Building A Market Research Copilot using MCP and Python

2026-04-22 · Source: AI Advances - Medium · Field: Finance & Economics — FinTech & Digital Financial Services, Capital Markets & Investment Management · Depth: Intermediate, long

Summary

This tutorial outlines the initial steps for building a financial market research copilot designed to test natural-language investment theses against market data. Unlike traditional stock summarizers, this copilot parses a user's thesis, fetches historical prices and fundamental data via EODHD's MCP server, and transforms these inputs into structured evidence. Part 1 focuses on setting up the MCP client, parsing the natural-language thesis into structured fields (ticker, lookback window, thesis, mode), and building the data pipeline to fetch and process historical price data and fundamental data. Key outputs include market signals like total return, volatility, maximum drawdown, and trend slope from price data, and fundamental signals such as profitability, returns on capital, growth, valuation multiples, and analyst revisions. The system uses OpenAI for parsing and memo generation, with defined limits for lookback days, tickers, and tool calls.

Key takeaway

For AI Engineers or Data Scientists building financial analysis tools, consider shifting from simple summarization to thesis-driven validation. Your systems can provide deeper insights by parsing specific claims, fetching relevant market and fundamental data, and programmatically generating evidence layers. This approach allows for more robust, verifiable outputs than generic stock assistants, enhancing the utility for traders and analysts.

Key insights

A financial research copilot can validate natural-language investment theses using structured market and fundamental data.

Principles

• Start with a thesis, not a ticker
• Structure raw data into explicit signals
• Combine LLM flexibility with code boundaries

Method

The copilot workflow involves parsing a natural-language thesis, fetching historical prices and fundamentals via MCP, computing market and fundamental signals, mapping signals to evidence, assigning a verdict, and generating a research memo.

In practice

• Use EODHD's MCP server for financial data access
• Implement `client.py` for MCP access with retries/timeouts
• Structure `core.py` for thesis-testing logic

Topics

• Market Research Copilot
• Investment Thesis Testing
• EODHD MCP Server
• Financial Data Pipeline
• Price Signal Generation

Best for: AI Engineer, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Advances - Medium.