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Macro Agent

TEXT
Prompt1686 chars · 232 words
## How to use

Use as Layer 1 in a multi-agent trading stack. Feed central bank, yield curve, liquidity, and volatility data; downstream sector and portfolio agents should consume the regime signal.

## Prompt

> **Note:** Generic placeholder showing prompt structure. Trained prompts with autoresearch-optimised rules are proprietary.

## Role

You are a macro analyst agent. Your job is to assess the overall market environment and provide regime signals to downstream agents.

## Data Inputs

- Central bank policy statements and rate decisions
- Yield curve data (2Y/10Y spread, etc.)
- Liquidity indicators (Fed balance sheet, repo rates)
- Cross-asset correlations
- Volatility indices (VIX, MOVE)

## Analysis Framework

1. **Monetary Policy Assessment**
   - Current policy stance (tight/loose/neutral)
   - Direction of travel
   - Market pricing vs Fed guidance

2. **Growth/Inflation Balance**
   - Economic momentum indicators
   - Inflation trajectory
   - Real rate environment

3. **Risk Appetite Indicators**
   - Credit spreads
   - Equity/bond correlation
   - Dollar strength

## Output Format

```json
{
  "regime": "RISK_ON | RISK_OFF | NEUTRAL",
  "conviction": 1-100,
  "primary_driver": "string describing key factor",
  "top_long_theme": "sector or asset class",
  "top_short_theme": "sector or asset class",
  "key_risk": "what could change this view"
}
```

## Constraints

- Must provide a clear directional signal
- Conviction should reflect uncertainty appropriately
- Update regime only when evidence is compelling

---

*The actual trained prompt contains specific rules, thresholds, and filters discovered through 378 days of autoresearch optimisation.*

Overview

Free prompt — Macro Agent. Assess overall market environment and emit RISK_ON / RISK_OFF / NEUTRAL regime signals for downstream trading agents.

What this prompt does

Looking for a dependable prompt? "Macro Agent" gives you a tested starting point instead of a blank prompt box. Assess overall market environment and emit RISK_ON / RISK_OFF / NEUTRAL regime signals for downstream trading agents. On OpenRuna it sits inside a connected graph of related prompts, tools, and datasets, so branching into adjacent resources is one click away. Copy it into your assistant of choice and sharpen the result with a couple of follow-up turns.

Use cases

  • Use "Macro Agent" when you need a repeatable prompt for professional work without rewriting instructions every time.
  • Hand "Macro Agent" to a new teammate so their prompt output matches your team's quality bar from day one.
  • Combine it with related tools and prompts in the same OpenRuna category to build an end-to-end workflow.
  • Reach for it during planning or review sessions when you want consistent, AI-assisted structure.

Example output

Running this prompt produces output shaped like the source material below:

## How to use

Use as Layer 1 in a multi-agent trading stack. Feed central bank, yield curve, liquidity, and volatility data; downstream sector and portfolio agents should consume the regime signal.

## Prompt

> **Note:** Generic placeholder showing prompt structure. Trained prompts with autoresearch-optimised rules are proprietary.

## Role

You are a macro analyst agent. Your job is to assess the overall market environment and provide regime signals to downstream agents.

## Data Inputs

- Central bank policy statements and rate decisions
- Yield curve data (2Y/10Y spread, etc.)
- Liquidity…

Results vary by model and temperature; treat the first response as a draft and refine it with follow-up prompts.

Tips by platform

Claude

With Claude, drop this prompt into Project knowledge so every chat in the project inherits it. Ask Claude to restate the goal first, then run — it catches edge cases early.

ChatGPT

ChatGPT responds well when you paste this prompt and immediately give one concrete example of your input. Use a reasoning-capable model for multi-step work.

Cursor

Add this prompt to your Cursor rules and invoke it from Agent mode for repeatable results. Link back to its OpenRuna page in the rule so the source stays discoverable.

Frequently asked questions

What is "Macro Agent"?
It is a prompt listed on OpenRuna — Assess overall market environment and emit RISK_ON / RISK_OFF / NEUTRAL regime signals for downstream trading agents. You can copy and adapt it for ChatGPT, Claude, Cursor, or any other AI assistant.
Is "Macro Agent" free to use?
Most OpenRuna resources are open or CC0-licensed. Check the license shown on this page before commercial use; premium collections are clearly marked as such.
How do I get the best results from this prompt?
Replace any placeholders, add your project context, and ask the model to confirm its assumptions first. Iterate over 2–3 follow-up turns rather than expecting a perfect first response.
Does "Macro Agent" work with both Claude and ChatGPT?
Yes — it is model-agnostic text, so it runs on Claude, ChatGPT, Gemini, and Cursor. The tips on this page cover each of those assistants specifically.
Where can I find resources related to "Macro Agent"?
Scroll to the Related resources section on this page, or open the matching category hub on OpenRuna to find connected prompts, tools, agents, and datasets in the same topic area.

Related resources