Discover how to improve SQLAlchemy performance using an in-memory database built in pure Python. We benchmark SQLite and introduce a faster SQLAlchemy in-memory alternative for backtesting and simulations.

Discover 7 Python built-in functions that every developer should know. These tips and tricks will help you optimize your code, improve performance, and write cleaner trading algorithms.

Explore key design patterns like Abstract Factory, Service Locator, and Event Emitter to build modular and scalable algorithmic trading systems in Python.

This article introduces core low latency C++ programming techniques, including inline functions, CPU-level optimizations, and compile-time tricks to boost performance in trading systems and real-time applications.

Many trading strategies show low correlations on paper, but that doesn't mean they're independent. In this article, we will explore the hidden risks in trading portfolios by understanding the interdependence of strategies and the limitations of correlation as a sole risk measure.

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.