Thanks for posting. I can't find a detailed list of Excel functions supported, not supported, work in progress. I'm interested for example in FORECAST.ETS and others.
An extract of the (very short) article: "Stage 3: Agentic AI
This is the final, most profound stage. What if the system could run itself? Agentic AI platforms from companies like Pigment and Anaplan are creating systems of autonomous agents that perceive, reason, and act. Imagine an "Analyst" agent detecting a sales variance. It triggers a "Planner" agent to investigate, which then activates a "Modeler" agent to run simulations and propose a new budget. The human is no longer in the loop; they are overseeing it. Is this not the endgame?"
Asking Google about the normal typical cost of deployment of Pigment or Anaplan: "Pigment pricing starts around $500-800 annually per Explorer seat, with Contributor and Editor seats costing more, leading to a total cost of $75,300-$106,250 annually for a Professional configuration. Anaplan, a competitor, has pricing that starts around $200,000 per year, but final costs depend on company size and model complexity. Pigment is best for companies seeking a modern, visual, and flexible planning platform, while Anaplan is more suited for enterprises with highly complex, structured, and large-scale planning needs."
Those platforms are targeted to big companies, but I doubt that the author (as me I'm doing for a living) has any idea about the capability of spending of small/medium companies and the budget allocated to the financial modeling (if any).
Anyway, speaking about talking after reading, my answer is yes, Excel has and will have a place for financial modeling and the article isn't providing any concrete evidence of the contrary.
Caution! Curious about this comment (having a family member with Parkinson's) I searched for treatment from naturalherbscentre.com and I found that this type of comment was copy-pasted elsewhere
Over lunch the other day, a friend mentioned his brother, a professional asset manager, swears by a simple mean reversion trading strategy. His strategy consists of buying the 10 biggest losers in the stock market each day and selling them at the close of the following trading session. I asked him if he knew which index or exchange his brother used to pick his losers from, and he told me that he wasn’t certain. As a curious casual investor, I decided to put this strategy to the test using historical data and backtest the trading strategy with Python.
Disclaimer: This article is for informational and educational purposes only and is not intended to serve as personal financial advice.
What you will learn from this backtesting tutorialCopy heading link
In this article, I’ll walk through the process of backtesting a daily Dow Jones mean reversion strategy using Python in Datalore notebooks. To make it accessible even for those with limited coding experience, I’ll leverage Datalore’s AI Assistant capabilities. I’ll also show how intuitive prompts can be used to create the key components of the backtest, and demonstrate Datalore’s interactive charting and reporting features to effectively analyze and share the backtest results.
To make things more challenging for myself (and easier for you), I won’t write a single line of code myself. Every line of code in this tutorial will be generated by AI as shown below:
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my elderly father lives alone and tends to fall asleep on the toilet. I have developed a solution with Arduino that, after an initial measurement, monitors the appearance of obstacles in front of him. after 15 minutes of the obstacle remaining, it emits a sound for 10 seconds and starts monitoring again.