This n8n workflow automates the process of analyzing candlestick chart data, generating visual insights, and retrieving related market and web information, all triggered by an incoming webhook. It is designed for traders, financial analysts, or platforms that need real-time chart processing and contextual data integration.

The workflow begins when it receives an HTTP webhook request, which could carry chart data or related parameters. The data is first structured and prepared for processing. If the chart is in Base64 image format, the workflow converts it into a binary file for further analysis.

The core of the workflow involves sending this data to an AI agent powered by LangChain, equipped with an OpenRouter Chat Model, which is capable of interpreting financial data and generating insights or summaries. To enhance context, the workflow maintains conversational memory with a window buffer, allowing the AI to reference recent interactions.

In parallel, the workflow integrates with MarketStack to fetch real-time market data relevant to the analysis and uses SerpAPI to perform web searches for additional market or financial news. The fetched data and AI-generated insights are assembled into a Markdown report for easy readability.

Finally, the workflow compiles all outputs into a response that is sent back through the webhook, making it suitable for integration with trading dashboards, alert systems, or automated trading platforms. This automation streamlines complex analysis workflows, saves time, and improves decision-making processes in finance-related activities.

Node Count	11 – 20 Nodes
Nodes Used	@n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.lmChatOpenRouter, @n8n/n8n-nodes-langchain.memoryBufferWindow, @n8n/n8n-nodes-langchain.toolCalculator, @n8n/n8n-nodes-langchain.toolSerpApi, convertToFile, markdown, marketstackTool, respondToWebhook, set, stickyNote, webhook