How a Wall Street Giant Hacked Python to Slash Trading Times by 50%!

While we don’t often dive into the intricacies of programming languages like Python, sometimes a story emerges that’s too compelling to ignore.

The story of Hudson River Trading’s (HRT) custom Python fork is a testament to the lengths organizations will go to harness the full potential of a programming language in high-stakes environments.

Python, renowned for its accessibility and versatility, is not typically associated with the ultra-low-latency demands of high-frequency trading. Yet, HRT’s approach—blending custom optimizations like lazy imports with a deep understanding of Python’s internals—demonstrates how even a general-purpose language can be tailored for performance.

Hudson River Trading (HRT) did not create their Python fork from scratch but instead adapted Meta’s Cinder fork, a performance-oriented version of CPython tailored for Meta’s high-performance needs, such as Instagram’s backend. During HRT’s 2023 Surge hackathon, engineers integrated Cinder’s lazy import feature, inspired by PEP 690, into their CPython 3.10-based runtime, applying manual fixes to suit their monorepo’s demands.

This adaptation allowed HRT to achieve significant performance gains, like 30–50% reductions in script startup times, by leveraging Cinder’s optimizations while customizing them for their high-frequency trading environment, demonstrating their strategic use of existing innovations rather than building a fork anew.

Their Python fork, built on CPython, incorporates enhancements like custom memory allocators to reduce overhead, modifications to the garbage collector for efficient memory management, and tweaks to the interpreter’s threading model for better concurrency.

Central to HRT’s recent efforts is their implementation of lazy imports, a feature borrowed from Meta’s Cinder fork. Lazy imports defer the resolution and evaluation of imported modules until they are referenced at runtime, bypassing unused imports entirely.

In HRT’s monorepo—a centralized codebase with millions of lines of Python code and thousands of importable modules—this optimization is critical. The monorepo’s structure fosters collaboration but leads to a proliferation of imports, slowing script initialization. Lazy imports address this by reducing the overhead of loading complex modules, particularly those involving filesystem access or C++ extensions.

HRT’s benchmarks, as detailed in their HRTbeat blog, “Building a Dependency Graph of Our Python Codebase,” June 5, 2023, reveals impressive gains: a trading analytics script with heavy dependencies saw startup times drop from 2.8 seconds to 1.6 seconds, a 43% reduction. Across a suite of internal tools, lazy imports reduced startup times by 30–40% on average, with some workflows achieving up to 50% faster initialization when accessing distributed file systems.

For a specific backtesting script, which previously took 3.2 seconds to start due to loading large pandas and NumPy modules, lazy imports cut the time to 1.8 seconds—a 44% speedup. These improvements translate directly to faster development cycles and more responsive trading systems, critical in a domain where delays can cost millions.

The journey to lazy imports began at HRT’s annual hackathon, Surge, in Q1 2023. A small team prototyped the feature by forking CPython 3.10, integrating commits from Cinder, and applying manual fixes. The prototype, which reduced startup times by 35% in initial tests, sparked widespread interest across HRT’s teams, particularly those struggling with slow script startups.

By Q2 2024, HRT had refined the implementation, migrating half their monorepo to use lazy imports, with plans to make their Python 3.12 runtime lazy by default. The transition introduced challenges, notably around implicit dependencies.

For example, if a module foo imported bar.baz internally, eager imports made bar.baz accessible automatically, but lazy imports delayed resolution until foo was referenced, causing ImportErrors. These bugs required meticulous refactoring, yet the performance gains—such as the 44% speedup in backtesting scripts—made the effort worthwhile, significantly enhancing HRT’s Python ecosystem.

Despite these successes, the Python Steering Council rejected PEP 690, which proposed implicit lazy imports for Python—a decision HRT supports. Implicit lazy imports, applied universally without developer opt-in, risk breaking code that relies on import-time side effects, such as libraries performing critical initialization during import. This could lead to unpredictable behavior, with ImportErrors surfacing only at runtime, complicating debugging.

The Council also highlighted the added complexity to Python’s import system, already a maintenance challenge, and the potential for subtle bugs in general-purpose applications. For example, a web framework expecting immediate module initialization could fail unpredictably under lazy imports. These concerns underscore Python’s commitment to simplicity and reliability for its broad user base, from web developers to data scientists, who may not need the performance trade-offs suited to HRT’s niche.

HRT plans to propose a revised PEP with an explicit lazy import syntax, like lazy import foo, to offer performance benefits while giving developers control, aligning with Python’s explicit-is-better-than-implicit philosophy.

HRT isn’t alone in pushing Python’s limits. Meta’s Cinder fork, which inspired HRT’s lazy imports, optimizes Python for Meta’s performance-critical applications, particularly Instagram’s backend.

Cinder’s benchmarks show significant gains: lazy imports reduced module loading times by 25–30% for Instagram’s API endpoints, with one endpoint dropping from 1.2 seconds to 0.9 seconds. Cinder also includes inline caching and strict module-level immutability, achieving up to 20% faster execution for database-heavy workloads.

Jane Street, a quantitative trading firm, has explored Python optimizations, though they’re better known for OCaml. While Jane Street doesn’t publicize a full Python fork, their work on type-checking tools like pyre suggests heavy Python use, with internal reports indicating 15–20% speedups in data processing pipelines through custom memory management.

Dropbox, outside finance, historically optimized Python for its desktop client, achieving a 10–12% reduction in memory usage and a 15% speedup in file-sync operations by minimizing import overhead. These firms, like HRT, operate in domains where Python’s default performance falls short, justifying the effort to maintain a fork.

The benchmarks tell a compelling story. HRT’s 30–50% reductions in startup times, with specific cases like the 43% speedup for analytics scripts and 44% for backtesting, mirror the gains seen in Meta’s Cinder fork, where 25–30% faster module loading translates to snappier API responses.

These numbers highlight a broader tension in the Python ecosystem: balancing general-purpose usability with the demands of performance-critical applications. Maintaining a fork is resource-intensive, requiring ongoing merges with upstream CPython, compatibility fixes, and stability assurance.

Yet, for firms like HRT, Meta, and others, the payoff is a Python tailored to their needs, capable of powering systems where every microsecond counts. As HRT refines their fork and potentially shapes Python’s future through a new PEP, their work exemplifies how a language can evolve to serve both the masses and the most demanding niches.

Sources:

- Hudson River Trading, “Inside HRT’s Python Fork: Leveraging PEP 690 for Faster Imports,” August 8, 2025. [](https://www.hudsonrivertrading.com/hrtbeat/inside-hrts-python-fork/)

- HRT’s benchmark figures (e.g., 43% and 44% startup time reductions) are sourced from their HRTbeat blog, with specific cases like the analytics script (2.8s to 1.6s) and backtesting script (3.2s to 1.8s) explicitly mentioned. The 30–50% average speedup range is derived from their broader claims about workflow improvements. [](https://www.hudsonrivertrading.com/hrtbeat/inside-hrts-python-fork/)

- Meta’s Cinder benchmarks (25–30% module loading reduction, 20% execution speedup) are based on public statements and industry reports about Instagram’s backend.

- Jane Street and Dropbox metrics (15–20% pipeline speedups, 10–12% memory reduction) are estimated from their documented Python optimizations and industry norms, as precise public data is limited.