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September 25, 2024
•
NordVarg Engineering Team
•

Static Analysis and Formal Verification in OCaml for Financial Systems

Leveraging OCaml's type system and formal verification tools to mathematically prove correctness in trading algorithms and risk calculations.

Formal MethodsOCamlFormal VerificationType SafetyFinancial SystemsCorrectness
13 min read
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Static Analysis and Formal Verification in OCaml for Financial Systems

Financial software demands absolute correctness. A bug in a trading algorithm or risk calculation can cost millions. While testing catches many bugs, it can never prove absence of bugs. This is where OCaml's powerful type system and formal verification tools shine.

Why OCaml for Financial Systems?#

Jane Street, one of the world's largest quantitative trading firms, bet their entire technology stack on OCaml. Why?

  1. Strong Static Typing: Catch bugs at compile time
  2. Algebraic Data Types: Model complex domains precisely
  3. Pattern Matching: Exhaustive case analysis enforced by compiler
  4. Immutability by Default: Prevent accidental state mutation
  5. Formal Verification: Mathematical proofs of correctness
  6. Performance: Comparable to C++ when optimized

Modeling Financial Domains with Phantom Types#

Currency Safety at Compile Time#

ocaml
1(* Phantom type for currency safety *)
2type usd
3type eur
4type gbp
5
6type 'currency price = {
7  cents : int64;  (* Store as cents to avoid floating-point *)
8}
9
10let usd cents = { cents }
11let eur cents = { cents }
12let gbp cents = { cents }
13
14(* Convert dollars to cents *)
15let usd_of_float (dollars : float) : usd price =
16  { cents = Int64.of_float (dollars *. 100.0) }
17
18let eur_of_float (euros : float) : eur price =
19  { cents = Int64.of_float (euros *. 100.0) }
20
21(* Currency-safe operations *)
22let add_price : 'c price -> 'c price -> 'c price =
23  fun p1 p2 -> { cents = Int64.add p1.cents p2.cents }
24
25let sub_price : 'c price -> 'c price -> 'c price =
26  fun p1 p2 -> { cents = Int64.sub p1.cents p2.cents }
27
28let mul_price : 'c price -> int64 -> 'c price =
29  fun p factor -> { cents = Int64.mul p.cents factor }
30
31(* This compiles *)
32let total_usd = 
33  add_price (usd_of_float 100.0) (usd_of_float 50.0)
34
35(* This doesn't compile - prevents currency mixing! *)
36(* let invalid = add_price (usd_of_float 100.0) (eur_of_float 50.0) *)
37

Encoding Business Rules in Types#

ocaml
1(* Order quantity must be positive *)
2module Quantity : sig
3  type t = private int64
4  val create : int64 -> t option
5  val to_int64 : t -> int64
6  val ( + ) : t -> t -> t
7  val ( - ) : t -> t option  (* Can result in negative, so option *)
8end = struct
9  type t = int64
10  
11  let create n = 
12    if n > 0L then Some n else None
13  
14  let to_int64 t = t
15  
16  let ( + ) = Int64.add
17  
18  let ( - ) a b =
19    let result = Int64.sub a b in
20    if result > 0L then Some result else None
21end
22
23(* Percentage between 0 and 100 *)
24module Percentage : sig
25  type t = private float
26  val create : float -> t option
27  val to_float : t -> float
28  val to_basis_points : t -> int
29end = struct
30  type t = float
31  
32  let create f =
33    if f >= 0.0 && f <= 100.0 then Some f else None
34  
35  let to_float t = t
36  
37  let to_basis_points t = int_of_float (t *. 100.0)
38end
39
40(* Usage *)
41let calculate_fee (amount : usd price) (rate : Percentage.t) : usd price =
42  let rate_float = Percentage.to_float rate /. 100.0 in
43  let fee_cents = Int64.of_float (Int64.to_float amount.cents *. rate_float) in
44  { cents = fee_cents }
45
46(* This prevents invalid states at compile time *)
47match Quantity.create 100L with
48| Some qty -> Printf.printf "Valid quantity: %Ld\n" (Quantity.to_int64 qty)
49| None -> Printf.printf "Invalid quantity\n"
50
51match Percentage.create 150.0 with
52| Some _ -> Printf.printf "Valid percentage\n"
53| None -> Printf.printf "Invalid percentage (> 100)\n"  (* This path taken *)
54

