Why I Still Reach for Lisp and Scheme in Finance, Despite Loving Haskell

Haskell. The name evokes images of elegant type systems, pure functions, and a beautifully expressive language. As a quantitative finance professional, I’ve spent considerable time learning and experimenting with Haskell. I love it. It’s a joy to write code in. But despite all that, I consistently find myself reaching for Lisp (specifically Common Lisp and Scheme) for certain projects, and even for specific parts of projects where Haskell would seem like the natural fit. This isn't a condemnation of Haskell; it's an explanation of why, in the pragmatic world of finance, older tools often still have a significant edge.

§The Allure of Haskell in Finance

Before diving into why Lisp and Scheme persist, let's acknowledge Haskell’s strengths, particularly in the context of financial applications.

Type Safety: Finance relies on precision. Errors in calculations can lead to substantial losses. Haskell’s strong, static type system catches errors at compile time that would only surface at runtime in dynamically typed languages.
Functional Purity: The emphasis on immutability and pure functions simplifies reasoning about code, crucial when building complex financial models. Avoiding side effects makes parallelization easier, essential for high-performance computing.
Mathematical Elegance: Financial modeling is inherently mathematical. Haskell’s syntax and features align naturally with mathematical notation, making code more readable and maintainable.
Concurrency & Parallelism: Modern financial applications demand scalability. Haskell’s concurrency features allow for efficient use of multi-core processors and distributed systems.

These benefits explain the growing interest in Haskell within the quantitative finance community. Projects involving high-assurance calculations, risk management systems, and algorithmic trading often benefit from its strengths. You can find good introductory resources on functional programming with Haskell here:

§Why Lisp and Scheme Still Hold Their Own

Despite Haskell’s advantages, Lisp and Scheme continue to thrive in specific niches within finance. This isn't nostalgia; it’s about practicality.

§1. Rapid Prototyping and the REPL

This is the big one. Lisp and Scheme’s REPL (Read-Eval-Print Loop) is unmatched. The ability to write code, execute it immediately, inspect the results, and modify the code in a tight feedback loop is incredibly powerful for exploration and prototyping.

§In finance, this means:

Quickly testing financial models: Trying out different formulas, simulating market scenarios, and visualizing results are all vastly faster in a Lisp/Scheme REPL.
Exploratory Data Analysis: Interactively examining and manipulating financial data without the overhead of compilation and build processes.
Iterative Algorithm Development: Refining trading strategies or risk models through immediate feedback.

Haskell's tooling has improved, with GHCi being a capable REPL, but it still doesn't quite match the fluidity and responsiveness of a good Lisp/Scheme environment. The initial compilation time, even for small changes, can interrupt the flow of thought. Consider a situation where you’re tweaking a pricing model. With Lisp, you can change a single line and see the impact instantly. With Haskell, you’re often looking at a few seconds of compilation time, even with incremental compilation.

§2. Existing Codebases and Legacy Systems

Many financial institutions have decades of accumulated code in Lisp and Scheme, particularly Common Lisp. Rewriting these systems in Haskell is often prohibitively expensive and risky. Maintaining and extending these legacy systems requires continued expertise in Lisp and Scheme.

§Furthermore, these systems often handle core infrastructure:

Trade order management systems: Critical for executing trades efficiently and accurately.
Risk management platforms: Assessing and mitigating financial risks.
Pricing and valuation libraries: Calculating the fair value of financial instruments.

These are not areas where you want to introduce potentially disruptive changes unless absolutely necessary.

§3. Tooling and Libraries – A Mature Ecosystem for Finance

While Haskell’s library ecosystem is growing, Lisp and Scheme have a long history of specialized libraries for finance.

Numerical computation: Libraries like CL-Numfmt for formatting financial numbers and numerical analysis tools in Scheme.
Time series analysis: Packages for handling and analyzing financial time series data.
Data handling: Efficient libraries for parsing and manipulating large financial datasets.
Connectivity: Libraries for connecting to market data feeds and trading APIs.

These libraries are often well-tested, optimized for performance, and specifically tailored to the needs of financial professionals. Building equivalent functionality in Haskell would require significant effort.

§4. Metaprogramming and DSLs (Domain Specific Languages)

Lisp’s macro system is legendary. It allows you to extend the language itself, creating domain-specific languages (DSLs) tailored to financial modeling.

Custom syntax for financial formulas: Define a DSL that reads like standard financial notation, making code more intuitive for quants.
Automated code generation: Generate boilerplate code for common financial tasks, reducing development time.
Optimization at compile time: Transform code to improve performance for specific financial algorithms.

Haskell has Template Haskell, which offers some metaprogramming capabilities, but it’s generally considered more complex and less flexible than Lisp’s macros. The ability to fundamentally alter the syntax of the language is a powerful tool in finance, where complex models often require custom notation.

§Comparing Specific Implementations

§Let’s look at some popular implementations:

§| Feature | Common Lisp | Scheme (Racket) | Haskell |

§|-------------------|-------------|----------------|---------|

Common Lisp: A versatile and performant implementation, often used for large-scale financial systems. CLISP and SBCL are popular choices. Provides excellent tooling and a vast library ecosystem.

Racket (Scheme): Known for its educational focus and powerful macro system. A great choice for prototyping and building DSLs. Offers excellent documentation and a supportive community. Good for rapid development. You can learn more here:

Haskell: Excellent for projects requiring high reliability and performance. Suited for complex financial modeling and risk management. Requires a significant investment in learning the language and its ecosystem.

§The Future: A Pragmatic Blend

I don’t see Lisp and Scheme disappearing from finance anytime soon. They address critical needs – rapid prototyping, maintaining legacy systems, and leveraging existing specialized libraries – that Haskell currently doesn't fully satisfy.

§However, I believe the future lies in a pragmatic blend. We’ll likely see:

Interoperability: Using Haskell to build new, critical components and integrating them with existing Lisp/Scheme systems.
Gradual Adoption: Migrating parts of legacy systems to Haskell incrementally, focusing on the areas where its benefits are most significant.
DSL Development: Leveraging Lisp’s macro system to create DSLs that can be used to interface with Haskell code.

Ultimately, the best tool for the job depends on the specific requirements of the project. While I admire Haskell’s elegance and power, I’ll continue to reach for Lisp and Scheme when speed, flexibility, and the need to work with existing systems are paramount.

§Disclaimer

Please note that some links in this article are affiliate links. This means that I may earn a small commission if you purchase a product through one of these links. This does not affect the price you pay. The views expressed in this article are my own and do not constitute financial advice. Always do your own research before making any investment decisions.