I improved my backtesting performance by 99.9% because of a Google Gemini

Enhancing Backtesting Performance by 99.9% Through the Power of Google Gemini

In the rapidly evolving landscape of algorithmic trading, performance optimization is a perpetual challenge. As someone whose background spans biology and full-stack development—with no formal training in systems engineering or computer science—I embarked on a five-year journey to build and refine a no-code algorithmic trading platform. Approximately two and a half years ago, I decided to undertake an ambitious rewrite of my entire application using Rust, lauded for its speed and concurrency capabilities. However, the transition proved to be unexpectedly arduous and fraught with challenges.

The Initial Struggle with Rust

While Rust appeared to be an ideal choice on paper due to its performance advantages, the reality of development was far more complex. My previous experience with multiple programming languages such as Java, TypeScript, and Go gave me confidence, but Rust’s steep learning curve and unique paradigms made the process difficult and, at times, frustrating. I documented these struggles in a detailed post over a year ago, which resonated widely within the developer community, sparking debates about Rust’s touted benefits versus its practical challenges.

A Turning Point: Incorporating Google Gemini and AI-Powered Pair Programming

Recently, my development process underwent a transformation, thanks to the innovative capabilities of Google’s Gemini 2.5 Pro and Claude Opus 4.1. These advanced large language models (LLMs) provided a robust pair programming workflow that significantly accelerated my development cycle and dramatically improved the performance of my platform. Here’s how I leveraged these tools:

1. Preparation: I supplied both Gemini and Claude with all relevant source files and detailed project requirements.
2. Iterative Critique: I used the models to analyze and critique each other’s proposed plans, fostering a collaborative refinement process.
3. Implementation Assistance: Once the optimal plan was established, I engaged Gemini as a pair programmer to facilitate the coding process.

This method enabled me to eliminate substantial performance bottlenecks within my application, culminating in an astonishing 99.9% increase in backtesting speed.

Key Lessons and Best Practices

Throughout this experience, I identified several pivotal strategies for successful AI-assisted development:

• Leverage Large Language Models: Despite skepticism some may have towards AI, I found that incorporating models like Gemini and Claude was indispensable. They drastically reduced development time and helped uncover optimization opportunities that would have otherwise been missed.

• Deep Understanding Over Vibe-Coding: It’s