This article provides a complete, practical walkthrough of the Telegram Webhook Send Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   How to Build an AI-Powered Technical Stock Analysis Bot Using n8n and Telegram
   
   Meta Description:  
   Discover how to automate stock analysis with a no-code workflow using n8n, Telegram, and OpenAI. Streamline chart generation, technical insights, and user interaction for financial analysis.
   
   Keywords:  
   AI stock analysis, n8n workflow, Telegram bot for stocks, chart automation, OpenAI GPT-4, technical analysis automation, trading bot, Langchain, indicator interpretation, automated trading insights, candle charts, RSI analysis, Stochastic RSI.
   
   Third-Party APIs Used:
   
   1. Telegram Bot API  
   2. OpenAI (GPT-4 for text and image analysis, transcription)  
   3. Chart-Img API (TradingView Chart Image Generation)  
   4. Airtable API (Storage of stock tickers and scheduled analyses)
   
   Article:
   
   How to Automate AI-Driven Technical Stock Analysis via Telegram Using n8n
   
   In today’s fast-paced trading environment, individual investors and analysts are increasingly turning to AI tools to derive insights from complex stock data. Manually tracking indicators such as RSI or Stochastic RSI—let alone forecasting trends from candlestick charts—can be time-consuming and error prone. Fortunately, with a few smart integrations, this process can be transformed into a fully-automated, interactive experience.
   
   Thanks to the open-source power of n8n, this workflow bridges conversational AI, chart generation, and technical financial analysis, all through a simple Telegram chat window. Let's explore how this robust system works and how it can help democratize stock analysis using intuitive tools and APIs.
   
   Overview of the AI Stock Analysis Workflow
   
   This project, created by Mark Shcherbakov from the 5minAI community, enables users to engage with a sophisticated AI technical analyst agent entirely via Telegram. Behind the scenes, the system uses n8n to orchestrate voice message processing, chart generation, financial analysis, and response delivery.
   
   Here’s how it works in practice:
   
   Step 1: Telegram Trigger  
   A user sends either a text or voice message to a Telegram bot. This message contains a request for technical analysis, typically including a stock ticker and optionally, a preferred chart style. If the user sends a voice note, it is automatically downloaded and transcribed using OpenAI’s Whisper API.
   
   Step 2: Conversational AI Agent with Langchain  
   Once transcribed (or when a text message is received), n8n passes the message to a Langchain-based AI agent powered by OpenAI GPT-4. This agent is strategically instructed to:
   - Welcome users with professional, friendly dialogue
   - Ask clarifying questions as needed
   - Use a tool called “GetChart” to fetch technical graphs
   - Analyze chart data thoroughly (but without making buy/sell recommendations)
   All memory from past interactions is stored using Langchain’s Window Buffer Memory.
   
   Step 3: Chart Generation Using Chart-Img API  
   When a technical chart is requested, the workflow passes the ticker symbol and desired style (e.g., “candle”, “line”, “heikinAshi”) to the Chart-Img API. Here, a visual chart is generated in TradingView format with volume and technical overlays like RSI and Stochastic RSI included.
   
   Step 4: Advanced Technical Analysis with OpenAI Vision  
   The generated chart image is then processed by GPT-4’s visual analysis capabilities. A custom prompt guides the model to:
   - Identify key candlestick patterns (e.g., hammer, doji)
   - Evaluate market trends
   - Extract and interpret RSI and Stochastic RSI values
   - Look for crossovers and divergences
   
   The AI returns a detailed explanation suitable for all knowledge levels.
   
   Step 5: Response Delivery via Telegram  
   Finally, the AI’s analysis and the chart image are delivered as a user-friendly message in Telegram. No other action is required from the user. If the user asked to save a ticker for future follow-up, it is automatically stored in Airtable.
   
   Scheduled Analyses (Scenario 2)
   
   In addition to interactive analysis, the system includes a scheduled reporting feature. Using an n8n Schedule Trigger, the workflow:
   - Retrieves all saved tickers from Airtable
   - Loops through each stock
   - Automatically sends each through the analysis process
   - Returns charts and insights to a predefined recipient
   
   This is ideal for power users who want regular technical updates on their favorite stocks.
   
   Built with Flexibility in Mind
   
   This project is not only powerful, but easily customizable. For instance:
   - Swap GPT-4 with another model
   - Add new studies like Bollinger Bands
   - Adjust chart time intervals (e.g., from weekly to daily)
   - Translate messages into multiple languages
   - Sync reports to Google Sheets or Slack instead of Telegram
   
   Many of these enhancements only require a drag-and-drop setup within n8n.
   
   Conclusion
   
   This no-code AI stock analyzer built with n8n shows what’s possible when you combine conversational AI, powerful APIs, and automation logic. Whether you’re a trader, trainer, or developer, the flexibility of this system makes it ideal for integrating intelligent analytics into your day-to-day workflow.
   
   Created by a community member and available for others to explore and adapt, this project highlights how advanced stock analysis no longer requires a data science team—just smart tools and a well-designed flow.
   
   If you're ready to build your own AI trading agent, start with a Telegram bot and explore this workflow step-by-step. For a visual walkthrough, check out the YouTube tutorial linked inside the n8n project.
   
   —
   
   Need a hand setting this up? Watch the instructional video or reach out to content creator Mark Shcherbakov and the 5minAI community to learn how low-code AI is transforming finance one workflow at a time.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.