FIX Engine Model
The development of the FIX Engine model began at Imandra Inc. (AI) as part of the Imandra Markets product. The model consists of an executable formalisation of an administrative-level FIX engine, a sample application-level model and a set of verified precise statements about the engine's behaviour. The properties we verify are derived from the English prose specification published by the FIX Community.
There's an explicit interface between administrative and application models to make it easier to generate the application-level logic from a higher-level language (e.g., AI's Imandra Protocol Language or FIX Orchestra).
We hope this project will be the beginning of an industry-wide effort to mathematically formalise the rules and algorithms that run the financial markets. This formalisation will significantly benefit the industry, cutting down time and costs for connecting (and ensuring regulatory compliance of) the numerous systems relying on this protocol.
This README will give you a brief overview of the project. For further information please see the documentation page at https://docs.imandra.ai/fix-engine/
Having a complete formal model of the FIX protocol brings many benefits. To name a few:
Consensus: With the formal model, the industry has a precise, unambiguous foundation for describing, evaluating and improving the definitions and mechanics of the protocol.
Queryable: A formal verification engine like Imandra can be used to ask and answer deep questions about the behaviour of the model automatically.
Testing: Powerful model-based testing techniques can be used to automatically generate high-coverage test suites (see Test Suite section below).
Executable: Outside of the Imandra-specific commands used in verification, the model itself is all valid OCaml code. You may use the full OCaml stack to compile and run the engine.
Exportable: Using the OCaml stack, the model can be compiled into a number of target languages and library formats. The community can use the verified model to generate source code in other languages (e.g. C++ or Java) targeting specific proprietary (or open source) libraries.
Simulation: The model can be used as the basis of simulation environments, using the verified engine as a semantic "gold-standard" and test oracle.
The model is written in two languages: OCaml and IML. OCaml is a functional language that is gaining tremendous momentum within the financial services industry. For more information, please see www.ocaml.org. For example, Jane Street a well-known market participant is a strong supporter of the language, with an active blog.
IML stands for Imandra Modelling Language - it is a pure subset of OCaml for which we have developed a mechanised formal semantics. This means that everything written in IML can be given a precise axiomatic (i.e., mathematical) representation, and Imandra can then be used to analyze and answer deep questions about the code. Although Imandra is proprietary and requires a license to use, OCaml is open source and there are a number of open source theorem provers which may be adapted to reason about this model. For further information on theorem provers, see Formal Verification section below.
The model is currently targeting version 4.4 of the FIX protocol. Over time, we plan to cover other relevant versions. View the technical documentation for further information.
In addition to the model source code, you will find a collection of Verification Goals (VGs). VGs are statements in IML expressing properties of the FIX engine model that we wish to verify. It's important to note that IML is used for both building the model and describing the properties we wish to verify about it.
Consider the following quote from Volume 2 of the FIX 4.4 specification:
When either end of a FIX connection has not sent any data for [HeartBtInt] seconds, it will transmit a Heartbeat message.
One way to formalise that statement is to create two VGs:
- VG.1.1 - any outbound message will result in an updated
- VG.1.2 - any time update will result in check whether Heartbeat should be sent out
(* VG.1.1 *) verify last_time_data_sent_gets_updated ( engine : fix_engine_state ) = let engine' = one_step ( engine ) in let cond = begin engine.outgoing_fix_msg = None && engine'.outgoing_fix_msg <> None && engine.fe_curr_mode <> Retransmit end in cond ==> (engine'.fe_last_time_data_sent = engine'.fe_curr_time ) ;; (** VG.1.2 *) let outbound_msg_heartbeat ( m : full_top_level_msg option )= match m with | Some ( ValidMsg vmsg ) -> begin match vmsg.full_msg_data with | Full_FIX_Admin_Msg admin_msg -> begin match admin_msg with | Full_Msg_Heartbeat _ -> true | _ -> false end | _ -> false end | _ -> false ;; let time_update_received ( m, last_time_data_sent, last_time_data_received, hbeat_interval : fix_engine_int_msg option * fix_utctimestamp * fix_utctimestamp * fix_duration ) = match m with | Some ( TimeChange tc_data ) -> begin let valid_send_time = utctimestamp_duration_Add ( last_time_data_sent, hbeat_interval ) in let valid_received_time = utctimestamp_duration_Add ( last_time_data_received, hbeat_interval ) in utctimestamp_GreaterThan ( tc_data, valid_send_time ) && utctimestamp_GreaterThan ( valid_received_time, tc_data ) end | _ -> false ;; verify hbeat_sent_if_no_data_sent ( engine : fix_engine_state ) = let engine' = one_step ( engine ) in let cond = begin not ( hbeat_interval_null ( engine.fe_heartbeat_interval )) && engine.fe_curr_mode = ActiveSession && is_int_message_valid ( engine ) && is_state_valid ( engine ) && time_update_received ( engine.incoming_int_msg, engine.fe_last_time_data_sent, engine.fe_last_data_received, engine.fe_heartbeat_interval ) end in cond ==> outbound_msg_heartbeat ( engine'.outgoing_fix_msg ) ;;
It's important to note that the 'translation' of the English-prose statements into IML (or other mathematically precise formal languages) may not be unique. The inherent ambiguity of natural languages is a major reason why the efforts of formalising protocol specifications must be collaborative and industry-wide.