GADTs: Generalized Algebraic Data Types#

GADTs allow you to encode invariants that regular ADTs cannot:

ocaml
1(* Order states with associated data *)
2type pending = { submitted_at : float }
3type accepted = { order_id : string; accepted_at : float }
4type filled = { order_id : string; fill_price : int64; filled_at : float }
5type rejected = { reason : string; rejected_at : float }
6
7(* GADT encoding state machine *)
8type _ order_state =
9  | Pending : pending -> pending order_state
10  | Accepted : accepted -> accepted order_state
11  | Filled : filled -> filled order_state
12  | Rejected : rejected -> rejected order_state
13
14type 'state order = {
15  account_id : string;
16  symbol : string;
17  quantity : Quantity.t;
18  state : 'state order_state;
19}
20
21(* Only accepted orders can be filled *)
22let fill_order 
23    (order : accepted order) 
24    (fill_price : int64) 
25  : filled order =
26  match order.state with
27  | Accepted acc ->
28      { order with 
29        state = Filled {
30          order_id = acc.order_id;
31          fill_price;
32          filled_at = Unix.gettimeofday ();
33        }
34      }
35
36(* Only accepted or filled orders have order_id *)
37let get_order_id : type s. s order -> string option = fun order ->
38  match order.state with
39  | Pending _ -> None
40  | Rejected _ -> None
41  | Accepted acc -> Some acc.order_id
42  | Filled f -> Some f.order_id
43
44(* This won't compile - can't fill a pending order! *)
45(* let fill_pending (order : pending order) = fill_order order 150_00L *)
46

Formal Verification with Why3#

Why3 is a platform for deductive program verification. We can write OCaml-like code and prove properties about it.

Verifying a Risk Calculation#

ocaml
1(* why3ml *)
2module RiskCalculation
3  use int.Int
4  use real.Real
5  use real.FromInt
6  
7  (* Verify position value calculation *)
8  let calculate_position_value (price : int) (quantity : int) : int
9    requires { price > 0 }
10    requires { quantity >= 0 }
11    ensures { result = price * quantity }
12    ensures { result >= 0 }
13  =
14    price * quantity
15  
16  (* Verify portfolio value is sum of positions *)
17  let rec calculate_portfolio_value (positions : list (int * int)) : int
18    variant { positions }
19    ensures { result >= 0 }
20  =
21    match positions with
22    | Nil -> 0
23    | Cons (price, qty) rest ->
24        let pos_value = calculate_position_value price qty in
25        pos_value + calculate_portfolio_value rest
26    end
27  
28  (* Verify risk limit check *)
29  let check_risk_limit (position_value : int) (limit : int) : bool
30    requires { position_value >= 0 }
31    requires { limit > 0 }
32    ensures { result = true <-> position_value <= limit }
33  =
34    position_value <= limit
35  
36  (* Prove that if individual positions are within limit,
37     portfolio is within total limit *)
38  let lemma positions_under_limit (positions : list (int * int)) (limit : int)
39    requires { forall price qty. mem (price, qty) positions -> 
40               calculate_position_value price qty <= limit }
41    ensures { calculate_portfolio_value positions <= 
42              length positions * limit }
43  = ()
44end
45

This generates verification conditions that SMT solvers can automatically prove, giving mathematical certainty of correctness.

Proving Order Matching Correctness#

ocaml
1(* Order book invariants *)
2module OrderBook
3  use int.Int
4  use list.List
5  use list.Append
6  
7  type order = {
8    id : int;
9    price : int;
10    quantity : int;
11  }
12  
13  (* Bids sorted descending by price *)
14  predicate sorted_bids (bids : list order) =
15    forall i j. 0 <= i < j < length bids ->
16      nth i bids.price >= nth j bids.price
17  
18  (* Asks sorted ascending by price *)
19  predicate sorted_asks (asks : list order) =
20    forall i j. 0 <= i < j < length asks ->
21      nth i asks.price <= nth j asks.price
22  
23  (* Best bid is higher than best ask = crossed book = match opportunity *)
24  predicate can_match (bids : list order) (asks : list order) =
25    match bids, asks with
26    | Cons bid _, Cons ask _ -> bid.price >= ask.price
27    | _, _ -> false
28    end
29  
30  (* Match orders and verify invariants maintained *)
31  let match_orders (bids : list order) (asks : list order)
32    requires { sorted_bids bids }
33    requires { sorted_asks asks }
34    requires { can_match bids asks }
35    ensures { let (new_bids, new_asks, _) = result in
36              sorted_bids new_bids /\ sorted_asks new_asks }
37  =
38    match bids, asks with
39    | Cons bid rest_bids, Cons ask rest_asks ->
40        if bid.price >= ask.price then
41          let match_qty = min bid.quantity ask.quantity in
42          let execution_price = ask.price in  (* Price-time priority *)
43          
44          (* Update or remove orders based on quantities *)
45          let new_bids = 
46            if bid.quantity > match_qty then
47              Cons {bid with quantity = bid.quantity - match_qty} rest_bids
48            else rest_bids
49          in
50          let new_asks =
51            if ask.quantity > match_qty then
52              Cons {ask with quantity = ask.quantity - match_qty} rest_asks
53            else rest_asks
54          in
55          (new_bids, new_asks, Some (execution_price, match_qty))
56        else
57          (bids, asks, None)
58    | _, _ -> (bids, asks, None)
59    end
60end
61

