Building an AI Investment Research Assistant: Simple Tutorial -

The world of investing is evolving rapidly, and AI-powered research assistants are transforming how investors identify opportunities and manage portfolios. Here’s a straightforward tutorial to build your own AI research assistant for investment analysis, whether you’re a beginner or a DIY enthusiast.

Step 1: Define the Research Workflow

Before coding, map out key tasks for your AI assistant:

Fetch stock data and financials
Analyze market news and sentiment
Summarize company performance
Generate actionable investment insights

For best results, break your workflow into specific agents or modules (e.g., quantitative analysis, news agent, summarizer).

Step 2: Select Your Tools & Technology

Data APIs: Yahoo Finance, Alpha Vantage, or EOD Historical Data for real-time prices and company fundamentals.
AI & LLMs: GPT-4o, Claude, or Gemini for language understanding and summarization.
Automation Platforms: Stack AI, Amazon Bedrock, Zapier, or Python scripts to orchestrate workflows.
Knowledge Bases: Integrate sources for SEC filings, earnings reports, and other financial documents.

Step 3: Build the Assistant

Option A: No-Code Platforms

Platforms like Stack AI let you design workflows visually:

Open Stack AI and create a new project.
Add nodes for stock ticker input, web/news search, and LinkedIn/company info.
Connect these nodes to an LLM (e.g., GPT-4o) for summarization.
Add tools for stock data retrieval and analytics.
Name your AI agent, configure prompts (e.g., “Summarize news and stock performance for XYZ”), and export or deploy your workflow.

Option B: Code-Based Approach (Python Example)

pythonimport openai
import yfinance as yf

def get_stock_data(ticker):
    data = yf.Ticker(ticker).info
    return data

def generate_summary(ticker, news, fundamentals):
    prompt = f"Create an investment research report for {ticker} using these fundamentals and news articles."
    response = openai.ChatCompletion.create(
        model="gpt-4o",
        messages=[{"role":"user", "content": prompt}]
    )
    return response['choices']['message']['content']

data = get_stock_data('AAPL')
news = ["Apple launches new product...", "Quarterly earnings beat expectations..."]
summary = generate_summary('AAPL', news, data)
print(summary)

Connect stock data, recent news, and financials.
Use an AI model to generate summaries and insights automatically.

Step 4: Orchestrate Multi-Agent Collaboration

For larger workflows, configure subagents:

Supervisor Agent: Divides complex queries and delegates subtasks.
Quantitative Analysis Agent: Handles data retrieval and statistical analysis.
News Agent: Collects and analyzes relevant financial news and filings.
Summarizer Agent: Synthesizes all findings into actionable investment research.

Step 5: Run and Refine

Test your assistant with different stock tickers and queries.
Adjust prompts, data sources, and agent workflows as needed.
Add features over time (e.g., portfolio optimization, risk analysis).

Best Practices and Tips

Keep tasks modular so you can add new features without breaking existing workflows.
Focus on transparency—track reasoning steps and outputs for auditability.
Update data sources and retrain models periodically to keep insights relevant.

Conclusion:
With just a few tools and a modular approach, you can build an AI investment research assistant that automates data collection, analyzes financial news, and generates insightful research—saving you time and helping uncover new investment opportunities in 2025.