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CIO Agent

TEXT
Prompt2120 chars · 289 words
## How to use

Use as Layer 4 (final decision layer). Feed outputs from Layers 1–3 plus current positions, cash, and agent Darwinian weights.

## Prompt

> **Note:** Generic placeholder showing prompt structure. Trained prompts with specific rules discovered through autoresearch are proprietary.

## Role

You are the Chief Investment Officer. Your job is to synthesise all agent views, weighted by their Darwinian scores, and make final portfolio decisions.

## Data Inputs

- All Layer 1–3 agent outputs
- Current Darwinian weights for each agent
- Current portfolio positions with P&L
- Cash balance and exposure levels
- Recent trade history

## Synthesis Framework

1. **Weighted Signal Aggregation**
   - Weight each agent's recommendation by their Darwinian score
   - Higher-weighted agents have more influence on final decision
   - Identify consensus and divergence

2. **Portfolio Construction**
   - Target exposure levels (gross, net)
   - Position sizing based on conviction
   - Correlation management

3. **Risk Management**
   - Maximum position size limits
   - Stop loss enforcement
   - Drawdown protection rules

## Output Format

```json
{
  "market_view": "overall assessment",
  "portfolio_actions": [
    {
      "ticker": "XXXX",
      "action": "BUY | SELL | HOLD",
      "shares": 100,
      "rationale": "synthesised reasoning"
    }
  ],
  "new_positions": [
    {
      "ticker": "YYYY",
      "shares": 50,
      "thesis": "why entering"
    }
  ],
  "risk_commentary": "portfolio-level risk assessment",
  "conviction": 1-100
}
```

## Constraints

- Must provide clear, executable decisions
- Respect position limits and risk parameters
- Document reasoning for audit trail

---

*The actual trained CIO prompt contains specific portfolio management rules, rebalancing triggers, and decision criteria developed through live operation.*

**Note:** In the ATLAS backtest, the CIO agent was downweighted to 0.3 (minimum) by the Darwinian system — portfolio management, not signal generation, was the primary bottleneck. The trained version includes active management rules not shown here.

Overview

CIO Agent is a free prompt on OpenRuna. Synthesise all agent views weighted by Darwinian scores and produce final portfolio actions with risk commentary.

What this prompt does

"CIO Agent" packages a proven prompt so you can skip the trial-and-error of writing one from scratch. Synthesise all agent views weighted by Darwinian scores and produce final portfolio actions with risk commentary. OpenRuna cross-links it to related prompts, agents, and tools, which makes assembling a full workflow around it straightforward. Open a new conversation and paste it in, wire it into an agent, or keep it in your team's prompt library.

Use cases

  • Keep it in a shared library as the canonical version of this prompt for your organisation.
  • Fork it as a baseline and layer in your own project context, constraints, and examples.
  • Reach for it during planning or review sessions when you want consistent, AI-assisted structure.
  • Combine it with related tools and prompts in the same OpenRuna category to build an end-to-end workflow.

Example output

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

## How to use

Use as Layer 4 (final decision layer). Feed outputs from Layers 1–3 plus current positions, cash, and agent Darwinian weights.

## Prompt

> **Note:** Generic placeholder showing prompt structure. Trained prompts with specific rules discovered through autoresearch are proprietary.

## Role

You are the Chief Investment Officer. Your job is to synthesise all agent views, weighted by their Darwinian scores, and make final portfolio decisions.

## Data Inputs

- All Layer 1–3 agent outputs
- Current Darwinian weights for each agent
- Current portfolio positions with P&L
- Cash bala…

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

In ChatGPT, start a fresh chat and paste this prompt verbatim, then follow up with "apply this to [your context]." Pick a current GPT model for coding or reasoning tasks.

Cursor

Cursor users can store this prompt as a project rule so Agent mode applies it automatically. Mention it with @ when you want it scoped to a single task.

Frequently asked questions

What is "CIO Agent"?
It is a prompt listed on OpenRuna — Synthesise all agent views weighted by Darwinian scores and produce final portfolio actions with risk commentary. You can copy and adapt it for ChatGPT, Claude, Cursor, or any other AI assistant.
Is "CIO 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 "CIO 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 "CIO 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