# Corporate Event Calendar Change Monitor (`quanmatrix/corporate-event-calendar-change-intelligence`) Actor

Compare recurring investor-relations calendar snapshots to detect company event additions, cancellations, reschedules and timing shifts, then return structured change evidence and monitoring actions.

- **URL**: https://apify.com/quanmatrix/corporate-event-calendar-change-intelligence.md
- **Developed by:** [Rafael Barreto Haddad](https://apify.com/quanmatrix) (community)
- **Categories:** Business, News, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $8.40 / 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.
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

## Corporate Event Calendar Change Monitor

Compare recurring investor-relations calendar snapshots to detect company event additions, cancellations, reschedules and timing shifts, then return structured change evidence and monitoring actions.

### Why use this Actor

Investors and sales teams need to detect material event-calendar changes without manually watching hundreds of investor-relations pages.

### Key features

- Buyer-native inputs aligned to **corporate event calendar monitor**.
- Predictable structured output in the default Apify Dataset.
- Pay Per Event pricing tied to measurable result rows or reports.
- Optional MCP connector delivery for downstream automation and AI agents.

### Quick start

Start with this working example and replace the target values with your own:

```json
{
  "currentItems": [
    {
      "eventId": "E1",
      "company": "Acme Corp",
      "eventType": "Earnings Call",
      "eventValue": 1,
      "eventDate": "2026-10-28"
    },
    {
      "eventId": "E2",
      "company": "Acme Corp",
      "eventType": "Investor Day",
      "eventValue": 1,
      "eventDate": "2026-11-15"
    },
    {
      "eventId": "E3",
      "company": "Beta Inc",
      "eventType": "Conference",
      "eventValue": 1,
      "eventDate": "2026-10-20"
    }
  ],
  "previousItems": [
    {
      "eventId": "E1",
      "company": "Acme Corp",
      "eventType": "Earnings Call",
      "eventValue": 1,
      "eventDate": "2026-10-25"
    },
    {
      "eventId": "E3",
      "company": "Beta Inc",
      "eventType": "Conference",
      "eventValue": 1,
      "eventDate": "2026-10-20"
    }
  ],
  "maxItems": 20000
}
```

The Actor writes structured results to the default Apify Dataset and can be used from the Store, API, schedules, Tasks, automations and MCP-compatible AI workflows.

### Input

- `currentItems` — Current records: Current source or normalized rows to analyze.
- `currentDatasetId` — Current Apify Dataset ID: Optional Apify Dataset ID used when currentItems is not supplied.
- `previousDatasetId` — Previous Apify Dataset ID: Optional previous Apify Dataset ID used instead of previousItems.
- `maxItems` — Maximum records: Maximum records loaded from a Dataset input.

All integration and advanced analysis fields are optional. The default example is intentionally runnable without configuring MCP or a previous-run baseline.

### Output

The Dataset exposes predictable machine-readable output. Representative fields include `results`.

### Pricing

This Actor uses Pay Per Event. The current factory base price is **$0.012000 per result event**. The Apify Store remains the source of truth for the price and plan/tier details shown to the buyer.

### Use cases

- Run the buyer-ready workflows exposed as Apify Tasks without preparing a custom integration first.
- Use the Actor from API or schedules for recurring collection, comparison or monitoring.
- Feed the structured Dataset output into spreadsheets, databases, automations or AI agents.
- Compare repeat runs when the product supports snapshots or previous-run inputs.

### Automation and AI

Use the same Actor through Apify API, schedules, public Tasks and the Apify MCP server. Outputs are structured for downstream workflows and AI agents rather than requiring manual copy/paste.

### Limitations

- Public websites and upstream APIs can change markup, access rules, rate limits or field availability without notice.
- Fields that are not publicly available are returned as unavailable or omitted rather than fabricated.
- Analytical outputs depend on the quality and coverage of the supplied or collected source data.
- Treat marketplace, reputation, workforce, safety or commercial signals as decision support and validate material decisions against the underlying source evidence.

### Detailed documentation

### Detailed documentation

Turn investor-relations calendar snapshots into company-level event additions, cancellations, reschedules and timing-shift intelligence.

### Why use this Actor

Investors and sales teams need to detect material event-calendar changes without manually watching hundreds of investor-relations pages. This Actor sits above raw extraction: supply a current dataset, optionally add a previous snapshot, and receive an aggregated report built for recurring monitoring and AI-agent workflows.