Notice how this approach differs from traditional 'testing'. In the statement above, we're making a high-level claim about the model behaviour, and we can subject this claim to analysis over the entire system state-space. When Imandra analyses the model with respect to such a statement, it works to symbolically verify that the claim holds in all possible scenarios. When such a claim does not hold, Imandra works to construct a precise sequence of events (a "counterexample") which exhibits a violation of the property.
For further information on model verification, see the Model Verification section of the project's homepage.
Test Suite Generation
A fundamental issue with financial trading systems is that they may be in a virtually infinite number of possible states. That is, there are infinitely many (or infeasibly many) possible sequences of incoming messages which they may have to process.
With Imandra, we've built on recent advances in formal verification to develop powerful new forms of model-based testing. These approaches construct high-coverage test suites by 'decomposing' the state-space of the system model into a finite number of symbolically described 'regions' of behaviour and then 'solving' for relevant test-cases. Each region contains a set of symbolic constraints for the input parameters and a corresponding invariant that the system should obey whenever its inputs satisfy the constraints. Imandra also generates corresponding coverage proofs verifying that the collection of the generated regions properly covers the possible behaviours of the decomposed algorithm. This forms a core part of Imandra's "Testflow" framework for architecting and automatically deriving test suites meeting rigorous state-space coverage metrics.
We will seek to ensure that the repository contains up-to-date Imandra region decompositions and test packs for the latest models.
Traditionally, the application of formal verification has been reserved to highly specialised teams (often with PhDs in the subject) in academia, institutions such as NASA, and safety-critical industries such as avionics and microprocessor design.
At AI, our mission is to democratize formal verification, bringing its power to new industries in a user-friendly and scalable way. These applications to new industries are powered by recent advances, including CDCL-based Satisfiability (SAT) and Satisfiability Modulo Theories (SMT) solving, nonlinear decision procedures and scalable techniques for symbolic execution.
For more on our vision for formal verification for finance, see our short explainer video.
AI has published several technical white papers about current application of FV to financial markets:
- Case Study: 2015 SEC Fine Against UBS ATS
- Transparent Order Priority and Pricing
- Creating Safe and Fair Markets
AI has also written several public comments to regulatory proposals by the SEC and CFTC:
- Response to Proposed Rule, Regulation Automated Trading (“Regulation AT”) RIN 3038-AD52
- Response to Release No. 34-76474
If you're interested in further background on techniques underlying Imandra, see the following academic papers:
- Decidability of Univariate Real Algebra with Predicates for Rational and Integer Powers
- A Complete Decision Procedure for Univariate Polynomial Problems in Isabelle/HOL
- Computation in Real Closed Infinitesimal and Transcendental Extensions of the Rationals
- The Strategy Challenge in SMT Solving
- Collaborative Verification-Driven Engineering of Hybrid Systems
And here are links to some great interactive and automated theorem provers:
For a great non-technical introduction to the discipline of formal verification and some of its history, we recommend "Mechanizing Proof" by Donald Mackenzie.
For learning the mathematics behind the techniques, we suggest:
- Handbook of Practical Logic and Automated Reasoning by John Harrison, Intel
- Coq'Art by Yves Bertot and Pierre Castéran, INRIA
- A Computational Logic and subsequent books by Robert S. Boyer and J Strother Moore, UT Austin
- Isabelle/HOL - A Proof Assistant for Higher-Order Logic by Tobias Nipkow, Lawrence C. Paulson and Markus Wenzel, Cambridge and T.U. München
- Decision Procedures - An Algorithmic Point of View by Daniel Kroening, Oxford and Ofer Strichman, Technion
- Quantifier Elimination and Cylindrical Algebraic Decomposition by Bob Caviness and Jeremy Johnson (Eds.)
- Symbolic Logic and Mechanical Theorem Proving by Chin-Liang Chang and Richard Char-Tung Lee, NIH
- Principles of Model Checking by Christel Baier and Joost-Pieter Katoen, T.U. Dresden and RWTH Aachen
- Handbook of Automated Reasoning by Alan Robinson and Andrei Voronkov (Eds.)
If you have other relevant publications or academic papers related to application of formal verification (or formal methods, etc) to financial markets, please create a PR.