# Equity Analyst — AI Investment Research Agent (`fuze/equity-analyst`) Actor

Pulls primary SEC EDGAR data (XBRL financials + filings + 10-K narrative) and writes a structured, cited investment memo — thesis, bull/bear, financial trend, risks, verdict. Monitor a portfolio for new filings. Not investment advice. Actively maintained.

- **URL**: https://apify.com/fuze/equity-analyst.md
- **Developed by:** [Fredrik Hansson](https://apify.com/fuze) (community)
- **Categories:** AI, Agents
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $154.00 / 1,000 company analyzeds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

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

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Equity Analyst — AI Investment Research Agent

Give it tickers; it pulls **primary SEC data** (EDGAR: XBRL financials + filing history + 10-K narrative) and writes a structured, **cited investment memo** — thesis, bull/bear cases, financial-trend read, risks, recent developments, verdict. Point it at a previous run and it flags new filings since then = ongoing portfolio monitoring.

Every AI finance actor stops at Google News sentiment. This one is grounded in the actual filings, with citations — a real analyst memo, not a bullish/bearish tag.

> **Not investment advice.** For research and educational purposes only.

![Equity Analyst — AI Investment Research Agent — input and sample output](https://datatools-hub.vercel.app/actor-shots/equity-analyst.png)

### 🔗 Chain it after your scraper

Already screening markets with **[Yahoo Finance Scraper (`architjn/yahoo-finance`)](https://apify.com/architjn/yahoo-finance)**, **[Yahoo Finance Scraper (`benthepythondev/yahoo-finance-scraper`)](https://apify.com/benthepythondev/yahoo-finance-scraper)** or **[SEC EDGAR Filings Scraper (`constant_quadruped/sec-edgar-filings-scraper`)](https://apify.com/constant_quadruped/sec-edgar-filings-scraper)**? Take the tickers from your screen and drop them in — each one comes back as a filings-grounded, cited investment memo instead of a raw quote. Works with any stock-screener or finance scraper's output.

### Quick start

1. **Fill in the input** — set the fields in the input tab (prefilled with a working example).
2. **Click Start** — the Actor runs and writes one record per item to the dataset.
3. **Get your data** — export to **JSON, CSV or Excel**, or pull it via the API.

### What it does

Per ticker:

- Resolves the ticker → SEC CIK
- Pulls **multi-year financials** from XBRL company facts (revenue, net income, operating income, gross profit, assets, liabilities, equity, cash, diluted EPS)
- Reads the recent **filing history** (10-K, 10-Q, 8-K, Form 4 insider, etc.)
- Extracts **10-K narrative** (business, risk factors, MD\&A) — full depth on `deep`
- Writes a **structured, cited investment memo** and (with a baseline) flags **new filings** since the last run

### Input

| Field | Type | Description |
|---|---|---|
| `tickers` | array | US-listed tickers (e.g. AAPL, MSFT). Required. |
| `depth` | string | `standard` or `deep` (full 10-K analysis). |
| `baselineDatasetId` | string | A previous run's dataset ID → flag new filings (monitoring). |

```json
{ "tickers": ["AAPL", "MSFT", "NVDA"], "depth": "standard" }
```

### Output

One memo record per ticker: financials series, recent filings, the structured memo (thesis / bull / bear / trend / risks / verdict), and source citations.

### Pricing (pay-per-event)

| Event | When | Price |
|---|---|---|
| `company-analyzed` | per company (memo from primary SEC data) | $0.50 |
| `deep-report` | per company on `deep` depth (full 10-K analysis) | $3.00 |
| `monitoring-update` | per company per re-run that finds new filings | $0.50 |

### Who it's for

Retail investors, analysts, founders and small funds priced out of AlphaSense/Bloomberg.

### Notes

- Requires an OpenAI API key (`OPENAI_API_KEY`).
- US-listed companies only (data from SEC EDGAR). Not investment advice — verify before relying on any output.

### Related actors

- [Analyst Agent — Autonomous Research & Monitoring](https://apify.com/fuze/analyst-agent) — Give it a question; get a structured, cited analyst-grade report. Plans sub-questions
- [Argus — Autonomous Intelligence Agent](https://apify.com/fuze/argus) — Describe a mission in plain English; Argus plans what to watch, gathers from public
- [AI Company Enrichment](https://apify.com/fuze/ai-company-enrichment) — Turn a list of domains into structured company profiles — firmographics, contacts

# Actor input Schema

## `tickers` (type: `array`):

US-listed stock tickers to analyze (one per line). Example: AAPL, MSFT, NVDA.

## `depth` (type: `string`):

standard = financials + filings + risk excerpt. deep = adds full 10-K business/risks/MD\&A analysis (higher charge).

## `baselineDatasetId` (type: `string`):

Optional. A previous run's dataset ID — results then flag new filings since then (portfolio monitoring).

## `maxConcurrency` (type: `integer`):

How many tickers to process in parallel (kept low to respect SEC rate limits).

## `model` (type: `string`):

OpenAI model for the memo synthesis. gpt-4o-mini is the cost-effective default.

## Actor input object example

```json
{
  "tickers": [
    "AAPL"
  ],
  "depth": "standard",
  "maxConcurrency": 3,
  "model": "gpt-4o-mini"
}
```

# Actor output Schema

## `memos` (type: `string`):

All investment memo records in the default dataset.

# 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 = {
    "tickers": [
        "AAPL"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("fuze/equity-analyst").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 = { "tickers": ["AAPL"] }

# Run the Actor and wait for it to finish
run = client.actor("fuze/equity-analyst").call(run_input=run_input)

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

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

```

## CLI example

```bash
echo '{
  "tickers": [
    "AAPL"
  ]
}' |
apify call fuze/equity-analyst --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,fuze/equity-analyst"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/WOkTCUjlls4XO5WNh/builds/PYJ5WSwd8WAYv3heJ/openapi.json