# Money Engine 2 (`rustic_soybean/money-engine-2`) Actor

- **URL**: https://apify.com/rustic_soybean/money-engine-2.md
- **Developed by:** [Chris Johnson](https://apify.com/rustic_soybean) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.00 / 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/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

## Buzz-to-Sales Lag Analyzer

Analyze dated social-mention counts alongside a business's own daily units sold. The Actor reports the dates of the largest observed mention and sales peaks, their day offset, and the strongest descriptive correlation between daily changes within a bounded lead window.

### Data and access

This Actor does not scrape social platforms or access store accounts. Supply aggregated daily mentions exported from a source you are authorized to use and your own product-level sales counts. This avoids platform credentials, third-party API charges, and collection outside the source's access rules. Do not include post text, usernames, customer data, or other personal information.

### Agent input/output

Input is a `products` array. Each product has a stable `productId` and 3–366 consecutive daily `observations`, each containing `date` (`YYYY-MM-DD`), integer `mentions`, and numeric `unitsSold`. `maxLagDays` ranges from 0 to 30. The Actor emits one dataset item per product with the peak dates, peak offset, best lag of differenced Pearson correlation, paired-day count, and an evidence status.

### Interpretation

This is a historical diagnostic, not a sales forecast or causal model. Selecting the strongest correlation across multiple lags can find chance patterns; seasonality, promotions, stockouts, pricing changes, returns, campaign timing, and incomplete mention coverage can distort results. A longer time series and repeated product launches are needed before a merchant should rely on a recurring pattern. The Actor deliberately does not say when a future sales peak will occur.

The product hypothesis is to help small ecommerce teams calibrate whether authorized buzz metrics tend to lead their own sales data and by how many days. Store demand and willingness to provide paired time series remain unvalidated; the Actor should stay staged until users test the workflow and show willingness to pay.

### Development

Run `npm test`. No external service, API key, or paid dataset is required. A Store price should only be chosen after an Apify production run establishes actual compute cost and marketplace review validates customer demand.

# Actor input Schema

## `products` (type: `array`):

Daily social mention counts and your own sales units by product. Use data exported from sources you are authorized to use.

## `maxLagDays` (type: `integer`):

Descriptive cross-correlation window. Longer windows need more history and can find coincidental patterns.

## Actor input object example

```json
{
  "products": [
    {
      "productId": "example-product",
      "observations": [
        {
          "date": "2026-10-01",
          "mentions": 8,
          "unitsSold": 3
        },
        {
          "date": "2026-10-02",
          "mentions": 12,
          "unitsSold": 4
        },
        {
          "date": "2026-10-03",
          "mentions": 20,
          "unitsSold": 5
        },
        {
          "date": "2026-10-04",
          "mentions": 34,
          "unitsSold": 7
        },
        {
          "date": "2026-10-05",
          "mentions": 25,
          "unitsSold": 11
        },
        {
          "date": "2026-10-06",
          "mentions": 15,
          "unitsSold": 18
        },
        {
          "date": "2026-10-07",
          "mentions": 10,
          "unitsSold": 14
        }
      ]
    }
  ],
  "maxLagDays": 3
}
```

# Actor output Schema

## `results` (type: `string`):

Dataset records containing observed peak timing, descriptive lag correlation, and evidence limitations.

# 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 = {};

// Run the Actor and wait for it to finish
const run = await client.actor("rustic_soybean/money-engine-2").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 = {}

# Run the Actor and wait for it to finish
run = client.actor("rustic_soybean/money-engine-2").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 '{}' |
apify call rustic_soybean/money-engine-2 --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,rustic_soybean/money-engine-2"
        }
    }
}
```

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/wfFH4qha56mTLrx3t/builds/9pQIZwJ6HgNaPQr9Q/openapi.json