# Telegram Signal Channel Audit — Reach & Backtested Edge (`genkenobi/telegram-signal-audit`) Actor

Audit any public Telegram channel: posting cadence, views per post, reach trend, top posts — plus backtested performance (win rate, expectancy in R, sample size) for signal channels tracked by our simulation engine. One JSON report per channel.

- **URL**: https://apify.com/genkenobi/telegram-signal-audit.md
- **Developed by:** [Burhan Hayber](https://apify.com/genkenobi) (community)
- **Categories:** News, Automation
- **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $40.00 / 1,000 channel audits

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

## Telegram Signal Channel Audit — Reach & Backtested Edge

Audit any **public** Telegram channel in one call: how often it posts, how many views a post gets,
whether reach is growing or fading, which posts worked — and, for signal channels tracked by our
simulation engine, whether the calls actually made money.

No Telegram login, no user session, no phone number. Everything comes from Telegram's public web
preview (`t.me/s/<channel>`).

### What you get

| Field | Meaning |
|---|---|
| `channel`, `url`, `status` | Resolved handle and audit status (`ok`, `no_public_posts`, …) |
| `reach.posts` / `posts_per_day` | Output volume in the window |
| `reach.views_avg` / `views_median` / `views_max` | Readership per post (parsed from the public preview) |
| `reach.trend_pct` | Second half of the window vs first half, in % |
| `reach.media_share_pct` | Share of posts carrying an image or video |
| `reach.busiest_hours_utc` | Three strongest posting hours (UTC) |
| `top_posts[]` | Up to five best-performing posts with date, views and excerpt |
| `backtest` | Win rate, expectancy in **R**, confidence interval, profit factor, drawdown, sample size — only when the channel is tracked |

`R` is the result measured in units of the planned risk at the stop-loss: `+1R` means the trade earned
exactly what it risked. It makes channels with different stop distances comparable.

### Example input

```json
{
  "channel": "@smcsniperpro01",
  "days": 30,
  "include_backtest": true
}
```

### Example output (shortened)

```json
{
  "channel": "@smcsniperpro01",
  "status": "ok",
  "period_days": 30,
  "reach": {
    "posts": 84,
    "posts_per_day": 2.8,
    "views_avg": 5120,
    "views_median": 4870,
    "trend_pct": 12.4,
    "media_share_pct": 71.4
  },
  "backtest": {
    "tracked": true,
    "sample_trades": 79,
    "win_rate_pct": 69.62,
    "expectancy_r": 0.1654,
    "ci95_r": [0.013, 0.3177],
    "profit_factor": 1.81,
    "max_drawdown_r": -3.08,
    "evidence": true
  }
}
```

### Pricing

Pay per event:

- `channel-audit` — one report per channel
- `backtest-report` — only charged when backtested performance is actually delivered

Nothing is charged for channels that cannot be read (private, deleted, no public preview).

### Limits, honestly

- **Public channels only.** Private groups, invite links and channels without a web preview cannot be audited.
- The public preview exposes roughly the newest 20 posts per page; the Actor pages backwards
  (up to 12 pages), so very high-frequency channels may not reach the full window.
- View counts are the numbers Telegram shows publicly — they are not unique users.
- Backtest figures are a **simulation on historical candles**, not real fills: entries as market
  orders, take-profit legs as limit orders, stop-loss with slippage, fees and funding booked.
  Channels are measured only after enough signals. Nothing here is investment advice.
- Channel operators can request a right of reply; tracked channels are listed publicly with their
  methodology.

### Sources

Reach metrics: `https://t.me/s/<channel>` (public preview).
Backtest metrics: the operator's read-only simulation service, queried over an authenticated HTTPS API.

# Actor input Schema

## `channel` (type: `string`):

Public channel as @username, username or https://t.me/username (e.g. @savunmasanayist). Private groups and invite links cannot be audited.

## `days` (type: `integer`):

How many days of posts to analyse. The web preview shows roughly the newest 20 posts per page, so the oldest posts may fall outside very long windows for high-frequency channels.

## `include_backtest` (type: `boolean`):

Add win rate, expectancy in R and sample size when the channel is tracked by our signal simulation engine (independent candle-based replay, not self-reported figures).

## `min_views_sample` (type: `integer`):

With fewer posts than this, no first-half/second-half reach trend is reported.

## `include_posts` (type: `boolean`):

Return the top posts by views (and the newest post) with text excerpt, date and view count.

## Actor input object example

```json
{
  "days": 30,
  "include_backtest": true,
  "min_views_sample": 6,
  "include_posts": true
}
```

# Actor output Schema

## `report` (type: `string`):

Channel reach metrics, top posts and — when tracked — backtested win rate and expectancy in R.

# 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("genkenobi/telegram-signal-audit").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("genkenobi/telegram-signal-audit").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 genkenobi/telegram-signal-audit --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,genkenobi/telegram-signal-audit"
        }
    }
}
```

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/ACblXIYECpN5Hpu4s/builds/wyKmBoMoebjgrzepe/openapi.json