I Built a Profitable AI Agent Day Trader - Here's How (n8n)

About This Video

This tutorial from AI Pathways takes a practical deep dive into building an autonomous day trading agent using n8n, the open-source workflow automation platform. Rather than a surface-level overview, the video walks through the complete architecture from start to finish: connecting to real-time stock market data APIs, designing trading logic with technical indicators and decision rules, and wiring everything together in visual workflows that execute without manual intervention. The instructor covers critical topics like position sizing to manage exposure, defining risk management parameters to protect capital, and backtesting your strategy against historical data so you can validate performance before going live with real money. Along the way, viewers learn how to integrate n8n with financial data providers such as Alpha Vantage and Polygon.io for price feeds, and how to set up webhook triggers that respond to significant price movements in near real time. The video also addresses practical concerns that beginners often overlook, like paper trading first to build confidence, monitoring agent performance through dashboards and logs, and configuring alerts for unusual trading behavior or error states. The workflow includes a human-in-the-loop override so you retain final control over any trade. Whether you're curious about algorithmic trading, exploring new use cases for AI agents beyond content creation, or simply want to see how far n8n automation can stretch into financial applications, this walkthrough delivers a thorough, code-optional blueprint you can adapt to your own trading ideas and risk tolerance.

What You'll Learn

• •How to connect n8n to real-time stock market data APIs like Alpha Vantage and Polygon.io
• •Designing trading decision logic using technical indicators and condition-based workflows
• •Implementing position sizing and risk management rules within your automation
• •Backtesting your strategy against historical data before going live
• •Setting up webhook triggers for near real-time price monitoring and trade execution
• •Paper trading best practices and performance monitoring for autonomous trading agents
• •Extending the core workflow with alerts, logging, and fail-safe mechanisms