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Modeling Financial Concepts

Functional Domain Modeling (FDM) is all about capturing business concepts in a precise, unambiguous, and processable manner. Those coming from a coding background will be comfortable with its use of first class programming language. There will be some differences from general purpose languages, and especially object-oriented languages, that are more focused on execution than capturing business concepts. This tuturial reviews how we can capture common business concepts concisely and expressively with Morphir. Morphir uses Elm as its main FDM language of choice, so we'll demonstrate the concepts with Elm examples.

Using The Language Of The Business

FDM relies heavily on Domain Driven Design, which has the concept of the ubiquitous language: making sure that the business and technology stakeholders establish a consistent language to describe the business. The best way to do this is to ensure that the code uses that language directly with no translation. Morphir makes this easy by being very friendly for labeling data in the form of types.

We can use a standard trade as an example. When the business talks about trading, they probably refer to the trade's quantity, price, and value as Trade Quantity and Trade Price, and Trade Value. So we want to capture those concepts in our code. As technologists, we know that these fields instances of integers and decimals. While we might be tempted to declare them as such, it would be better if we could use the business language. And this is how we want to approach modeling these concepts in Morphir. Elm has the concept of type aliases, so we'll take advantage of that.

type alias TradeQuantity = Int

type alias TradePrice = Decimal

type alias TradeValue = Decimal

Now when we see these values in our code, we know they map directly to the business concepts that the users know them as. For example:

value : TradeQuantity -> TradePrice -> TradeValue
value quantity price =
(ToDecimal quantity) * price

This bit of Elm defines a function that takes a TradeQuantity and TradePrice and calculates the TradeValue. This simple act is incredibly effective in avoiding misunderstandings and mistakes.

Keeping Incompatible Concepts Separate

In our trade example, we aliased TradePrice and TradeValue as aliases of Decimal. That means that the compile considers them to be of the same Decimal type, so anything you can do with Decimals can be done with these two types. That's useful in some ways, like applying functions like absolute value. In other ways, we don't want to mix the two. For example, what does it mean to do TradePrice + TradeValue? From a business perspective, this makes no sense, so we shouldn't be able to do it in code. Morphir has the concept of Units of Measure, which you might have seen as a first class concept in F#. In Elm it's implemented via a mechanism called Phantom Types. So we probably want to implement some core types of Value and Price.

type Price a = Price Decimal

type Quantity a = Quantity Int

type Value a = Value Decimal

This will ensure that we don't mix Price, Quantity, and Value in invalid ways. There are a lot of concepts in finance that we might want to approach this way. For example, we'd never want to accidentally use prices in different currencies without normalizing them. The same goes for things like fixed rate versus variable rate loans.

Categorizing Things

Categorizing stuff is one of the most common actions in enterprise development. Finance is full of categorizing, like categorizing trades, accounts, and products into different sets. This often gets very complex and categorization is usually highly contextual, meaning the same things might get categorized differently across different parts of the business. This has historically been a major source of complexity in finance. One of the main causes is the fact that categorization is usually approached up front, meaning the categories are decided first and then the things are created directly into those categories. This limits later flexibility for different contexts and often creates incredibly complex graphs of categories. In practice, we usually see this with object-oriented systems where the base classes or traits are decided first and then the specific things are specified with classes that extend them.

Functional Domain Modeling takes a different approach to these challenges. In FDM, the structure of things are defined first and only put into categories in contexts as needed. This simplifies the modelers' jobs by alleviating the need to account for all possible use cases up front. The tradeoff is that it requires mapping into categories later.

Let's look at an example. A classic financial modeling exercise is to model various financial products. For this example, let's take a subset of a Treasury function since Treasury needs to work across asset classes. The standard object-orient approach is to start with something like this:

public interface Product {
public String getISIN();
public String getCusip();
public String getSedol();
// ...

public class Stock {
private final String cusip;

public Stock(String cusip) {
this.cusip = cusip;

public String getISIN() {
return null;

public String getCusip() {
return this.cusip;

public String getSedol() {
return null;


Data sets

As we model data we come across a few established patterns, which we'll discuss below. These are general guideliness that should be balanced with the context of the project.

Infrequently changing limited values

This category describes a finite set of infrequently changing data. Some good examples of this type are days of the week, months of the year, ISO country codes, and currency codes. Often, programmers will want to refer to these directly without the possibility of mistakes. The standard approach to specifying these values is to use an enum. The functional way tends to focus on discriminated union types. In Elm, this looks like:

type DayOfWeek = Sunday | Monday | Tuesday | Wednesday | Thursday | Friday | Saturday

isWeekend : DayOfWeek -> Bool
isWeekend day =
day == Saturday || day == Sunday

Frequently changing limited values

This category describes a finite set of frequently changing data. A good example of this might something like pizza toppings, where there's a core set of common toppings and frequent changes based on market conditions. In theory, the frequency of change shouldn't affect how we model. It would be great to model these as enums as well and rely on continuous delivery to get that into production. In practice, many environments are not agile enough so rely on the database. So the common approach is to define these as such:

type alias PizzaTopping = String
type Topping 
= Cheese
| Pepperoni
| Mushrooms
| Ricotta
| Sausage
| Other String

isVegetarian topping =
not (topping == Pepperoni || topping == Sausage)

toppingToString : Topping -> String
toppingToString topping =
case topping of
Cheese -> "Cheese"
Pepperoni -> "Pepperoni"
Mushrooms -> "Mushrooms"
Ricotta -> "Ricotta"
Sausage -> "Sausage"
Other s -> s

Unlimited values

This category describes a set of data that is unbounded for all practical purposes. The most common approach to these is simply to define a type alias:

type alias Name = String

A more type-safe approach is to define it with a type to prevent accidental use across unrelated fields:

type Name = Name String

hi : Name -> String
hi (Name name) =
"Hi " ++ name

Custom Types

When you have something in your domain that you can describe as a list of alternatives (X can either be A or B or C or ...) you probably want to model it as a custom type. A simple special case of this is enumerations. For example a Trade's Status can either be Open or Closed:

type TradeStatus
= Open
| Closed

Custom types are more flexible than enumertaion though because they allow you specify different structures for each case. For example an Order's Price can either be Market or Limit:

type OrderPrice
= Market
| Limit Price

Notice that a market price doesn't have a specific value associated with it since it moves with the market but a limit price would have a specific price limit. In order to take full advantage of this flexibility you should use a pattern match to branch out on the various cases.

Special case: enumerations

As mentioned above a special case of custom types is enumerations where each case is just a constructor with no type assigned:

type RAG
= Red
| Amber
| Green

Encoding enums

in many cases enums have well known n-letter codes that are used to store and transfer the values more efficiently. While this is not strictly part of the business logic this happens so frequently that it usually makes sense to include them in your model. Here's how you would do that for the RAG type above:

toCode : RAG -> String
toCode rag =
case rag of
Red ->

Amber ->

Green ->

fromCode : String -> Result String RAG
fromCode string =
case string of
"R" ->
Ok Red

"A" ->
Ok Amber

"G" ->
Ok Green

other ->
Err other

Notice that fromCode returns a Result. This is a way to explicitly say that the operation may fail. Since you can pass in any string to the function it should fail if it's not a valid code for your type.

Special case: Wrapper types

An important special case of custom type is when you have a single case. With this approach you can wrap another type to hide it from the rest of the model. For example you could wrap a String into a Cusip type to communicate the fact that you are expecting a special kind of string:

type Cusip = Cusip String

You can still use any string as Cusip for testing but you have to explicitly wrap it which makes it safer to use:

doSomething (Cusip "123456789")