extension creates money and an equivalent liability. That's the point of the statement, not money supply and its regulation.
Fair enough, just wanted to point out that banks originate money as part of the lending process. You could maybe call that "money out of asset purchases" whatever that means and yes its a valid balance sheet operation. I guess you would agree that a bank needs no prior savings or deposits in order to make a loan, i.e. it doesn't need to have money that it "lends out"?
Or let me ask you this: What would in your opinion be a valid example of "money out of nowhere"?
some engineers and average person sees the entire fiat money as something that needs to be "earn" or get returns from investments only. they dont see how it's literally created as free and out of nothing.
Very true but also understandable because that's how most households perceive it from their micro-economic standpoint. But its very deceptive because it suffers from a fallacy of composition. If households want to earn more than they spend (i.e. save), some other entity must spend more than they earn. This can be somebody taking a mortgage or the government running a deficit. Debt and savings are mirror images of the same thing, two sides of the same coin and we use double-entry book keeping to track them.
It is very insightful to view money as a creature of accounting for debtor-creditor relations rather than thinking of it as some kind of commodity.
The idempotency keys section alone is worth the read most devs learn that lesson the hard way.
I just wish the financial industry itself had known about these when the core banking systems and financial communication protocols of the 60s and 70s were invented that are still being used to this day...
Many of these predate the widespread knowledge of idempotency, so often idempotency keys are hacked together by joining various, hopefully globally unique fields, except that they never quite are. (You can look behind the curtain sometimes, e.g. when your bank does not let you transfer the same amount to the same recipient account on the same calendar day.)
100%. It deserves more detail, too.
I've spent many hours explaining how idempotency is supposed to work, and why it's important. Most teams understand the need for it, but very few thought about it up front.
Also audit trails. Good audit trail can save company (and you) in emergency as well. Useful for debugging and last resort of compliance data source.
I just build the audit trails and skip the non audit trails.
That way I can debug and have a last resort of compliance, but also save time by not building the first resort of compliance.
A Plaid balance check is NOT a guarantee that the ACH debit you're about to submit will go through.
I don't care if the balance is one million, before that ACH can process, every single dollar can be (a) wired out, (b) cleared out by yesterday's ACHs (bills, autopay, whatever) and checks, or (c) spent at debit/ATM.
I probably shouldn't tell you why I know that some fintechs don't address this.
It's a good indicator but absolutely no guarantee. I've had to tell project managers this because they didn't understand this concept.
I think this is covered by the "overdraft" section, if the only way to know for sure is to just submit it.
All this debate about floats or ints or decimals, obscures the fact “reconciliation” is an essential part of dealing with money - essentially some separate implementation of the money movement that detects when your rounding strategy or floats end up creating or destroying money, but all the accounts balance at then of the day.
I have just left a fintech company after 5 years and I can say after reading this, it looks legit to me (not AI slop as someone asked). These are the same sort of lessons I learned during my time in the industry.
I would recommend anyone starting in fintech to take some time to understand accounting principles and the ledger in a bit more depth than just debits vs credits - this is likely what is most unfamiliar to programmers.
Also financial software is very data-heavy and I learned more about databases in my time working in fintech than the 15 years before that. I think going into a bit more detail about even the basics (indexes) will save a lot of headaches.
> I would recommend anyone starting in fintech to take some time to understand accounting principles and the ledger in a bit more depth than just debits vs credits
Any good resources you would recommend to learn more about this?
I've seen a few attempts at "accounting for programmers" guides, some of which I came across while browsing HN (if you search you'll find plenty). The one I used to send new developers was one such guide[1]. They also have another on building a ledger[2]. These are a good start and I wish I could recommend a proper textbook that goes into more detail - detail that I wish I had known early on - but I learned mostly as I went along.
[1] https://www.moderntreasury.com/journal/accounting-for-develo...
[2] https://www.moderntreasury.com/journal/how-to-scale-a-ledger...
TFA, in Appendix A: Resources, lists many.
> I would recommend anyone starting in fintech to take some time to understand accounting principles and the ledger in a bit more depth than just debits vs credits
Probably good advice (for everyone in fintech, not just programmers) considering the absolute disaster that happened at Synapse. Kind of wild nobody has gone to jail for that.
is there any technical article that describes shortfalls and mistakes made in Synapse?
I am curious as I had a bit of money in Yotta.
Similar style and message to https://shapeofthesystem.com/
Chunks of good advice, tossed and blended into a soup of verbiage generated by a helpful LLM. It sounds smooth and resists being read at the same time. I hope we can get to the point where actually good, helpful prose is generated more often than not.
I really enjoyed this. This year I joined a fintech company, coming from a non-financial domain. This is the kind of information that I had to learn from experience, other devs, and random blogs/articles. The fact that this is a centralized location is awesome! I wish I knew about this when I was onboarding.
Anyone know of resources like this but for capital markets? Things that would allow engineers new to trading equities, options, FX, bonds, and commodities to learn about different flows, market structure, common architectures, and other things that normally you learn from years of experience.
Equities and (listed) commodities are relatively easy to get a handle on but it genuinely takes months / years even at the frontline to understand how fixed income and FX works because its still almost entirely an OTC market. There is more central clearing than ever before but e.g. if I (say) buy a bond, fund it using a repo, swap my loot back some other currency, quite a lot of this could easily still be relying on humans pressing buttons and wiring money around.
To learn how and why these things are traded, however, read this book, the only (good) truly beginners guide to fixed income:
https://www.jdawiseman.com/books/pricing-money/Pricing_Money...
Even with equities, things are not entirely trivial.
