Oracle released its fiscal Q4 and full-year 2026 financial results, highlighting significant momentum in its AI and cloud infrastructure segments. While headline figures were record-breaking, the market’s reaction underscores a growing tension in the AI infrastructure sector: the trade-off between massive long-term contracted demand and the immediate, heavy capital expenditure required to deliver it. Q4 Performance at a Glance Non-GAAP EPS: $2.11 (Up 24% year-over-year, beating Wall Street consensus of $1.96). GAAP EPS: $1.45 (Up 21% year-over-year). Remaining Performance Obligations (RPO): Hit a massive record of $638 billion, jumping $85 billion in Q4 alone from the $553 billion reported in Q3. This surge reflects intense enterprise backlog growth driven by cloud and AI infrastructure demand. The Growth Engine & The "Funding Test" Oracle's core growth continues to be heavily fueled by Oracle Cloud Infrastructure (OCI) and cloud application e...
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...