### Key features

- Event additions and reschedules instead of company profiles.

- Company-level snapshot comparison.

- Timing-shift and cancellation signals.

- Recurring investor-relations monitoring actions.

- Dataset-first event intelligence.

- Reads inline JSON rows or Apify Dataset IDs with limited READ permission.

- Writes one auditable report to the default Dataset and `INTELLIGENCE_REPORT`.

### Example

Use the prefilled example or replace `currentItems` with rows from an upstream Actor. On recurring runs, provide the prior period in `previousItems` or `previousDatasetId`. The Actor normalizes common aliases, compares snapshots, ranks the strongest entity changes and emits `agentAction`.

### Use cases

- Investor relations monitoring.
- Equity research.
- Event-driven investing.
- B2B sales triggers.
- AI agents.

### Pricing

One primary pay-per-event outcome: one decision-ready intelligence report. Base price USD 0.012 before Apify tier discounts. The 256 MB data-first architecture is designed for strong unit economics.

### Limitations

- Analyzes supplied public or appropriately licensed data and does not bypass restricted sources.
- Scores are decision-support signals, not predictions or guarantees.
- Keep stable identifiers across snapshots for best change detection.
- Zero-direct-competition status is rechecked before publication because the Store changes continuously.

### Workflow

`upstream dataset -> current snapshot -> optional previous snapshot -> normalization -> entity aggregation -> change scoring -> ranked signals -> agentAction`.

### Input

Provide `currentItems` as JSON records or point `currentDatasetId` to an Apify Dataset. For recurring comparisons, provide `previousItems` or `previousDatasetId`. Keep stable entity identifiers across snapshots whenever possible. `maxItems` limits Dataset reads. Optional Gen2 fields can compare the current decision metric with a prior analysis and estimate economic impact only when the user explicitly supplies an impact value.

### Output

The default Dataset receives one structured intelligence report with record counts, new and removed records, ranked entity signals, decision confidence, regression status, executive decision, recommended action, and transparent economic-impact fields when enabled. The same report is stored in `INTELLIGENCE_REPORT` for downstream automations and agent workflows.

# Changelog

This Actor's version history is a separate document: https://apify.com/quanmatrix/corporate-event-calendar-change-intelligence/changelog.md

# Actor input Schema

## `currentItems` (type: `array`):

Current source or normalized rows to analyze.

## `currentDatasetId` (type: `string`):

Optional Apify Dataset ID used when currentItems is not supplied.

## `previousItems` (type: `array`):

Optional previous snapshot rows for period-over-period comparison.

## `previousDatasetId` (type: `string`):

Optional previous Apify Dataset ID used instead of previousItems.

## `maxItems` (type: `integer`):

Maximum records loaded from a Dataset input.

## `previousAnalysis` (type: `object`):

Optional prior Gen2 output used to calculate decision-metric deltas and regression.

## `valuePerImpactUnitUsd` (type: `number`):

Optional user-supplied economic value per impact unit. Leave empty to avoid monetary estimation.

## `monthlyRuns` (type: `integer`):

Optional expected monthly run count used only with valuePerImpactUnitUsd for economic impact estimation.

## `mcpConnectors` (type: `array`):

Optional MCP connectors authorized in your Apify account. Use them to send or write this Actor result to tools such as Slack, Notion, GitHub, Sentry, Supabase, or another compatible MCP service.

## `mcpToolName` (type: `string`):

Optional exact MCP tool name. Leave blank to let the selected MCP action preset discover a compatible tool automatically.

## `mcpToolArguments` (type: `object`):

JSON object passed to the selected MCP tool. String values may use {{actor_title}}, {{result_summary}}, or {{result_json}} placeholders.

## `mcpFailOnError` (type: `boolean`):

When enabled, an MCP delivery error fails the Actor run. Disabled by default so data extraction and intelligence results remain available even if the external destination is unavailable.

## `mcpActionPreset` (type: `string`):

Choose a safe action pattern. AUTO_SAFE_WRITE discovers a compatible non-destructive write tool automatically; use a specific preset for Slack, GitHub, Notion, or database delivery.