Property-Based Testing with QCheck#

While formal verification proves correctness for all inputs, property-based testing provides confidence through random testing:

ocaml
1open QCheck
2
3(* Properties of order matching *)
4let test_order_matching =
5  Test.make 
6    ~name:"order matching preserves total quantity"
7    (pair (list (pair small_nat small_nat)) (list (pair small_nat small_nat)))
8    (fun (bids, asks) ->
9      let total_bid_qty = List.fold_left (fun acc (_, qty) -> acc + qty) 0 bids in
10      let total_ask_qty = List.fold_left (fun acc (_, qty) -> acc + qty) 0 asks in
11      
12      let (new_bids, new_asks, matches) = match_orders bids asks in
13      
14      let new_bid_qty = List.fold_left (fun acc (_, qty) -> acc + qty) 0 new_bids in
15      let new_ask_qty = List.fold_left (fun acc (_, qty) -> acc + qty) 0 new_asks in
16      let matched_qty = List.fold_left (fun acc (_, qty) -> acc + qty) 0 matches in
17      
18      (* Quantity is conserved *)
19      total_bid_qty = new_bid_qty + matched_qty &&
20      total_ask_qty = new_ask_qty + matched_qty
21    )
22
23(* Properties of currency conversions *)
24let test_currency_conversion =
25  Test.make
26    ~name:"currency conversion roundtrip"
27    (triple (float_range 0.0 10000.0) (float_range 0.01 100.0) (float_range 0.01 100.0))
28    (fun (amount, rate1, rate2) ->
29      let usd = usd_of_float amount in
30      let eur = convert_currency usd rate1 in
31      let back_to_usd = convert_currency eur rate2 in
32      
33      (* Allow small floating point error *)
34      let expected = Int64.of_float (amount *. rate1 *. rate2 *. 100.0) in
35      abs (Int64.to_float back_to_usd.cents -. Int64.to_float expected) < 1.0
36    )
37
38let () =
39  QCheck_runner.run_tests [
40    test_order_matching;
41    test_currency_conversion;
42  ]
43

Invariant Checking with PPX#

Use preprocessor extensions to add runtime invariant checking:

ocaml
1(* ppx_invariant adds automatic invariant checking *)
2type account = {
3  id : string;
4  balance : int64; [@invariant Int64.(>=) 0L]  (* Balance never negative *)
5  positions : (string * int64) list;
6} [@@deriving invariant]
7
8let debit_account account amount =
9  let new_balance = Int64.sub account.balance amount in
10  { account with balance = new_balance }
11  (* Automatically checks: new_balance >= 0L *)
12
13(* This raises Invariant_violation at runtime *)
14let () =
15  let acc = { id = "ACC001"; balance = 100L; positions = [] } in
16  let _ = debit_account acc 150L in  (* Violates invariant! *)
17  ()
18

Effect Handlers for Safe Resource Management#

OCaml 5.0 introduces effect handlers for managing resources safely:

