LangChain Automate

This workflow is ideal for:

• Traders and Investors: Those looking to analyze stock data and generate insights on stock performance.
• Financial Analysts: Professionals who require automated tools for technical analysis of financial charts.
• Developers: Individuals wanting to integrate AI capabilities into their applications using Telegram and LangChain.
• Businesses: Companies that aim to automate their stock analysis processes to enhance decision-making.
• Educators and Students: Those interested in learning about stock analysis and automation in finance.

This workflow addresses the challenge of obtaining real-time stock analysis without the need for manual intervention. It allows users to:

• Automatically receive stock chart analyses based on user requests via Telegram.
• Utilize advanced AI capabilities to interpret stock charts, enabling users to make informed trading decisions.
• Save and manage stock tickers for future reference and analysis.
• Streamline the process of gathering and analyzing stock data, thus saving time and reducing complexity.

1. Trigger: The workflow starts with a Telegram Trigger that listens for incoming messages.
2. Message Processing: The message is processed to determine if it contains text or voice input using a Switch node.
3. Text and Voice Handling:
   • If text is detected, it is stored for further analysis.
   • If a voice message is received, it is downloaded and transcribed using the Transcribe node.
4. AI Interaction: The transcribed or text message is sent to the AI Agent for analysis, which generates a request for stock analysis.
5. Chart Generation: The Get Chart node is called to generate stock charts based on user-defined parameters (ticker and chart style).
6. Technical Analysis: The generated chart is analyzed by the Technical Analysis node, providing insights into market trends, RSI, and other metrics.
7. Results Delivery: The analysis and charts are sent back to the user via Telegram using the Send Analysis and Send Chart nodes.
8. Data Management: Users can save tickers for future analysis using the Save Ticker node, which stores them in Airtable.
9. Scheduled Analysis: The workflow can also be triggered on a schedule, allowing for regular updates and analyses of saved tickers.