LLMs for Quant Research: Build Text Signals & Research Agents You Can Trust
Build financial research tools that extract trading signals, verify evidence, and evaluate investment strategies using LLMs, RAG, and reliable backtesting.
Workshop description
Build practical research applications that extract financial signals from earnings calls, analyze SEC filings, and provide answers backed by verifiable sources.
Develop research agents that use financial data and Python tools, then evaluate their accuracy, reliability, and potential investment value through structured testing.
Who this workshop is for
Data scientists, ML engineers, AI developers, and Python developers looking to build financial research applications using LLMs, RAG, and machine learning.
Quantitative analysts, quant researchers, and algorithmic traders interested in extracting trading signals, testing strategies, and improving research reliability.
Financial analysts, equity research analysts, and investment researchers seeking to analyze earnings calls, company filings, and financial disclosures more efficiently.
Portfolio analysts, risk analysts, and investment professionals interested in evaluating alternative data, validating financial signals, and supporting evidence-based investment decisions.
What you will learn
Financial text signals: Extract sentiment, earnings guidance changes, and risk indicators from financial documents, benchmarking results against established financial language models.
RAG for financial research: Build a filings research application that retrieves relevant information, interprets tables, and provides source-backed answers.
Research agents: Connect LLMs to market data, financial filings, and Python tools with controlled access, spending limits, and human approval.
Reliable evaluation: Use labelled datasets, incremental-information tests, and walk-forward backtesting to assess signal quality and prevent data leakage.
What you'll be able to build and evaluate
A financial research pipeline that converts earnings calls into measurable signals and tests whether they add value beyond existing financial indicators.
A research agent that analyzes SEC filings, retrieves verified information, and supports investment research without introducing future data into historical tests.
Prerequisites
Working knowledge of Python, data analysis, and basic machine learning concepts.
Familiarity with financial datasets, LLM APIs, or backtesting is helpful. A supported LLM API or local model is needed for hands-on exercises.
Key takeaways
Build reusable research workflows that extract financial insights, validate evidence, and identify unreliable outputs.
Evaluate trading signals with controlled backtests, prevent data leakage, and maintain reproducible research results.
What you will get
Full HD workshop recording, certificate of participation, and presentation slides.
Four working notebooks, approximately 200 labelled evaluation examples, and a pre-deployment checklist covering data leakage, evidence quality, incremental value, and reproducibility.
Meet your Instructor
Bruno Gonçalves, Founder of Data For Science, author, trainer, and consultant specializing in Generative AI and Machine Learning.
PhD in Physics of Complex Systems, former faculty member at Aix-Marseille Université, and former Data Science Fellow at NYU's Center for Data Science. He has taught thousands of practitioners through O'Reilly, Pearson, and live workshops.