# AI Hedge Fund Research Agent (Buy/Hold/Sell) (`openactor/ai-hedge-fund-research-agent`) Actor

Get one clear Buy/Hold/Sell stock decision packet per run with confidence, thesis, catalysts, risks, and source links.

- **URL**: https://apify.com/openactor/ai-hedge-fund-research-agent.md
- **Developed by:** [OpenActor](https://apify.com/openactor) (community)
- **Categories:** Business, AI
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $10.00 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## AI Hedge Fund Research Agent (US Stocks)

### Brief

Get a clear **Buy / Hold / Sell** view for one US stock ticker in a single run, with confidence score, thesis, catalysts, risks, and source links.

### Who is this for

- Investors and traders who want fast, structured stock research
- Product teams building stock insight features into apps
- Automation users who need one reliable decision packet per ticker

### What it gives you

- One recommendation packet per run
- Confidence score from `0` to `100`
- A short thesis explaining the call
- Upside catalysts and downside risks
- Source links so you can verify the evidence

### Input

- `ticker` (required): US stock symbol (example: `AAPL`, `TSLA`, `JPM`)
- `horizon` (required): `scalping | day_trade | swing | long_term`
- `maxDrawdownPct` (optional): `0-100`
- `targetVolatilityPct` (optional): `0-100`
- `maxPositionPct` (optional): `0-100`

### Output

Each successful run returns exactly one item with:

- `recommendation`: `buy | hold | sell`
- `confidencePct`: integer `0-100`
- `thesis`: plain-language summary
- `catalysts`: list of potential upside drivers
- `risks`: list of key risks/invalidation points
- `sources`: list of links used by the run

### Data Sources

- Public market price data
- Public company fundamentals snapshot
- Recent market news headlines
- Reddit discussion signals

### Input example

```json
{
  "ticker": "AAPL",
  "horizon": "swing"
}
```

### Output example

```json
{
  "ticker": "AAPL",
  "horizon": "swing",
  "recommendation": "hold",
  "confidencePct": 58,
  "thesis": "AAPL shows a mixed swing setup with positive recent momentum but stretched conditions.",
  "catalysts": [
    "Recent 20-day momentum is positive.",
    "News flow is supportive."
  ],
  "risks": [
    "Short-term overbought conditions.",
    "Signal quality can vary when public endpoints are rate-limited."
  ],
  "sources": [
    {
      "name": "Price data",
      "url": "https://query1.finance.yahoo.com/v8/finance/chart/AAPL?interval=1d&range=6mo"
    },
    {
      "name": "News feed",
      "url": "https://news.google.com/rss/search?q=AAPL%20stock&hl=en-US&gl=US&ceid=US:en"
    }
  ]
}
```

### FAQ

**Q: Does this place trades for me?**\
A: No. It only provides research output.

**Q: Do I need paid market data subscriptions?**\
A: No. It is designed to run on public/free data sources.

**Q: Why are some fields sometimes empty?**\
A: Some public endpoints can throttle or block requests. The Actor still returns a valid packet using available evidence.

**Q: How many items does one run produce?**\
A: Exactly one decision packet per successful run.

**Q: Is this financial advice?**\
A: No. Use this as research support, not as a guarantee of outcomes.

### Release notes

#### v0.4

- Improved reliability when market or social data endpoints are temporarily blocked
- Added fallback fundamentals coverage so runs return richer company context more consistently
- Improved recommendation generation stability with stricter output validation and retry handling

#### v0.3

- Updated documentation for end users with simpler wording
- Added clearer examples of input and output payloads
- Improved publication-ready listing description

#### v0.2

- Added monetization-ready pricing events
- Improved result consistency and validation of output fields
- Improved explanation quality with structured thesis/catalysts/risks format

#### v0.1

- Initial release with single-ticker decision packet
- Horizon-aware recommendations (`scalping`, `day_trade`, `swing`, `long_term`)
- Source-linked evidence output for transparency

# Actor input Schema

## `ticker` (type: `string`):

US stock ticker symbol (single ticker per run). Example: AAPL

## `horizon` (type: `string`):

Choose the decision horizon / style of recommendation.

## `maxDrawdownPct` (type: `number`):

Optional risk target. If provided, the recommendation should respect this maximum drawdown target.

## `targetVolatilityPct` (type: `number`):

Optional risk target. If provided, the recommendation should aim for this volatility level.

## `maxPositionPct` (type: `number`):

Optional position sizing cap (0–100).

## Actor input object example

```json
{
  "ticker": "AAPL",
  "horizon": "swing"
}
```

# Actor output Schema

## `recommendation` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "ticker": "AAPL",
    "horizon": "swing"
};

// Run the Actor and wait for it to finish
const run = await client.actor("openactor/ai-hedge-fund-research-agent").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "ticker": "AAPL",
    "horizon": "swing",
}

# Run the Actor and wait for it to finish
run = client.actor("openactor/ai-hedge-fund-research-agent").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "ticker": "AAPL",
  "horizon": "swing"
}' |
apify call openactor/ai-hedge-fund-research-agent --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=openactor/ai-hedge-fund-research-agent",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/6gXKSqjGRRHXCoXjb/builds/98isZYahGCn7rh0l9/openapi.json