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...
Most of my investment strategies share a simple backbone... I watch how price behaves around the 10, 30, and 50 day averages . Trend, for me, is not a mystical concept... it’s a practical way of asking whether price is moving with conviction or just drifting. In all my strategies, including the Augmented Income Strategy (AIS) , I look for a specific pattern before committing more capital: Price below the 50 day average... below the 30 day average... and then a 10 day average that starts to turn up above them. That “below, below, and 10 day above” contradiction is my way of letting the market prove that selling pressure has exhausted itself and that a new, healthier trend may be forming. It’s a simple structure, but it keeps me from chasing strength too early or buying weakness without confirmation. Interestingly, this way of thinking about trend has some coincidental overlap with work I’ve seen from Gary Antonacci ... a well known voice in momentum and trend‑based investing...