Is it better to run your quant trading in the cloud or locally? In this article, I outline the pros and cons of each approach and explain why running locally is often better for research while running in the cloud is better for live trading.
Is it better to run your quant trading in the cloud or locally? In this article, I outline the pros and cons of each approach and explain why running locally is often better for research while running in the cloud is better for live trading.
Backtest speed can significantly affect research friction. The ability to form a hypothesis and quickly get an answer from a backtest allows you to investigate more hypotheses. In this article, I explore several factors that affect backtest speed and compare the performance of 3 open-source backtesters.
How does a company's profitability affect its stock returns? In this post, I use Alphalens, a Python library for analyzing alpha factors, to investigate the relationship between operating margin, a profitability ratio, and future returns.
When researching fundamental factors, analyzing alpha shouldn't be your first step. You can save time and spot issues early by starting with a basic exploration of your factor's distribution and statistical properties, a process known as exploratory data analysis (EDA). This post looks at operating margin, a profitability ratio, to demonstrate what you can learn from exploratory data analysis.
You may have heard about Python type hints and wondered whether they're relevant to quants or only to professional software developers. In this article, I'll explain how QuantRocket's JupyterLab environment uses type hints to enable better auto-complete and in-editor documentation, and I'll explain when quants should use type hints in their own code.