Agentic AI in Trading: Build a Multi-Agent Quant Research System (Stop Coding Alone)

A quant fund manager + A HFT prop desk founder + A quant teacher = a session worth watching On 9 April, we hosted Kelvin Foo, Dr Gaurav Raizada, and Vivek Krishnamoorthy for a workshop on Algorithmic Trading & Options Risk Management. Watch the recording: www.quantinsti.com/articles/algorithmic-trading-python-ai-options-risk-management-webinar/ . . In this video, we break down how Agentic AI in trading changes the entire research workflow. Instead of prompting one AI repeatedly and fixing errors, you build a multi-agent quant system where each AI agent handles a specific responsibility - just like a professional research desk. You’ll see how hedge funds are using agent-based AI systems to test thousands of trading strategies in parallel - and how you can apply the same structured workflow in your own quant research. We cover: • Hypothesis design • Data engineering • Backtesting automation • Performance evaluation • Jupyter notebook generation • Risks like hallucination, overfitting & data snooping This is not about replacing traders. It’s about accelerating research and collapsing the idea-to-alpha gap. If you want to build structured AI workflows for trading, explore the full Agentic AI for Trading program (link below). https://bit.ly/4qYYiyg Learn to apply AI and ML in trading in a practical hands-on manner EPAT syllabus on Machine learning & AI: https://bit.ly/46ZYaYe Free self-paced course for beginners: https://bit.ly/3OvsLqt Apply AI in trading strategies: https://bit.ly/4qVQVrD AI in portfolio management: https://bit.ly/3N6FkrG 🎓 About the Speaker: Mohak Pachisia is a Senior Quantitative Researcher at QuantInsti, specializing in trading strategy development, financial modeling, and quantitative research. Before joining QuantInsti, he worked with Evalueserve, where he led the learning and development function for the the Risk and Quant Solutions division. About EPAT The EPAT program by QuantInsti is a structured learning track focused on algorithmic and quantitative trading. It emphasizes Python-based strategy development, backtesting, risk management, and applied projects guided by mentors. Join EPAT - Executive Programme in Algorithmic Trading: https://bit.ly/3MT92jV 📚 Key Topics Covered: What Agentic AI really means in trading Why single-prompt AI workflows fail Multi-agent quant research systems Hypothesis Designer Agent Data Scout Agent Backtester Agent Performance Analyst Agent AI Orchestrator / Notebook Generator Testing multiple strategy variations in parallel Risks: hallucination, data snooping, overfitting Human oversight in AI trading workflows 🎯 What You’ll Learn: Why most traders “babysit AI” instead of using it properly How to structure trading workflows into modular AI agents How hedge funds use multi-agent systems for alpha research How to convert plain English ideas into testable blueprints How to automate end-to-end backtesting pipelines The three major dangers of using AI in trading Why AI is a research accelerator - not a crystal ball ⏱️ Timestamps 0:00 Introduction 0:14 The Core Problem – Untested Trading Ideas 0:28 Why Using ChatGPT Feels Like “Babysitting AI” 1:07 The Mindset Shift – What Agentic AI Really Is 1:33 How Quant Firms Use Multi-Agent Systems 2:14 Testing Thousands of Ideas in Parallel 2:39 Why Single AI Models Fail in Trading Workflows 3:27 The Relay Race Framework (Modular AI Agents) 3:40 Hypothesis Designer – Converting Ideas into Testable Blueprints 4:39 Data Scout – Fetching & Structuring Data 5:08 Backtester – Simulating Historical Trades 5:27 Performance Analyst – Measuring Returns, CAGR, Sharpe, Drawdown 5:40 AI Orchestrator – Auto-Generating Research-Ready Notebooks 6:06 Parallel Testing vs Traditional One-Idea Approach 6:26 Reality Check – The Dangers of Agentic AI 6:38 Sin #1: Hallucination 6:53 Sin #2: Data Snooping 7:09 Sin #3: Overfitting 7:19 Why Human Insight Still Matters 7:52 Building Full Agentic Workflows (Course Mention) 💡 Key Takeaways: AI works best in structured, modular workflows Multi-agent systems reduce logic mixing and hallucination Speed of idea testing matters more than idea generation Parallel strategy testing increases research efficiency Human oversight prevents overfitting and false alpha Agentic AI collapses the idea-to-alpha cycle 🔎 Keywords (Search Optimized) agentic AI in trading AI trading workflow multi agent AI system quant trading AI AI backtesting automation algorithmic trading AI hedge fund AI research AI trading strategies Jupyter notebook backtesting AI agents for trading quant research automation data snooping in trading overfitting in algorithmic trading AI hallucination trading systematic trading AI #️⃣ Hashtags #AgenticAI #QuantTrading #AlgorithmicTrading #AITrading #Backtesting #SystematicTrading #TradingStrategies #MachineLearningTrading #HedgeFunds #PythonForTrading