Link to original video by Derek Cheung

AI-Powered Automated Stock Analysis with n8n 📈

Outline Video AI-Powered Automated Stock Analysis with n8n 📈

Short Summary:

This video demonstrates an AI-powered automated stock analysis system built using the n8n workflow automation platform. The system mimics a human research team, utilizing several AI agents (senior researcher, research analysts, senior editor) to analyze SEC 10K filings. The process involves two main n8n workflows: one managing the research team's tasks and another handling the question-and-answer interaction with the SEC 10K data stored in a vector database. The system generates comprehensive stock analysis reports in Google Docs, improving efficiency and reducing human error in investment due diligence. The video showcases a no-code approach using n8n templates, although a code-based alternative using Crew AI is also mentioned. The key technologies used are n8n, OpenAI API, a vector database, and Google Docs.

Detailed Summary:

The video is divided into several sections:

  1. Introduction and Concept Overview: The video introduces the concept of automating stock analysis using AI and n8n. The speaker emphasizes the goal of creating a system that mimics a human research team to improve efficiency and accuracy. The "AI team" consists of a senior researcher, research analysts, and a senior editor, all working within the n8n workflow.

  2. Data Source and System Architecture: The primary data source is SEC 10K filings (typically 200+ pages). The system architecture is explained using a diagram showing two main n8n templates: a "front-end" template managing the AI agents and their tasks, and a "back-end" template handling the question-and-answer interaction with the SEC 10K data stored in a vector database. The speaker mentions an alternative code-based approach using Crew AI.

  3. Workflow 1: AI Team Management (Front-end): This section details the first n8n workflow. It outlines the roles of the AI agents: the senior researcher plans the research, delegates tasks to the research analysts, and the senior editor refines the final report. The workflow is shown visually, highlighting how tasks are divided and processed. The speaker emphasizes the use of prompts to guide each agent's actions. The final output is a Google Doc report.

  4. Workflow 2: SEC 10K Q&A (Back-end): This section describes the second n8n workflow, which involves two steps: upserting the SEC 10K PDF into a vector database and a Q&A interface. The upserting process involves uploading the PDF from Google Drive to the vector database. The Q&A interface uses a webhook to receive questions from the first workflow and return answers from the vector database. The speaker demonstrates the process of uploading a PDF and obtaining the webhook URL.

  5. Workflow Integration and Demonstration: This section shows the integration of the two workflows. The speaker demonstrates how the first workflow uses the webhook URL from the second workflow to query the SEC 10K data. The entire process is shown in action, from initiating the analysis to generating the final Google Doc report. The speaker highlights how the system utilizes the OpenAI API for natural language processing and task delegation. The speaker also mentions the use of Wikipedia as an additional information source.

The video concludes with a demonstration of the complete system in action, showing how the AI agents collaborate to produce a comprehensive stock analysis report. The speaker provides links to the n8n templates used in the video. No specific quotes are highlighted, but the overall message emphasizes the efficiency and accuracy gains achieved through automation.