A small machine learning lab specializing in alpha models for select financial partners faced a critical bottleneck: their market simulation loop took between 900 and 1300 milliseconds per window using Python and Pandas. For experiments spanning years of historical data, this translated to grueling 6- to 20-hour runs. The team pursued a series of rewrites—first replacing Pandas with NumPy, then transitioning to hand-written Rust and C++—to slash compute time to just 1–5 milliseconds per window on a standard cloud server. On a high-clock AMD test rig, latency dropped further to 4–40 microseconds, transforming what were once day-long experiments into processes that complete in minutes.
The hidden cost of simulation in machine learning
In quantitative finance, simulation serves as the ultimate safeguard against look-ahead bias. It ensures that models are tested under conditions that mirror real-world trading environments. However, the computational demands of this process are often underestimated. The team’s simulation pipeline—from data ingestion to feature engineering, inference, and evaluation—was built entirely in Python. While this stack excelled in flexibility and rapid prototyping, it struggled under the weight of rolling-window computations and memory allocation churn.
For each 1-minute or 5-minute bar across years of historical data, a single simulation run consumed between 6 and 20 hours on a conventional workstation. The bottleneck wasn’t merely the volume of calculations; it was the cumulative latency introduced by Python’s interpreter overhead and the inefficient handling of rolling windows in Pandas. The team needed a solution that could reduce latency without sacrificing the rigor of their simulation process.
From Python to Rust: breaking the GIL ceiling
The first step toward optimization was replacing Pandas with NumPy, which delivered a substantial performance boost. Latency per window dropped to approximately 140 milliseconds, enabling the team to evaluate models across more scenarios. Yet, this improvement still fell short of what was needed to run complex experiments efficiently. The remaining overhead stemmed from Python’s Global Interpreter Lock (GIL), which limited parallelism and capped achievable throughput.
A colleague with deep Rust experience had long advocated for the language, arguing that its performance and safety guarantees made it ideal for high-throughput systems. Initially, the team resisted, questioning whether the added complexity was justified. However, the limitations of Python became undeniable. No matter how many CPU cores were allocated, the GIL and interpreter overhead imposed a hard ceiling on performance. The choice was clear: either accept the status quo or rewrite the core simulation logic in a language designed for speed and concurrency.
Rust and C++: the double-engine rewrite
The transformation began with a complete rewrite of every feature computation in Rust. The team focused on eliminating unnecessary recomputations and reducing allocation churn by maintaining O(1) incremental state per tick. This architectural shift alone eliminated much of the latency variance and improved memory efficiency.
Next, the inference models were ported to a C++ engine, compiled ahead-of-time (AOT) for the target CPU architecture. The C++ backend was exposed to the Rust codebase via Foreign Function Interface (FFI), enabling seamless integration while maximizing performance. The results were dramatic. Latency for a full simulation cycle per window plummeted to 1–5 milliseconds on a standard cloud virtual machine. On a high-clock AMD test rig, performance further improved to 4–40 microseconds, demonstrating the scalability of the new approach.
Beyond speed: unlocking new research avenues
The most significant impact of this rewrite wasn’t just faster simulations—it was the research possibilities it unlocked. With latency reduced to microseconds, the team could now run tick-level simulations in real time, a capability that was previously infeasible in Python. This opened the door to testing hypotheses inspired by interdisciplinary fields such as collective behavior and bioelectricity, concepts that have found surprising applications in financial modeling.
The team also prioritized transparency in their validation process. Every raw signal generated by their system is immediately written to a public S3 bucket with microsecond timestamps, ensuring that no look-ahead bias can occur. A live demo board reports real inference latency, providing an unfiltered view of performance on commodity hardware.
What this rewrite doesn’t solve
It’s important to clarify what this optimization does not achieve. The team’s infrastructure does not include colocation, kernel bypass, or exchange adjacency—features commonly associated with high-frequency trading (HFT) systems. Their focus was solely on reducing compute latency, not on minimizing data-feed latency or exploiting microstructural advantages. They explicitly avoid overstating their capabilities, positioning the rewrite as a performance enhancement rather than a competitive edge in HFT.
An open question for HFT practitioners
The team is curious to hear from practitioners who operate HFT or market-making systems in production. Given the constraints of fast compute without colocation, they wonder if their approach could be practically applied in real-world trading. One potential use case they’re exploring is adverse-selection defense for market-making, where skew and pull quotes might be adjusted preemptively in response to microstructure shifts. They acknowledge this idea may be flawed and welcome feedback from those with hands-on experience in the field.
The takeaway: Rust’s role in high-performance computing
This story isn’t an advertisement or a sales pitch. The team shares it because they believe in the value of honest engineering and the Rust community’s commitment to scrutinizing real-world performance claims. For those considering a similar transition, the message is clear: when Python’s flexibility clashes with Python’s limitations, a carefully planned rewrite in a systems language like Rust can deliver transformative results. The journey from 140 milliseconds to microseconds wasn’t just a technical achievement—it was a gateway to entirely new ways of thinking about financial simulation.
AI summary
Python ve Pandas’tan Rust ve C++’ya geçişle piyasa simülasyonu performansı 140ms’den 1-5 mikrosaniyeye düştü. Küçük bir ML laboratuvarının performans krizine bulduğu çözüm ve getirdiği fırsatlar.