## Actor input object example

```json
{
  "currentItems": [
    {
      "eventId": "E1",
      "company": "Acme Corp",
      "eventType": "Earnings Call",
      "eventValue": 1,
      "eventDate": "2026-10-28"
    },
    {
      "eventId": "E2",
      "company": "Acme Corp",
      "eventType": "Investor Day",
      "eventValue": 1,
      "eventDate": "2026-11-15"
    },
    {
      "eventId": "E3",
      "company": "Beta Inc",
      "eventType": "Conference",
      "eventValue": 1,
      "eventDate": "2026-10-20"
    }
  ],
  "previousItems": [
    {
      "eventId": "E1",
      "company": "Acme Corp",
      "eventType": "Earnings Call",
      "eventValue": 1,
      "eventDate": "2026-10-25"
    },
    {
      "eventId": "E3",
      "company": "Beta Inc",
      "eventType": "Conference",
      "eventValue": 1,
      "eventDate": "2026-10-20"
    }
  ],
  "maxItems": 20000,
  "monthlyRuns": 1,
  "mcpToolName": "",
  "mcpToolArguments": {},
  "mcpFailOnError": false,
  "mcpActionPreset": "AUTO_SAFE_WRITE"
}
```

# Actor output Schema

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

Decision-ready intelligence report.

# 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 = {
    "currentItems": [
        {
            "eventId": "E1",
            "company": "Acme Corp",
            "eventType": "Earnings Call",
            "eventValue": 1,
            "eventDate": "2026-10-28"
        },
        {
            "eventId": "E2",
            "company": "Acme Corp",
            "eventType": "Investor Day",
            "eventValue": 1,
            "eventDate": "2026-11-15"
        },
        {
            "eventId": "E3",
            "company": "Beta Inc",
            "eventType": "Conference",
            "eventValue": 1,
            "eventDate": "2026-10-20"
        }
    ],
    "previousItems": [
        {
            "eventId": "E1",
            "company": "Acme Corp",
            "eventType": "Earnings Call",
            "eventValue": 1,
            "eventDate": "2026-10-25"
        },
        {
            "eventId": "E3",
            "company": "Beta Inc",
            "eventType": "Conference",
            "eventValue": 1,
            "eventDate": "2026-10-20"
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("quanmatrix/corporate-event-calendar-change-intelligence").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 = {
    "currentItems": [
        {
            "eventId": "E1",
            "company": "Acme Corp",
            "eventType": "Earnings Call",
            "eventValue": 1,
            "eventDate": "2026-10-28",
        },
        {
            "eventId": "E2",
            "company": "Acme Corp",
            "eventType": "Investor Day",
            "eventValue": 1,
            "eventDate": "2026-11-15",
        },
        {
            "eventId": "E3",
            "company": "Beta Inc",
            "eventType": "Conference",
            "eventValue": 1,
            "eventDate": "2026-10-20",
        },
    ],
    "previousItems": [
        {
            "eventId": "E1",
            "company": "Acme Corp",
            "eventType": "Earnings Call",
            "eventValue": 1,
            "eventDate": "2026-10-25",
        },
        {
            "eventId": "E3",
            "company": "Beta Inc",
            "eventType": "Conference",
            "eventValue": 1,
            "eventDate": "2026-10-20",
        },
    ],
}

# Run the Actor and wait for it to finish
run = client.actor("quanmatrix/corporate-event-calendar-change-intelligence").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 '{
  "currentItems": [
    {
      "eventId": "E1",
      "company": "Acme Corp",
      "eventType": "Earnings Call",
      "eventValue": 1,
      "eventDate": "2026-10-28"
    },
    {
      "eventId": "E2",
      "company": "Acme Corp",
      "eventType": "Investor Day",
      "eventValue": 1,
      "eventDate": "2026-11-15"
    },
    {
      "eventId": "E3",
      "company": "Beta Inc",
      "eventType": "Conference",
      "eventValue": 1,
      "eventDate": "2026-10-20"
    }
  ],
  "previousItems": [
    {
      "eventId": "E1",
      "company": "Acme Corp",
      "eventType": "Earnings Call",
      "eventValue": 1,
      "eventDate": "2026-10-25"
    },
    {
      "eventId": "E3",
      "company": "Beta Inc",
      "eventType": "Conference",
      "eventValue": 1,
      "eventDate": "2026-10-20"
    }
  ]
}' |
apify call quanmatrix/corporate-event-calendar-change-intelligence --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,quanmatrix/corporate-event-calendar-change-intelligence"
        }
    }
}
```

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/nIaeFzddczwvrxu1O/builds/IBiTgHScUOkNmZeNp/openapi.json