A few examples: Some stocks trade in one currency, but pay dividends in a different currency. Some stocks go ex-div before the dividend amount has been determined (e.g. in Japan). Stocks trade on certain days, and trades settle on certain days, and they might be subject to different holiday calendars. Corporate actions are not entirely trivial, either (with rights issues, you need to issue two temporary securities for accounting purposes, etc.).
"Trading and exchanges" is the classic one everyone reads for listed markets, I should add.
I wanted to say thanks for this resource.
I'm founding a fintech startup and have been thinking about these things a lot. It is very helpful to have some general validation and guidance to make sure I'm not making huge mistakes.
I really wanted event sourcing, but thought it gets complicated when events have complex validation rules. I ended up with a mix of state and audit logs which works ok for now.
Does anyone have more learning resources in this field? Any model implementations, pet projects, anything to get going?
Huh, that's strange, where did all comments to this post go? I remember seeing three, then a few more the day after. Were they bots? They seemed legit recommendations, like the Pricing Money book.
I don’t work in fintech (yet) but I’ve studied finance recently and quite a lot of these pieces of advice are just intuitive when you know the business domain. Learning the “customer” of your software helps too
"Fintech" is extremely broad, and most of what gets called "Fintech" is really communication. Communication between firms, between traders, between systems, between ledgers, etc. There is no industry-wide "right" way to program anything, because the right way is ultimately whatever way the other party you're communicating with will understand.
If you're dealing with a party who tracks currency in cents, then tracking currency with more precision than that is going to lead to rounding disagreements. Vice versa if you deal in cents but they deal in tenths of cents. And so on for all the other advice in this document.
Does fintech here mean "crypto" and central bank currencies transactions?
> Webhooks are the most common way to receive signals from external systems, but processing them safely is not trivial
I see webhooks documented all the time, but I have yet to use them in practice, nor have my customers requested them. Is the above not true, or are they widely used in some sectors and not others?
In payment gateway integration, webhooks are usually considered a single source of truth for updating the payment status, with status check api as a fallback.
They are indeed everywhere, but it's possible you don't have a need for them. For example, every time you buy something online using Stripe, the seller receives a webhook indicating that a purchase was completed.
I receive webhooks all the time as ack that something was processed/ or a notification of the status of some sort of thing in an external system that we don't control.
The whole avoid floats thing just isn't true. I have 20years experience in fintech and most of it used doubles. Excel uses doubles. Your frontend will use doubles. All your db supports doubles. Your stdlib knows how to parse doubles. Json uses doubles (not in theory but in practice). Many ERP system uses doubles
The thing for working with currency with doubles is that you have to keep in mind that it can hold 15 digits of precision in total. As long as your numbers don't use more digits than that, like 123456789.01 or 123.456789, you can have perfect decimal precision in your financial math. You just have to always round the result to within 15 digits of precision after each computation, and before each comparison. That's what excel does.
The biggest advantage of doubles is that 1) they're widely supported and 2) you can mix different precision in your system, which will appear if you do international finance or advanced financial products. Some accounting require precision up to the thousandth, some need to be rounded to multiples of 0.25. So at the end of the day you'll never use basic math but some specialised accounting math library and that library can perfectly use float as a backend.
I think his experience in the crypto space is part of the reason why he is against it.
Thank you. It came at a much needed time.
> For whom?
It's refreshing to see someone using the correct phrasing.
The often-seen, stupid way is 'who is this book for'.
First half didn’t sound so bad.
Thank you so much for this. It came at the needed time
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Hey, author here. Happy to take feedback or answer questions.
P.S. I have no clue how HN works, I posted it myself yesterday and it got 6 points. ¯\_(ツ)_/¯ Anyway, glad for the reach.
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Sorry have to ask these days. Is this carefully written down information from years of experience in the field or AI slop?
Appears that the author got some help organizing the document, but wrote it all themselves.
Hey, author here :)
Its at least 80% organic artisanal writing and maybe 20% AI when I needed help with grammar, completeness, broader perspective and everything around.
It may be a good idea to start the book with a really short "About the author" to state exactly this and your work experience. Otherwise looks well written to me, good job! :)
Native English speaker. I scanned it and IMO there's a slight overuse/misuse of hyphens. Maybe the AI tool could be asked to identify and correct? (The hyphens might be triggering people to think it's AI, too).
They mostly need replacing with a full stop or a colon.
E.g.
"In practice this means storing the amount as an integer in its smallest unit - €12.34 becomes 1234"
->
"In practice, this means storing the amount as an integer in its smallest unit: €12.34 becomes 1234."
or
"In practice, this means storing the amount as an integer in its smallest unit (e.g., €12.34 becomes 1234)"
Hyphens here are meant to be formatting. You would be correct if this was a literary piece, but handbooks and sheets don't need to use these rules.
It's a misuse because in this context the hyphen could be mis-construed as a negative amount, and this causes the reader to stop and carefully re-read the content to ensure that they're not misinterpreting it. That's not where you want to be spending your reader's attention.
Spelling, grammar and punctuation matter everywhere, in my opinion.
I wouldn't have read your comment if it were all lowercase and used zero punctuation, for example.
Skimmed it and based on my experience in fintech, it looks good, accurately represents the real world. I guess there’s still a chance it is AI generated but it doesn’t seem like vacuous slop, it has substance!
I worked for a few years in consumer banking, and this looks like solid advice.
Whilst I wouldn't say anything in it requires years of experience to know, this would be helpful for someone who hasn't considered anything about monetary systems. It doesn't read like slop, but I could be wrong but even so it all seems fairly reasonable (I've only fully read about 50% before realising there's nothing new here for me, and then skimmed to rest).
from the author's mastodon post [0]
other than that, peruse the commits on the source [1], or wait for the author to respond.[0]: https://mas.to/@krever/116814803588993437
[1]: https://github.com/Krever/fintech-engineering-handbook/commi...