Value Investor
AI Trading Assistant for Smarter Investment Analysis
Project Overview
Personalized Stock Research with AI
This project is a personal-use AI chatbot designed for investment analysis and stock research based on value-investing principles. Users can add PDFs, books, and YouTube URLs to build a dynamic knowledge base, which the AI uses to analyze queries and suggest stocks.
Product Type
AI Trading Assistant & Research Chatbot
Platform
Web-Based Platform
The Challenges
Making Personal Investment Knowledge More Actionable
Investment research spans books, PDFs, and videos, and reviewing it by hand takes time. The chatbot turns that material into a knowledge base an AI can reason over.
Scattered Knowledge
Investment insights can be spread across books, PDFs, and videos.
Manual Research
Reviewing large amounts of investment material can be time-consuming.
Complex Analysis
Investment questions may require both qualitative and quantitative evaluation.
Personalized Context
Generic AI responses may not reflect an investor's own research principles.
Building a Personalized Investment Intelligence System
Turning User-Curated Investment Knowledge into Smarter Stock Research
Dynamic Knowledge Base
Build investment knowledge from PDFs, books, and YouTube URLs to support personalized investment analysis.
Knowledge Profiles
Extract and organize the provided material into reusable profiles for the trading assistant.
Agentic Research
Use a deep agent with specialized subagents to support qualitative and quantitative stock research.
Intelligent Responses
Analyze user queries and provide stock suggestions based on the available investment knowledge.
Technical Architecture
Powering AI-Driven Investment Research with Agentic Workflows
Rapid Prototype Interface
Streamlit — Used to build and deploy the prototype interface quickly.
Agent & LLM Framework
LangChain + LangGraph — Power the agent workflows, tool usage, and data-extraction processes.
Deep Agent Orchestration
DeepAgents — Enables complex research workflows with specialized subagents and file-based operations.
Tracing & Monitoring
LangSmith — Used for tracing and monitoring the LLM application.
MCP Integration
MCP Adapters — Connect MCP tools with the LangChain/LangGraph agent workflows.
Value & Outcomes
A Personalized Foundation for AI-Assisted Investment Research
Personalized Research
Answers queries using the user's curated knowledge base.
Deeper Analysis
Enables multiple agents to handle different research tasks.
Expandable Knowledge
Allows users to continuously add new investment material.
Working Prototype
Provides data extraction, file exploration, agent chat, and settings capabilities.
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