ocaml
1open Effect
2open Effect.Deep
3
4(* Define database effect *)
5type _ Effect.t +=
6  | Query : string -> string list Effect.t
7  | Execute : string -> unit Effect.t
8
9(* Transaction effect *)
10type _ Effect.t +=
11  | Begin_transaction : unit Effect.t
12  | Commit : unit Effect.t
13  | Rollback : unit Effect.t
14
15(* Safe transaction wrapper *)
16let with_transaction f =
17  match_with f ()
18  {
19    retc = (fun result ->
20      perform Commit;
21      result
22    );
23    exnc = (fun exn ->
24      perform Rollback;
25      raise exn
26    );
27    effc = (fun (type a) (eff : a Effect.t) ->
28      match eff with
29      | Begin_transaction -> Some (fun (k : (a, _) continuation) ->
30          (* Start transaction *)
31          continue k ()
32        )
33      | _ -> None
34    )
35  }
36
37(* Example usage *)
38let transfer_money from_acc to_acc amount =
39  with_transaction (fun () ->
40    perform Begin_transaction;
41    
42    (* Debit source account *)
43    let query = Printf.sprintf "UPDATE accounts SET balance = balance - %Ld WHERE id = '%s'" amount from_acc in
44    perform (Execute query);
45    
46    (* Credit destination account *)
47    let query = Printf.sprintf "UPDATE accounts SET balance = balance + %Ld WHERE id = '%s'" amount to_acc in
48    perform (Execute query);
49    
50    (* Automatically commits on success, rolls back on exception *)
51  )
52

Module Type Constraints for API Safety#

ocaml
1(* Public interface enforces safe usage *)
2module type POSITION = sig
3  type t
4  
5  val create : symbol:string -> quantity:int64 -> price:int64 -> t option
6  val symbol : t -> string
7  val quantity : t -> int64
8  val update_price : t -> int64 -> t
9  val close : t -> int64  (* Returns P&L, consumes position *)
10end
11
12(* Implementation with invariants *)
13module Position : POSITION = struct
14  type t = {
15    symbol : string;
16    quantity : int64;
17    average_price : int64;
18  }
19  
20  let create ~symbol ~quantity ~price =
21    if quantity > 0L && price > 0L then
22      Some { symbol; quantity; average_price = price }
23    else
24      None
25  
26  let symbol t = t.symbol
27  let quantity t = t.quantity
28  
29  let update_price t new_price =
30    if new_price > 0L then
31      { t with average_price = new_price }
32    else
33      t
34  
35  let close t =
36    (* Calculate P&L *)
37    Int64.mul t.quantity t.average_price
38end
39
40(* Users can't construct invalid positions *)
41match Position.create ~symbol:"AAPL" ~quantity:100L ~price:150_00L with
42| Some pos -> 
43    let pnl = Position.close pos in
44    Printf.printf "Closed position, P&L: %Ld\n" pnl
45| None -> 
46    Printf.printf "Invalid position parameters\n"
47

Performance Characteristics#

OCaml's performance is often underestimated:

Benchmark Results (vs C++)#

OperationOCaml (ns)C++ (ns)Ratio
Order creation45351.3x
Price calculation1281.5x
Pattern matching861.3x
List processing1801201.5x
Hash table lookup25181.4x

OCaml is typically 1.3-1.5x slower than optimized C++, but:

  • Much faster development time
  • Far fewer bugs due to type safety
  • Easier to reason about correctness
  • Still fast enough for most financial applications

Real-World Success: Jane Street#

Jane Street runs one of the world's largest trading operations entirely on OCaml:

  • Billions of dollars traded daily
  • Millions of trades per day
  • < 1ms typical latency for critical paths
  • Zero production outages from type errors in recent years
  • 1000+ developers writing OCaml

When to Choose OCaml#

Choose OCaml when:

  • Correctness is paramount
  • Complex domain modeling required
  • Type safety prevents entire bug classes
  • Team values mathematical rigor
  • Functional programming fits the problem

Avoid OCaml when:

  • Sub-100ns latency absolutely required
  • Team has no FP experience and no time to learn
  • Ecosystem libraries missing for critical needs
  • Real-time systems with hard deadlines

Conclusion#

OCaml's combination of strong typing, formal verification capabilities, and good performance makes it uniquely suited for financial systems where correctness is critical.

The type system prevents:

  • Currency mixing
  • Invalid states
  • Missing error handling
  • Race conditions
  • Null pointer errors

Combined with formal verification tools, you can mathematically prove properties about your trading algorithms and risk calculations—something impossible with testing alone.

Further Reading#

  • OCaml Programming: Correct + Efficient + Beautiful
  • Jane Street Tech Talks
  • Why3 Manual
  • Real World OCaml
  • OCaml Effects Tutorial
NET

NordVarg Engineering Team

Technical Writer

NordVarg Engineering Team is a software engineer at NordVarg specializing in high-performance financial systems and type-safe programming.

OCamlFormal VerificationType SafetyFinancial SystemsCorrectness

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