---
title: Use The Macro Dashboard Data in Your Own Planning Tools
canonical: "https://themacrodashboard.com/blog/use-the-macro-dashboard-data-in-your-own-planning-tools/"
pubDate: "2026-06-01T00:00:00.000Z"
updatedDate: "2026-06-01T00:00:00.000Z"
author: The Macro Dashboard
description: "A practical guide to using The Macro Dashboard data in spreadsheets, AI retirement prompts, local dashboards, and private planning workflows without turning the signal into personal financial advice."
categories: [Field Notes]
---
## The dashboard can be an input
The website is the easiest way to read The Macro Dashboard. It is not the only way to use it.
The short answer is: pull the public JSON into the tool where you already make decisions. That might be a Google Sheet, a local dashboard, a notebook, a private planning app, or an AI retirement-planning prompt. The dashboard supplies the current risk signal. Your tool supplies the personal context.
That distinction matters. A model allocation is not the same thing as your allocation. Your tax basis, account types, spending needs, retirement date, and trade rules still matter.
## Start with the compact endpoint
For most personal tools, start with the compact Portfolio endpoint:
```text
https://themacrodashboard.com/data/current-kiss.json
```
That file includes the current market regime, risk bias, GRID regime, liquidity signal, and allocation rows. Each allocation row includes the asset, symbol, actual model weight, percent of maximum exposure, and VAMS state.
If you only need current target weights, use the compact endpoint. If you need history, methodology notes, or chart series, use the full snapshot instead:
```text
https://themacrodashboard.com/data/kiss-status.json
```
The [Follow / Data page](/data/) lists the other JSON endpoints, including liquidity and Gavekal snapshots.
## Use a spreadsheet first
A spreadsheet is the cleanest first integration because it keeps the math visible.
The basic workflow is simple. Pull `current-kiss.json`, multiply the target weights by your portfolio value, then compare target dollars with current dollars. Vanguard's guide to [rebalancing](https://investor.vanguard.com/investor-resources-education/portfolio-management/rebalancing-your-portfolio) makes the same point in a simpler setting: a target weight is useful only if you also define how and when you rebalance.
A Google Sheet can use Apps Script to fetch the JSON. Google documents this pattern in its guide to [external APIs in Apps Script](https://developers.google.com/apps-script/guides/services/external). The Macro Dashboard data page includes a starter custom function for this exact use case.
The spreadsheet should not decide for you. It should answer a smaller question: if I followed the model target today, what would the target dollar amounts be, and how far am I from them?
## Use percent of max for personal portfolios
The actual model weights are useful, but percent of maximum exposure is the more portable number.
Suppose the dashboard shows stocks at 50% of maximum exposure. In the model, the maximum stock sleeve is 60%, so the model target would be 30% stocks. But if your personal maximum stock sleeve is 40%, the same signal would point to 20% stocks.
That is the method described in [How to Scale the Dashboard Percent of Maximum Exposure](/blog/how-to-scale-the-dashboard-percent-of-maximum-exposure/). The dashboard tells you how much of the risk budget is active. Your plan defines the size of the risk budget.
This is especially useful in retirement planning because the same macro signal can mean different things for different people. A 35-year-old saver, a 55-year-old pre-retiree, and a 68-year-old drawing from a portfolio do not have the same constraints.
## Pair it with AI planning prompts carefully
AI retirement prompts can be useful. They can stress test withdrawal rates, compare Social Security timing, think through Roth conversions, or sketch a healthcare bridge before Medicare.
The weak version is to ask a model, "What should my allocation be?" That usually produces generic advice.
A better version gives the model your planning assumptions and the current dashboard signal, then asks for scenarios. For example:
```text
I am using The Macro Dashboard as a risk overlay, not as personal financial advice.
Current dashboard data: https://themacrodashboard.com/data/current-kiss.json
My base allocation:
[stocks / bonds / cash / gold / bitcoin]
My constraints:
[retirement date, withdrawal needs, tax account types, trade limits]
Show three ways to apply this as an overlay:
1. reference only, no trade
2. small contribution/rebalancing tilt
3. scaled target using percent of maximum exposure
For each option, explain the tradeoffs, what could go wrong, and what question I should review before acting.
```
That prompt does not ask the AI to be a financial advisor. It asks the AI to organize scenarios around a rule-based signal.
This pairs naturally with retirement questions like withdrawal-rate stress tests and glide paths. Kitces' work on [sequence-of-return risk](https://www.kitces.com/blog/understanding-sequence-of-return-risk-safe-withdrawal-rates-bear-market-crashes-and-bad-decades/) is a useful reminder that bad returns matter more when withdrawals are starting or already underway. In that setting, the current risk backdrop is worth seeing, even if it does not dictate the answer.
## Build a private dashboard if you want more control
A private dashboard can do more than a spreadsheet.
It can fetch `current-kiss.json`, cache the latest reading, compare it with your own holdings, and show a small review checklist. If it runs locally, your personal account data can stay on your machine while the public macro signal comes from The Macro Dashboard.
A simple tool might show:
- current dashboard allocation
- your current allocation
- target dollars using scaled percent-of-max exposure
- difference between target and current dollars
- whether the difference is large enough to review
- notes on taxes, account location, and cash needs
The technical piece is not complicated. The browser [Fetch API](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API) can retrieve JSON. The harder part is deciding what the tool is allowed to do. In most cases, it should inform a review, not place trades.
## Keep the boundary clear
Investor.gov's guide to [asset allocation and diversification](https://www.investor.gov/introduction-investing/getting-started/asset-allocation) starts with the right idea: allocation is personal because goals, time horizon, and risk tolerance are personal.
The Macro Dashboard does not know those things. It knows the current public-data signal. That signal can make a planning tool better, but it cannot make the plan for you.
The practical version is simple: use the dashboard data as an input, scale it to your own plan, and let your tool show the tradeoffs before you do anything.