When developing a custom algorithmic trading program, the temptation is often to code every single feature, filter, and safety net directly into your core Python engine (or PHP, if you are running a web host with an SQL backend). Over time, this leads to bloated code, constant API throttling, and an engine that is too heavy to pivot quickly. Recently, I stepped back to evaluate my own architecture and realized that the secret to true quantitative agility isn't writing more code—it's offloading the heavy lifting to a spreadsheet's internal coding. It lives entirely outside the broker's platform and my local machine. The Problem with Broker Platforms Brokerage platforms are designed for execution, not iteration. While they offer excellent charting and fundamental data, they are fundamentally rigid. They force your workflow into their predetermined boxes. If you want to apply a unique Fibonacci-related growth factor, quickly weigh a custom triple-moving average crossove...
Exploring the mechanics of capital, the discipline of compounding, and the margin of safety found in a meaningful life.
A periodical by Michael Medeiros