We benchmark SQLAlchemy from 1.3 to 2.0 using in-memory SQLite, and unveil a purpose-built pure-Python memory dialect that crushes the old limits.

These 7 lesser-known Python features can make your quant code cleaner, faster, and easier to maintain. They're practical tools every trading developer should have in their toolbox.

A hands-on guide to useful design patterns for algorithmic trading systems. Learn how Abstract Factory, Service Locator, config-driven instantiation, and Event Emitter can help you build cleaner, more modular Python trading bots.

In modern software development, achieving low latency is crucial for performance-critical applications - from trading systems to real-time data processing. This article explores foundational techniques to reduce latency by leveraging compiler and CPU-level optimizations. Even a few saved CPU cycles can compound to meaningful gains at scale.

When constructing a portfolio, it's common to see low pairwise correlations among strategies - say, around 0.2. However, this number may not fully capture the underlying dependency between strategies, especially when they're built to exploit similar market tendencies, such as intraday trends. In this article, I explore how strategies can be interdependent, and why relying solely on correlation can be misleading when assessing risk.

This article walks you through the steps of setting up a new EC2 instance for algorithmic trading using Ubuntu 24.04 and Interactive Brokers. Running your trading algorithms on EC2 can offer scalability, reliability, and cost efficiency, ideal for managing both paper and live trading accounts.